1. Introduction

In the field of modern intelligent robotics, autonomous mobile robot systems have gradually become an important research direction and application area. These robots not only need to possess efficient motion control capabilities but also rely on precise environmental perception and optimized path planning to adapt to complex and changing environments, providing solutions for various application scenarios. Currently, with the rapid development of reinforcement learning and multi-sensor fusion technologies, the new generation of autonomous mobile robot systems based on these technologies shows good performance and broad application prospects.

In terms of motion control, autonomous mobile robots need to achieve precise positioning and navigation in dynamic and static environments. To this end, combining reinforcement learning algorithms allows robots to continuously optimize their control strategies in different motion states and learn more complex motion patterns. For example, through deep reinforcement learning (DRL) algorithms, robots can gradually adjust their motion parameters through a large number of trial-and-error processes, thereby improving stability, flexibility, and operational efficiency.

Environmental perception is another key element of autonomous mobile robot systems. Currently, multi-sensor fusion technology can fully utilize data from various sensors such as LiDAR, cameras, and sonar to provide more comprehensive and accurate environmental information. This fusion not only enhances the robot’s environmental understanding capabilities but also effectively reduces the information blind spots that a single sensor may encounter under specific conditions. Sensor data fusion strategies include but are not limited to Kalman filtering, particle filtering, and evidence-based reasoning methods in information theory. These technologies can help robots build and update their environmental maps in real-time, thereby making better decisions.

Path planning is one of the important research topics for autonomous mobile robots, aiming to find a travel route for robots in complex environments, ensuring that they can move quickly and efficiently while avoiding obstacles. Path planning methods based on reinforcement learning allow robots to autonomously explore the optimal path based on comprehensive environmental perception. Compared to traditional path planning algorithms, path planning methods that integrate reinforcement learning can achieve real-time updates in unexplored areas and adjust paths according to environmental and task characteristics. Some advanced algorithms, such as A*, RRT, and dynamic programming, combined with reinforcement learning, will provide autonomous mobile robots with more robust and intelligent decision-making capabilities.

In this design scheme, we propose a full-stack autonomous mobile robot system architecture, with specific implementation plans as follows:

Motion control module: Achieve feedback-based dynamic adjustment through a PID control algorithm optimized by deep reinforcement learning to ensure stable movement of the robot in complex environments.
Multi-sensor fusion module: Utilize LiDAR and visual sensors for data fusion to enhance the accuracy of environmental mapping and use filtering algorithms to remove noise in perception.
Path planning module: Based on environmental perception information, use reinforcement learning algorithms for path generation and optimization, while adapting to changes in dynamic environments for real-time path adjustments.
System integration and testing: Through repeated testing in simulation and real environments, with a particular focus on the robot’s performance in complex scenarios, to ensure its reliability and safety in practical applications.

The application of these integrated technologies and methods will enable autonomous mobile robot systems to better perform tasks in the complex environments of the real world, improving work efficiency and reducing human intervention. Thus, the autonomous mobile robot system based on reinforcement learning and multi-sensor fusion provides practical solutions for various industries and fields, such as logistics, service industries, and exploration.

1.1 Background Introduction

In recent years, with the rapid development of artificial intelligence technology, autonomous mobile robots (AMRs) have been widely applied in various fields such as industry, logistics, and healthcare. These robots generally possess basic functions such as environmental perception, path planning, and motion control, enabling them to perform various tasks in different working environments. However, existing systems often lack intelligence and adaptability, making it difficult to meet the real-time decision-making needs in dynamic and complex environments. Therefore, the design scheme for autonomous mobile robot systems based on reinforcement learning and multi-sensor fusion has emerged, providing new possibilities for enhancing the autonomy and flexibility of robots.

In terms of motion control, traditional control algorithms such as PID control and fuzzy control often struggle to demonstrate sufficient adaptability and robustness when faced with complex and changing environments. Therefore, using reinforcement learning (RL) as a control strategy can learn the optimal control strategy through interaction with the environment, allowing robots to self-adjust in complex environments, thereby achieving more precise motion control. This method can effectively handle the variations encountered by robots in practical tasks.

Environmental perception is a crucial part of autonomous mobile robot systems, typically relying on the collaborative work of various sensors. LiDAR, depth cameras, and IMUs (inertial measurement units) can provide rich information about the robot and its surrounding environment. However, data from a single sensor may be affected by noise or occlusion under certain conditions. By employing multi-sensor fusion technology, integrating data from different sensors helps improve the accuracy and reliability of environmental perception.

In terms of path planning, existing algorithms such as A* and Dijkstra perform well in static environments, but often struggle to quickly adjust paths in dynamic environments to respond to sudden obstacles. By introducing reinforcement learning technology, robots can select the best path based on their current state and historical experience in real-time environments and respond quickly to environmental changes. For different tasks and environments, efficient path planning models can be constructed using deep Q-learning or policy gradient methods.

In this design scheme, we will integrate motion control, environmental perception, and path planning into a full-stack development, forming an autonomous mobile robot system where each module collaborates to enhance overall performance. The specific implementation process can follow these steps:

System architecture design

Design the robot’s hardware architecture and sensor layout.
Select an appropriate computing platform to support real-time data processing and decision-making.

Data collection and preprocessing

Collect environmental information from the multi-sensor system, perform noise filtering and data fusion.
Implement data labeling and sample set creation to provide training data for reinforcement learning.

Reinforcement learning model training and tuning

Design reinforcement learning models based on deep learning methods.
Continuously optimize learning strategies through experiments in simulated and real environments.

System integration and testing

Integrate the motion control, environmental perception, and path planning modules.
Conduct system testing in experimental environments and real scenarios, collecting performance data.

The implementation of this scheme will enable autonomous mobile robots to be more intelligent and autonomous in complex environments, effectively enhancing their practicality in applications such as production and services, and promoting the development of intelligent manufacturing and automation.

1.2 Research Objectives

In the current fields of autonomous driving and automation, the design and implementation of autonomous mobile robot systems have become increasingly important. To achieve effective autonomous navigation and task execution in complex and dynamic environments, the technical solutions based on reinforcement learning and multi-sensor fusion can significantly enhance the robots’ autonomy and adaptability to environmental changes. Therefore, the objectives of this research are to construct an effective autonomous mobile robot system, considering motion control, environmental perception, and path planning, with specific goals including the following:

First, design an efficient motion control system that can maintain stable operation of the robot under various dynamic environmental conditions. By utilizing reinforcement learning algorithms, this system will have self-optimizing capabilities to adapt to different motion tasks, improving the flexibility and response speed of mobile robots.

Second, integrate various sensors, including LiDAR, cameras, and IMUs, to achieve comprehensive environmental perception. By fusing data from multiple sensors, the robot’s understanding of the surrounding environment will be enhanced, enabling it to more accurately identify obstacles and understand the complex characteristics of the environment. This environmental perception capability will provide reliable data support for subsequent path planning.

Finally, develop an intelligent path planning method that can calculate the optimal movement path based on real-time environmental information. This method will seamlessly integrate with the motion control system, ensuring that the robot can safely and quickly reach the target location while avoiding obstacles and unpredictable changes. By introducing reinforcement learning strategies, the path planning module can continuously learn and improve, adaptively optimizing the movement strategies in complex environments.

In achieving the above objectives, we will focus on the following key points:

Selection and optimization of reinforcement learning algorithms, conducting real-time training and validation in real environments.
Multi-sensor information fusion technology to address data inconsistency and timeliness issues.
Design flexible control strategies and path adjustment mechanisms for different types of mobile tasks.

Combining the above content, the autonomous mobile robot system based on reinforcement learning and multi-sensor fusion aims to achieve comprehensive solutions in key technology areas such as motion control, environmental perception, and path planning, to meet the diverse needs of practical applications and promote the continuous development of autonomous mobile robot technology.

1.3 Main Contributions

In this scheme, we propose a design for an autonomous mobile robot system based on reinforcement learning and multi-sensor fusion, with the main contributions reflected in the following aspects:

First, we designed an efficient motion control strategy that combines reinforcement learning algorithms with multi-sensor data input, enabling the robot to achieve autonomous navigation in dynamic and unknown environments. The reinforcement learning algorithm can continuously optimize the control strategy based on environmental feedback, improving the flexibility and accuracy of motion, while the fusion of multiple sensors provides richer environmental information, ensuring the robot’s stability and safety in various complex scenarios. Specifically, the motion control module achieves precise identification and dynamic avoidance of environmental obstacles through real-time processing of data from LiDAR, cameras, and IMU sensors.

Second, in terms of environmental perception, we developed an integrated perception system capable of efficiently processing data from different sensors and performing intelligent fusion. Through multi-sensor data fusion, we significantly improved the accuracy and robustness of environmental perception. By combining Kalman filtering and deep learning methods, the system can construct environmental maps in real-time while simultaneously performing target detection and tracking. This refined environmental perception capability enables the robot to effectively execute tasks in complex environments.

In the implementation of path planning, we introduced a path planning algorithm based on deep reinforcement learning. This algorithm utilizes the experience accumulated by the robot in the environment to optimize strategies, allowing for rapid calculation of efficient travel paths when facing complex terrains and dynamic obstacles. By incorporating real-time interaction with sensors and the environment, path planning not only considers static obstacles but can also dynamically adjust travel paths in response to environmental changes.

In summary, this scheme forms a reliable autonomous mobile robot system through full-stack development of motion control, environmental perception, and path planning.

Optimization of motion control strategies
Efficient data fusion mechanisms
Real-time environmental perception capabilities
Deep reinforcement learning path planning algorithms

These contributions not only enhance the flexibility and reliability of autonomous mobile robots in practical applications but also lay a solid foundation for further research and development in the future.

2. Overview of Autonomous Mobile Robot Systems

An autonomous mobile robot system is a complex system that integrates various technologies and methods to achieve automatic navigation, environmental perception, and task execution. The system typically includes three main modules: motion control, environmental perception, and path planning, with each module working collaboratively to achieve autonomous intelligent operation.

In terms of motion control, autonomous mobile robots need to possess fast and precise dynamic control capabilities to adapt to different motion scenarios and environmental changes. Robots typically use microprocessors and advanced control algorithms to achieve motion control through motor drive systems. Control strategies based on reinforcement learning can adjust the robot’s action strategies based on environmental feedback, optimizing motion paths and improving task execution efficiency. Commonly used control algorithms in practical applications include PID control, fuzzy control, and dynamic window methods.

In terms of environmental perception, autonomous mobile robots rely on data from various sensors to obtain information about the surrounding environment and perform real-time processing. Common types of sensors include LiDAR, cameras, ultrasonic sensors, and IMUs (inertial measurement units). By employing multi-sensor fusion technology, the system can effectively improve the accuracy and robustness of environmental perception. For example, algorithms such as Kalman filtering or particle filtering can be used to fuse sensor data, reducing noise impact and strengthening the establishment of environmental models.

In terms of path planning, autonomous mobile robots need to calculate the optimal path from the starting point to the target point in real-time. Common path planning algorithms include A*, Dijkstra, and RRT (Rapidly-exploring Random Tree). These algorithms can handle obstacles in dynamic environments while optimizing the path selection process through reinforcement learning to adapt to environmental changes and specific task requirements.

The design and implementation process of autonomous mobile robot systems typically follows several steps:

Determine task requirements: Clarify the application scenarios and task objectives of the robot system, such as logistics delivery, inspection monitoring, etc.
Select hardware platform: Choose suitable chassis, sensors, and computing platforms based on task requirements.
Develop motion control strategies: Implement basic motion control functions and introduce reinforcement learning algorithms for optimization.
Implement environmental perception modules: Integrate multiple sensors, design data fusion algorithms, construct environmental models, and achieve real-time updates.
Design path planning modules: Develop adaptive path planning algorithms and incorporate environmental perception results for decision-making.
System integration and testing: Integrate all modules together, conduct system testing and debugging to ensure coordination among modules and achieve design objectives.

The specific execution plan can be illustrated through the following data and flowcharts:

The design of this autonomous mobile robot system not only emphasizes the feasibility and effectiveness of technical implementation but also considers the flexibility and scalability of practical applications. The independence of each module allows the system to quickly adjust and optimize according to different application scenarios, thereby improving the adaptability and work efficiency of autonomous mobile robots in complex environments. Through a full-stack development model, it ensures that the design is both forward-looking and practical, ultimately achieving efficient autonomous operations.

2.1 Definition and Classification

An autonomous mobile robot is a machine system capable of perceiving, deciding, and executing tasks in complex environments without relying on human control, using its onboard sensors and computing capabilities. By integrating information from various sensors and executing autonomous decisions, mobile robots can achieve a series of operations such as environmental perception, dynamic path planning, and motion control. The core of these systems lies in their computing and information processing capabilities, enabling robots to navigate and execute tasks efficiently. Depending on their application fields and functions, autonomous mobile robots can be classified into the following categories:

Mobile Platforms: This is the most basic type, mainly including wheeled robots and tracked robots. They are suitable for movement in various terrains indoors and outdoors, as well as simple carrying tasks.
Service Robots: These robots mainly provide services in specific environments, such as medication delivery robots in hospitals or cleaning robots in homes. They are usually equipped with advanced sensor systems and autonomous decision-making capabilities.
Industrial Robots: These robots are typically used in manufacturing and assembly fields, such as handling robots or assembly robots on automated production lines. They require advanced path planning and obstacle avoidance capabilities to work efficiently in complex factory environments.
Exploration and Delivery Robots: This type of robot is widely used in express delivery and geographical exploration. They not only need good positioning capabilities but also must adapt to dynamically changing environments, completing tasks through complex path planning in urban or remote areas.
Agricultural Robots: Used for field operations such as sowing, fertilizing, and harvesting. They need to combine environmental perception, motion control, and decision-making to cope with complex outdoor conditions.
Rescue and Assistance Robots: Robots specifically designed for emergencies, capable of assessing situations in dangerous environments, locating trapped individuals, and conducting rescues.

For autonomous mobile robot systems, the realization of performance relies on the integration and optimization of the following aspects:

Motion Control: This part is responsible for the robot’s movement methods, including power system design, kinematic modeling, and control algorithm implementation. It ensures that the robot can move smoothly and safely while responding promptly to the appearance of obstacles.
Environmental Perception: By obtaining external environmental data through various sensors (such as LiDAR, cameras, ultrasonic sensors, etc.) and processing the data through fusion. The environmental perception module uses machine learning algorithms to recognize and understand complex scenes to support subsequent decision-making processes.
Path Planning: This module is responsible for calculating the best path from the starting point to the target. Path planning algorithms can be divided into global path planning and local path planning to cope with the impact of static and dynamic obstacles. At the same time, by combining environmental information and motion states, more flexible path adjustments can be achieved.

The entire autonomous mobile robot system enhances the robot’s autonomy and flexibility through this integration, while also improving its reliability and efficiency in practical applications. In technical implementation, reinforcement learning serves as an optimization strategy, enhancing the decision-making capabilities of autonomous mobile robots during task execution through continuous learning and adaptation in complex and variable environments.

For example, the following is a schematic diagram of motion control and path planning for an autonomous mobile robot based on reinforcement learning:

The above framework demonstrates how an autonomous mobile robot system based on reinforcement learning and multi-sensor fusion achieves self-optimization and autonomous operation through perception, decision-making, and control processes. This system design not only possesses high flexibility but also can quickly respond to changes in various application scenarios, ensuring efficient task completion.

2.2 Key Technologies

In the design of autonomous mobile robot systems, key technologies are the foundation that determines the overall performance and reliability of the system. The system mainly includes three core parts: motion control, environmental perception, and path planning. Each part requires advanced technologies and algorithms to ensure that robots can autonomously and efficiently complete tasks in complex environments.

First, in terms of motion control, precise control of the robot’s motion state is required. This typically involves using PID controllers, fuzzy control, or model-based control algorithms. Through sensor feedback (such as odometry and IMU), the control system can adjust the robot’s speed and direction in real-time to achieve the desired target. For example, in dynamic environments, adaptive control strategies can enable the robot to dynamically adjust its motion strategies based on environmental changes, thereby increasing flexibility and stability.

Second, in terms of environmental perception, multi-sensor fusion technology is particularly important. Commonly used sensors include LiDAR, cameras, ultrasonic sensors, and inertial measurement units (IMUs). Through data fusion algorithms, such as Kalman filtering and particle filtering, the system can integrate information from various sensors to generate accurate environmental maps and determine its position within the map. At the same time, machine vision technology can be used for object recognition and tracking, enhancing the robot’s understanding of the surrounding environment.

For example, the following table lists the characteristics and applications of different sensors:

Sensor Type	Characteristics	Applications
LiDAR	High precision 360° scanning	Environmental mapping, obstacle detection
Camera	Rich color information, depth estimation capability	Object recognition, visual SLAM
Ultrasonic Sensor	Low cost, limited range	Close-range obstacle detection
IMU	Real-time monitoring of motion state	Pose estimation, motion smoothing

In terms of path planning, utilizing graph search algorithms (such as A* and Dijkstra) and sampling-based algorithms (such as RRT and PRM) can effectively solve path planning problems in complex environments. Modern path planning algorithms often combine reinforcement learning techniques to form adaptive path planning systems. Through online learning, robots can optimize path selection based on historical experiences, avoiding known obstacles and adapting to dynamically changing environments.

In applications such as logistics and warehousing, combining environmental perception and path planning can achieve more intelligent path optimization. For example, robots can formulate optimal paths based on different task requirements while considering energy consumption and time costs. The reinforcement learning approach provides strong support for tuning decisions in complex scenarios, allowing the system to continuously learn in variable environments.

In summary, the implementation of the design scheme for autonomous mobile robot systems based on reinforcement learning and multi-sensor fusion requires crossing the three technical dimensions of motion control, environmental perception, and path planning. The collaboration and data sharing between these parts are key to improving overall work efficiency and flexibility. In practical applications, through continuous system integration and algorithm optimization, the widespread deployment of autonomous mobile robots in various application fields can be promoted.

2.2.1 Robot Motion Control

Robot motion control technology is a core part of autonomous mobile robot systems, directly affecting their ability and efficiency in executing tasks. This technology mainly involves decision-making and execution of the robot’s movement methods in various environments, ensuring that the robot can flexibly respond to various challenges in dynamic environments. The motion control system typically includes positioning, navigation, control algorithms, and motion planning.

First, the foundation of robot motion control is environmental perception, where the robot obtains environmental information through sensors to construct a real-time model of the current environment. Commonly used sensors include LiDAR, ultrasonic sensors, and cameras, which can provide distance, depth, and object recognition information to help the robot understand its surrounding environment. In multi-sensor fusion, the data from sensors is integrated through filtering algorithms (such as Kalman filtering or particle filtering) to improve the accuracy and robustness of environmental perception.

After obtaining accurate environmental information, the robot needs to perform path planning to ensure safe and efficient movement in complex environments. Path planning algorithms can be divided into global planning and local planning. Global planning is generally based on maps and target locations, with commonly used algorithms including A* and Dijkstra; while local planning focuses on obstacle avoidance and trajectory adjustment during the robot’s actual operation, with commonly used algorithms including dynamic window approach (DWA) and rapidly-exploring random tree (RRT). The following is a brief comparison of common path planning algorithms:

Algorithm	Advantages	Disadvantages
A*	Efficient, guarantees optimal path	High computational complexity, large memory usage
Dijkstra	Ensures finding the shortest path	Slow speed, not friendly for large-scale maps
DWA	Strong real-time performance, adapts to dynamic environments	May not find the global optimal solution
RRT	Quickly generates paths, suitable for complex environments	Path quality may be low

The implementation of motion control relies on the design of control algorithms, which aim to enable the robot to accurately execute movements based on planned paths. Classic control algorithms include PID control, fuzzy control, and reinforcement learning control. PID control is widely used due to its simple structure and good stability, but it may perform poorly in nonlinear or dynamically changing environments. Fuzzy control can adapt well to the control needs of complex systems by setting rules, while reinforcement learning control optimizes control strategies through learning processes, suitable for highly dynamic and uncertain environments.

In practical system implementation, to improve the robustness and flexibility of the system, the motion control module is generally developed in conjunction with other functional modules. For example, by collaborating with the environmental recognition module, the control module can dynamically adjust motion strategies and respond promptly to environmental changes. Additionally, the motion control system needs to undergo thorough testing and validation to ensure reliability and safety in real environments.

Overall, the design scheme for the motion control system in section 2.2.1 encompasses full-stack development from environmental perception, path planning to control execution, aiming to provide a comprehensive and flexible solution. Relying on multi-sensor fusion, precise path planning algorithms, and efficient control strategies, this motion control system can achieve effective autonomous movement in complex real-world scenarios, laying the foundation for the outstanding performance of autonomous mobile robot systems.

2.2.2 Environmental Perception

In autonomous mobile robot systems, environmental perception is one of the core technologies for achieving autonomous navigation and intelligent decision-making. Environmental perception involves not only detecting and understanding the surrounding environment but also effectively converting the collected information into the basis for the robot’s behavioral decisions. To achieve comprehensive environmental perception, robot systems typically need to utilize various sensor technologies and combine reinforcement learning algorithms for real-time analysis of environmental information.

First, the selection and layout of sensors are crucial. A combination of various sensors such as LiDAR, stereo cameras, ultrasonic sensors, and IMUs (inertial measurement units) can provide rich environmental information:

LiDAR: Provides high-resolution 2D or 3D point cloud data, capable of accurately measuring distances to surrounding objects, suitable for constructing environmental maps.
Cameras: Enhance the robot’s cognitive abilities by recognizing objects in the environment, such as obstacles, road signs, and other dynamic targets through visual information.
Ultrasonic Sensors: Suitable for close-range detection, especially effective in confined spaces, capable of real-time monitoring of surrounding disturbances.
IMU: Used to monitor the robot’s motion state, including acceleration and angular velocity, providing dynamic feedback for path planning and control.

Through multi-sensor fusion technology, robots can overcome the limitations of single sensors, generating more accurate and complete environmental models. For example, LiDAR can provide long-distance information about the environment, while cameras can perform object recognition, thereby improving the reliability and timeliness of information through data fusion. In this process, sensor data needs to be processed through filters, such as Kalman filters or particle filters, to eliminate noise and improve measurement accuracy.

Data processing after environmental perception is a key link in achieving effective navigation and control. Deep learning methods can be employed for image recognition and object detection, training neural network models to recognize different environmental features. This data-driven approach can effectively enhance the robot’s autonomous decision-making capabilities. For example, the application of convolutional neural networks (CNNs) in object detection has significantly improved the accuracy of identifying key obstacles from complex backgrounds.

Moreover, mechanisms for responding to dynamic changes in the environment are equally important. In practical applications, temporary obstacles such as pedestrians or other vehicles may appear in the surrounding environment, and robots need to possess the ability to adapt quickly. By utilizing reinforcement learning, robots can learn the best behavioral strategies through interaction with the environment. In this process, the robot’s perception system updates environmental information in real-time, feeding back to the reinforcement learning algorithm to adapt to dynamic changes.

In summary, environmental perception, as an important component of autonomous mobile robot system design, can achieve real-time acquisition and understanding of the surrounding environment through the collaborative work and fusion processing of various sensors, enhancing the robot’s ability to make autonomous decisions in complex environments, thereby realizing an efficient and flexible autonomous mobile system.

2.2.3 Path Planning

Path planning is a crucial link in autonomous mobile robot systems, directly determining the robot’s navigation capability and travel efficiency in complex environments. The goal of path planning is to generate a feasible and efficient path between the starting point and the endpoint based on the current environmental state, ensuring that the robot can safely avoid obstacles while minimizing travel time and energy consumption.

To achieve efficient path planning, the system needs to comprehensively consider motion control, environmental perception, and path optimization. First, in terms of environmental perception, the system needs to integrate multiple sensors, such as LiDAR, cameras, and ultrasonic sensors, which can provide rich environmental data and monitor changes in the surrounding environment in real-time. Sensor fusion algorithms play an important role in this process, enabling the system to accurately construct the current environmental model through filtering and estimation techniques, such as Kalman filtering and particle filtering, providing sufficiently accurate information for subsequent path planning.

In terms of motion control, the robot needs to possess certain dynamic planning capabilities to adapt to real-time changes in the environment. This includes understanding the robot’s kinematic model and handling motion constraints. Motion control methods based on reinforcement learning can optimize the robot’s behavior based on historical experiences, enabling it to respond quickly in complex environments. For example, using deep reinforcement learning algorithms, robots can continuously learn the best travel strategies through interaction with the environment.

Additionally, the path planning algorithm itself needs to be efficient and flexible. Common algorithms in path planning include A*, Dijkstra, and RRT (Rapidly-exploring Random Tree). Choosing the appropriate algorithm not only requires consideration of computational complexity and pathfinding efficiency but also needs to balance real-time performance and adaptability. For path planning needs in dynamic environments, dynamic programming algorithms are particularly suitable, as they can continuously update paths during the robot’s movement, ensuring timely adjustments in the face of newly emerging obstacles.

To enhance the efficiency of path planning, we propose combining local path planning with global path planning. In complex environments, an initial path can be generated through a global path planning algorithm, and then a local path planning algorithm can continuously optimize this path during the robot’s movement, responding to rapidly changing environments. This method effectively leverages global perspective information and real-time feedback from local environments, improving the flexibility and timeliness of path planning.

In practical implementation, the performance of different path planning algorithms can be compared (see Table 1) to select the most suitable solution for specific application needs.

Table 1: Comparison of Path Planning Algorithms

Algorithm	Advantages	Disadvantages	Real-time Performance
A* Algorithm	Combines heuristics, suitable for static environments	Insufficient adaptability for dynamic environments	Medium
Dijkstra Algorithm	Guarantees optimal path	High computational complexity, suitable for small-scale environments	Low
RRT	Strong exploration capability in complex spaces, quickly generates paths	May not guarantee overall optimal path	High
Dynamic Programming	Adapts to dynamic changes, optimizes real-time paths	High computational and storage overhead	High

After completing path planning, the robot needs to closely integrate with the motion control module to ensure smooth travel along the planned path. In path tracking control, methods such as PID controllers, fuzzy control, and sliding mode control can be applied to enhance the robot’s path tracking ability, ensuring it can move along the predetermined trajectory.

In summary, path planning in autonomous mobile robot systems encompasses all aspects of motion control, environmental perception, and real-time optimization, forming a complete closed-loop control system. Through continuous iteration, training, and optimization, this system will achieve efficient, flexible, and intelligent autonomous navigation capabilities, providing strong support for robots in daily applications and complex tasks.

3. System Architecture Design

The system architecture design aims to comprehensively integrate reinforcement learning and multi-sensor fusion technologies to ensure efficient motion control and decision-making capabilities of autonomous mobile robots in complex environments. The entire system architecture encompasses multiple core components, including environmental perception, motion control, and path planning, to achieve full-stack development and ensure the robustness and adaptability of robots in different scenarios.

First, in the environmental perception module, we will adopt multi-sensor fusion technology, including LiDAR, cameras, ultrasonic sensors, etc., to collect environmental information in real-time through a sensor network. The data from these sensors will undergo preprocessing for noise filtering and feature extraction to generate accurate environmental models. The design of the perception module also needs to consider timeliness and accuracy to ensure the effectiveness of subsequent decisions. The following is a comparison table of the performance and characteristics of the main sensors:

Sensor Type	Range	Accuracy	Response Time	Advantages and Disadvantages
LiDAR	0.1 – 100m	±2cm	< 20ms	High precision, multi-angle scanning, but high cost
Camera	0 – 50m	Depends on algorithms	< 30ms	Rich visual information, requires complex computational processing
Ultrasonic Sensor	0.02 – 4m	±1cm	< 10ms	Low cost, accurate for short-distance measurements, but affected by environmental interference

In terms of motion control, we will use model-based reinforcement learning algorithms to deeply optimize path tracking and obstacle avoidance control. By designing adaptive control strategies, the robot can dynamically adjust its position and posture in dynamic environments. Specific methods include utilizing deep Q-networks (DQN) or proximal policy optimization (PPO) algorithms to learn the best action strategies from sensor feedback data. The motion control module needs to be closely integrated with the perception module to achieve real-time data sharing and feedback adjustments.

In terms of path planning, we will use a combination of A* algorithm and dynamic programming methods, with the fused environmental model being used to generate efficient travel paths. Considering the impact of dynamic obstacles, the path planning module also needs to be updated in real-time to ensure that the robot can adapt to the constantly changing environment. Coordinating path planning with motion control can effectively reduce energy consumption during movement and enhance action efficiency.

To achieve the above functions, the system architecture will adopt a layered modular design, dividing each functional module into perception layer, decision layer, and execution layer. The perception layer is responsible for information collection and processing, the decision layer is responsible for path planning and motion strategy optimization, and the execution layer converts decisions into specific action instructions, driving the robot to move and adjust.

In terms of real-time data flow, the system will introduce ROS (Robot Operating System) as middleware, using Topics and Services to achieve efficient communication and data exchange between modules. This way, the real-time processing and feedback of sensor data can quickly reflect in the decision module, improving the system’s real-time response capability and overall performance.

Ultimately, through the full-stack design of reinforcement learning and multi-sensor data fusion, the intelligent level of autonomous mobile robots can be significantly enhanced, enabling them to better cope with complex and dynamic environments and achieve autonomous and intelligent goals. This system architecture design scheme not only possesses forward-looking and innovative aspects but also provides a practical technical path for real-world applications.

3.1 Hardware Architecture

In the hardware architecture design of autonomous mobile robot systems, emphasis is placed on full-stack development of motion control, environmental perception, and path planning to ensure the system’s efficiency and reliability. The system hardware mainly consists of the following parts: computing platform, sensor module, drive system, and power management.

First, the computing platform is the core of the robot system, responsible for running reinforcement learning algorithms and path planning strategies. A high-performance embedded computer, such as NVIDIA Jetson Xavier or Raspberry Pi 4, is recommended, as it has sufficient computing power to handle high-frequency data and complex calculations. These computing platforms can support real-time processing of deep learning models and sensor data, ensuring timely autonomous decision-making.

In terms of environmental perception, the robot requires a fusion of multiple sensors. This includes LiDAR (LiDAR), RGB cameras, depth cameras, and inertial measurement units (IMUs). LiDAR provides high-precision distance measurement and 3D environmental modeling capabilities, RGB cameras are used for object recognition and environmental understanding, depth cameras can obtain rich depth information, and IMUs help improve the robot’s stability and positioning accuracy during motion. These sensors are connected to the computing platform via interfaces such as I²C, SPI, and CAN, and data fusion is performed through corresponding software drivers.

The drive system is responsible for the robot’s motion control, typically including motors and microcontrollers. High-efficiency brushless DC motors or stepper motors are selected, combined with corresponding motor driver boards (such as Arduino or Raspberry Pi control boards) to achieve precise motion control of the robot. The motion control system needs to provide feedback on speed, position, and acceleration, ensuring stability and responsiveness through a closed-loop control system.

The power management module is the foundation for the entire system’s operation, typically consisting of lithium-ion battery packs and power management systems. The battery pack needs to meet voltage requirements of 24V or higher to support the power consumption of the computing platform and drive system. Additionally, a well-designed power management system can achieve efficient voltage conversion and load distribution through DC-DC converters, ensuring stable power supply under different operating conditions.

The design of the entire hardware architecture adopts a modular approach for easy future upgrades and maintenance. The following table summarizes the key parameters and functions of each hardware module:

Hardware Module	Model	Main Functions	Remarks
Computing Platform	NVIDIA Jetson Xavier	Deep learning and path planning	High-performance computing platform
Sensor	Velodyne VLP-16	Environmental modeling and distance measurement	LiDAR
Intel RealSense D435	Depth perception and RGB vision	Depth camera
MPU-6050	Motion state detection	Inertial measurement unit
Drive System	Maxon EC-4	Motion control	Brushless DC motor
Arduino UNO	Motor control and interface management	Control micro-module
Power Management	24V lithium battery pack	Power supply system	High energy density
TPS63070	Voltage conversion and management	DC-DC voltage regulation

Through this hardware solution, autonomous mobile robots can achieve efficient motion control, accurate environmental perception, and flexible path planning in complex environments, thereby fulfilling various autonomous navigation and task execution requirements.

3.1.1 Sensor Selection

In the design of autonomous mobile robot systems, the selection of sensors is a key link to ensure the system’s motion control, environmental perception, and path planning capabilities. During the selection process, it is necessary to comprehensively consider the performance, cost, and compatibility of sensors with other system components. The following are the specific considerations and the final selected sensor configuration for this system.

First, for environmental perception capabilities, this system needs to be equipped with various sensors that can perceive the surrounding environment to accurately identify obstacles, ground conditions, and detailed features. Considering the advantages and disadvantages of different types of sensors and system requirements, the following sensors were ultimately selected:

LiDAR: Used for high-precision measurement of the environment. This sensor can provide 360-degree environmental scanning data, with high resolution and measurement accuracy, capable of reliable operation in complex environments.
Depth Camera: Combined with the use of LiDAR, the depth camera can provide detailed three-dimensional images at close range, helping to obtain more environmental information in specific scenarios (such as indoor navigation).
Infrared Sensors: Used for close-range obstacle avoidance and environmental monitoring. Infrared sensors have advantages such as rapid response and low cost, making them an important supplement for the robot’s near-end environmental perception.
IMU (Inertial Measurement Unit): Used to obtain the robot’s motion state, including acceleration, angular velocity, etc., helping to improve the accuracy of intelligent navigation and motion control.
GPS Module: Although its use is limited in indoor environments, in outdoor mobility, the GPS module can provide support for positioning, improving positioning accuracy when combined with other sensors.

Based on the characteristics of the above sensors and system requirements, Table 1 summarizes the technical parameters and functions of the selected sensors:

Sensor Type	Main Functions	Accuracy	Cost Range
LiDAR	Environmental scanning	±2cm	5000-20000 RMB
Depth Camera	Close-range three-dimensional vision	±1cm	500-3000 RMB
Infrared Sensors	Close-range obstacle detection	±5cm	100-500 RMB
IMU	Motion state monitoring	±0.1g, ±0.1°	300-1000 RMB
GPS Module	Positioning services	±5m	200-1000 RMB

When integrating these sensors, it is necessary to ensure real-time data fusion. In terms of sensor data processing, state estimation-based filtering algorithms, such as Kalman filtering (Kalman Filter) or extended Kalman filtering (Extended Kalman Filter), can be used to achieve the fusion of multi-source sensor data, thereby improving the accuracy and reliability of environmental perception. The data fusion process will further enhance the effectiveness of path planning and motion control, laying a solid foundation for the autonomous mobile robot system.

The system also needs to select appropriate interfaces for sensors to ensure efficient and stable data transmission. For example, LiDAR typically outputs data via Ethernet interfaces, while depth cameras can use USB or HDMI interfaces. IMUs and infrared sensors are often connected via I2C or SPI interfaces. The choice of these interfaces will directly affect the overall response speed and data processing capabilities of the system.

Through scientifically reasonable sensor selection and efficient data fusion technology, this system will possess strong autonomous navigation and environmental perception capabilities, laying a solid foundation for subsequent path planning and motion control.

3.1.2 Computing Platform

The computing platform is the core of the autonomous mobile robot system, with main functions including processing sensor data, executing decision algorithms, and generating motion control instructions. In this scheme, a high-performance computing platform is selected to support reinforcement learning algorithms and multi-sensor data fusion technology while ensuring real-time requirements.

First, the computing platform should have strong processing capabilities to handle complex algorithms and real-time data input. It is recommended to use NVIDIA GPUs that support CUDA for accelerating deep learning and reinforcement learning models. Additionally, the system should be equipped with multi-core CPUs, such as Intel i7 or AMD Ryzen series, to support necessary computational tasks and multi-threaded processing, efficiently conducting environmental perception and path planning.

Second, the computing platform should provide sufficient memory and storage space. At least 32GB of RAM is required to ensure system stability during complex calculations. It is also recommended to equip an SSD (solid-state drive) as the main storage medium to improve data reading speed, meeting the demands of high-speed data processing. The storage space should not be less than 1TB to collect and store large-scale sensor data and model files.

In terms of sensor interfaces, the computing platform needs to support various interface standards, including USB, Ethernet, and serial communication, to facilitate real-time data interaction with sensors such as LiDAR, RGB-D cameras, and IMUs (inertial measurement units).

Next, to achieve motion control, the computing platform should have corresponding control interfaces, combined with motor drive modules, supporting PWM (pulse-width modulation) signal transmission to achieve precise control of motion instructions. Depending on the complexity of the robot’s movement, it is advisable to use a real-time operating system (RTOS) or utilize real-time extensions under Linux to ensure timely response of control instructions at critical moments.

Finally, to ensure the system’s scalability, it is recommended to adopt a modular hardware architecture design. The computing platform should have expandable PCIe slots to allow for the addition of more processing units or acceleration cards based on future needs. Additionally, the platform should be equipped with rich communication modules, such as Wi-Fi, Bluetooth, and 4G/5G, to facilitate data uploading, remote control, and status monitoring.

In this context, we can integrate the following hardware configuration as the computing platform solution for the system:

Hardware Component	Recommended Configuration
Processor	Intel i7 or AMD Ryzen
GPU	NVIDIA RTX 3060 or higher
Memory	32GB DDR4
Storage	1TB SSD
Operating System	Linux (with RTOS extension)
Sensor Interfaces	USB, Ethernet, Serial
Control Interfaces	PWM Output
Communication Modules	Wi-Fi, Bluetooth, 4G/5G

This computing platform design scheme meets the full-stack development needs of autonomous mobile robots in motion control, environmental perception, and path planning, while also possessing good performance and scalability.

3.1.3 Power System

The power system is a core component of the autonomous mobile robot system, and its design needs to comprehensively consider thrust sources, energy management, driving methods, and the ability to cope with different working environments. To achieve efficient motion control, environmental perception, and path planning, the selection and configuration of the power system are crucial.

First, the power system typically consists of motors, power transmission devices, and power sources. In this system, brushless DC motors (BLDC) are selected as the main power source. Brushless motors have advantages such as high efficiency, low noise, and long service life, making them suitable for mobile robots that require high precision and efficiency. Specific selection recommendations are as follows:

Voltage Range: 24V
Power Range: 250W-500W
Rated Speed: 3000-6000 RPM
Control Method: Use a combination of speed closed-loop control and position control to ensure stability and responsiveness at different speeds.

Next is the power transmission device, which mainly includes reducers and wheel drives. Choosing the appropriate reducer helps increase the robot’s output torque, reduce output speed, and lessen the burden on the motor. Depending on different application scenarios, planetary gear reducers are recommended. Table 1 lists the matching schemes for motors and reducers:

Motor (BLDC)	Reducer Type	Reduction Ratio	Output Torque (Nm)	Max Speed (m/s)
250W	Planetary Gear Reducer	10:1	25	1.5
500W	Planetary Gear Reducer	20:1	30	2.0

In terms of energy management, it is recommended to use lithium battery packs as the power source due to their high energy density and long lifespan. The battery capacity needs to be evaluated based on the overall power consumption of the system, estimated to be in the range of 4-8Ah. Through reasonable battery pack configuration, the overall weight of the robot can be reduced while ensuring endurance.

To achieve efficient motion control, the robot will integrate motor drive controllers, using PWM modulation technology to achieve precise speed and position control. At the same time, inertial measurement units (IMUs) and encoders will be used as feedback mechanisms to ensure real-time monitoring and adjustment of the robot’s motion state.

To adapt to complex working environments, the power architecture of this system also needs to possess certain environmental adaptability. For example, introducing different types of tires (such as all-terrain tires) can improve passability on uneven surfaces. Additionally, by changing the wheelbase and axle design, the robot’s center of gravity and stability can be optimized to cope with more complex operating conditions.

Finally, the design of the power system should also consider the power consumption and motion efficiency under actual operating conditions, combining the motion model with control strategies to enhance overall autonomous navigation capabilities. Through the combination and optimization of models, the designed power system can not only support basic motion needs but also provide a foundation for more advanced functional expansions.

Based on the above analysis, the designed power system scheme is practical and can effectively ensure the stability, flexibility, and long-term operational capabilities of autonomous mobile robots, meeting their application needs in complex environments.

3.2 Software Architecture

In the design of autonomous mobile robot systems, the software architecture plays a crucial role. To achieve full-stack development of motion control, environmental perception, and path planning, the software architecture of this system adopts a layered design principle for modular development and later expansion. The system is mainly divided into three main parts: perception layer, decision layer, and execution layer.

The perception layer is responsible for acquiring data from multiple sensors, conducting environmental modeling and state estimation. This layer includes sensor driver modules, data fusion modules, and state estimation modules. The sensor driver module is responsible for data collection from various sensors (such as LiDAR, cameras, inertial measurement units, etc.). The data fusion module relies on Kalman filtering or extended Kalman filtering to combine data from different sensors, outputting more accurate environmental information. The state estimation module uses sensor data and motion models to estimate the robot’s position and posture.

In the decision layer, the system implements reinforcement learning algorithms to optimize path planning and decision-making. This layer includes policy learning modules, path planning modules, and task allocation modules. The policy learning module uses reinforcement learning algorithms (such as deep Q-networks or Proximal Policy Optimization) to train the robot on how to select the optimal path in complex environments. The path planning module combines current environmental data, utilizing A* algorithm, RRT (Rapidly-exploring Random Tree), or other path planning algorithms to generate real-time feasible paths based on the robot’s state. The task allocation module is responsible for distributing tasks in multi-robot systems, ensuring effective resource utilization and collaboration.

The execution layer is responsible for specific motion control, including motion control modules and communication modules. The motion control module implements precise control of the robot’s movement based on commands from the decision layer, utilizing PID controllers or fuzzy controllers. The communication module is responsible for data transmission and interaction between different modules and different robots, ensuring the coordinated operation of the system.

The high-level view of the system architecture is as follows:

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Design Scheme for Autonomous Mobile Robot System Based on Reinforcement Learning and Multi-Sensor Fusion

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