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🔥 Content Introduction
1. Introduction
Path planning is one of the core technologies for intelligent systems such as mobile robots and drones, aiming to find a feasible path in complex environments that meets the criteria of “collision-free, short path, and high smoothness.” Grid maps have become the most commonly used environmental modeling method in path planning due to their intuitive modeling and clear expression of environmental information. The Whale Optimization Algorithm (WOA), a novel intelligent optimization algorithm proposed in 2016, has advantages such as fast convergence speed, fewer parameters, and strong global search capability. However, the original WOA is designed for continuous space optimization, and directly applying it to discrete grid path planning presents issues such as “coding mismatch and difficulty in satisfying obstacle avoidance constraints.” This paper discusses the improvement strategies for adapting WOA to path planning from the perspective of grid map modeling principles, and verifies its effectiveness in obstacle avoidance and path length optimization through simulation experiments, providing clear technical references for related researchers and students.
2. Principles of Grid Map Modeling
The essence of a grid map is to discretize the robot’s working environment into a “grid cell array,” using binary or multi-value coding to describe the state of each cell, achieving a digital representation of environmental information. The modeling process can be divided into three core steps:
(1) Environmental Information Collection and Preprocessing
First, raw environmental data is obtained through sensors (such as LiDAR and visual cameras), including information on obstacle locations, sizes, and environmental boundaries. Then, through denoising (such as filtering to remove measurement errors) and coordinate calibration (unifying the environmental coordinate system with the robot’s coordinate system), structured environmental data is obtained, laying the foundation for grid division.
(2) Grid Cell Division and State Coding
The grid size is determined based on application scenario requirements: if the robot is small and high environmental accuracy is required (such as indoor micro-robots), the grid side length can be set to 5-10 cm; if it is an outdoor drone, the side length can be set to 1-5 m (balancing computational efficiency and environmental detail). After division, each grid is assigned a state value:
- Free Grid: No obstacles, the robot can pass, coded as “0”;
- Obstacle Grid: Contains obstacles (such as walls, furniture, trees), coded as “1”;
- Special Grid: The starting grid is coded as “S”, and the ending grid is coded as “E”, facilitating algorithm recognition of the target.
For example, in a 10×10 indoor grid map (Figure 1), (1,1) is the starting point S, (10,10) is the ending point E, (3,2)-(3,4) and (6,5)-(8,5) are obstacle grids (coded as 1), while the rest are free grids (coded as 0), visually presenting the distribution of environmental obstacles.
(3) Advantages and Adaptability of Grid Maps
The core advantage of grid maps lies in “discretization + digitalization”: discretization transforms complex environments into an iterable set of units, suitable for iterative optimization by intelligent algorithms; digitalization facilitates computer storage and computation, eliminating the need to handle complex geometric relationships in continuous environments. Additionally, grid maps support dynamic updates (for example, when obstacles move, only the corresponding grid coding needs to be modified), providing convenience for dynamic path planning.
3. Fundamentals of the Whale Optimization Algorithm and Path Planning Adaptation Improvements
(1) Core Principles of the Original WOA
⛳️ Results
📣 Sample Code
🔗 References
[1] Wang Buwei, Pan Pengcheng. Path Planning for Mobile Robots with Multiple Target Points Based on Improved Whale Optimization Algorithm [J]. Robotics Technology and Applications, 2023(6):14-19. DOI:10.3969/j.issn.1004-6437.2023.06.004.
🎈 Some theoretical references are from online literature; if there is any infringement, please contact the author for removal.
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