This algorithm demonstrates optimal path generation for mobile robots in obstacle environments, suggesting improved real-time performance through unique strategies.
This study designed planar path planning algorithm for mobile robots based on Cellular Automata (CA). The construction of the algorithm was expounded including the cellular space, cellular states, neighborhood and evolutionary rule. The distance from each free cell to the target point was generated through the evolution of CA. Then, the negative gradient tracking mechanism was introduced to generate the global optimal path based on the cellular state values. To enhance real-time performance, the time complexity of the algorithm was reduced by stack-based cellular states updating strategy. Verified by experiments on the Matlab simulation and physical robots, this algorithm demonstrates a stable ability to generate the shortest path in complex obstacle environments.
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Li et al. (2025) studied this question.
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