Improved A* algorithm and model predictive control- based path planning and tracking framework for hexapod robots

Abstract

Purpose Path planning is a fundamental and significant issue in robotics research, especially for the legged robots, since it is the core technology for robots to complete complex tasks such as autonomous navigation and exploration. The purpose of this paper is to propose a path planning and tracking framework for the autonomous navigation of hexapod robots. Design/methodology/approach First, a hexapod robot called Hexapod-Mini is briefly introduced. Then a path planning algorithm based on improved A* is proposed, which introduces the artificial potential field (APF) factor into the evaluation function to generate a safe and collision-free initial path. Then we apply a turning point optimization based on the greedy algorithm, which optimizes the number of turns of the path. And a fast-turning trajectory for hexapod robot is proposed, which is applied to path smoothing. Besides, a model predictive control-based motion tracking controller is used for path tracking. Findings The simulation and experiment results show that the framework can generate a safe, fast, collision-free and smooth path, and the author’s Hexapod robot can effectively track the path that demonstrates the performance of the framework. Originality/value The work presented a framework for autonomous path planning and tracking of hexapod robots. This new approach overcomes the disadvantages of the traditional path planning approach, such as lack of security, insufficient smoothness and an excessive number of turns. And the proposed method has been successfully applied to an actual hexapod robot.

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