Efficient Modeling and Evaluation of Constraints in Path Planning for Multi-Legged Walking Robots

In this article, we propose a new method for constraints evaluation during the path planning of a multi-legged walking robot. We propose the application of Gaussian Mixtures (GM) to build constraint models. With the proposed analytical constraint function we can compute the gradient and move the robot out of the bound configuration. The proposed method allows checking self-collisions, and a workspace of the robot in a few microseconds and efficiently plan the motion of the robot. The proposed path planning method uses RRT-Connect framework. We show how to efficiently apply the constraints evaluation methods to optimize the posture of the robot, optimize the position of the feet during the swing phase and efficiently explore the search space. We compare the proposed method with the standard constraints evaluation algorithms. Finally, we present the results of path planning for the six-legged robot in various scenarios to show properties of the proposed motion planning algorithm with GM-based constraints evaluation.

Abstract licence: CC BY-NC-ND

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