Path planning for a tracked robot traversing uneven terrains based on tip‐over stability

Abstract

In the real‐world environment, the path planning method of tracked robot is widely studied when driving on uneven terrain. How to solve the problem that the traditional path planning algorithm cannot adapt to uneven terrain because of the constraints of obstacle avoidance and path length is a challenge for tracked robots. In this paper, a stability‐based path planning framework for tracked robot is proposed to reduce the risk of rollover when the tracked robot passes through uneven terrain. First, a virtual plane method is proposed to estimate the attitude of tracked robot. Second, on this basis, a dynamic high‐stability path evaluation algorithm for tracked robot based on force angle stability margin (FASM) is proposed, which transforms the stability‐based path planning problem into a hypergraph problem. Moreover, considering that the optimization problem is strongly nonlinear and nonconvex, a hybrid algorithm of covariance matrix adaptive evolution strategy (CMAES) and Levenberg–Marquardt (LM) is designed under the framework of generalized graph optimization (G2O) to improve the solution efficiency. Finally, simulation and experiments show that the stability‐based path planning framework can effectively plan the high‐quality path, and the maximum stability of the tracked robot is 0.9156 when the robot crosses uneven terrain using optimal path 2.

Stewart-inspired parallel spatial docking robot: design, analysis and experimental results

“…In recent years, with the development of unmanned docking technology (Chen et al , 2020; Chen and Qiao, 2020; Qiao et al , 2022; Yang et al , 2018; Li et al , 2017; Xue et al , 2022; Li et al , 2015), people have put forward higher requirements for the accuracy and stability of unmanned docking, especially for autonomous docking of unmanned vehicles and other fields (Li et al , 2021; Chen et al , 2021; Zhao et al , 2023; Wang et al , 2021). How to quickly and accurately achieve unmanned autonomous docking of ground vehicles has become a research hot spot (Qiao et al , 2014; Chen and Qiao, 2020; Li et al , 2018; Su et al , 2021; Qiao et al , 2001; Yang et al , 2014).…”