Lift path planning for a nonholonomic crawler crane

Abstract This paper presents an efficient lift path planning approach for a crawler crane in which the mobility of the crawler crane and its nonholonomic kinematics are considered. To obtain the optimal path rapidly, we improved the bidirectional Rapidly exploring Random Trees (RRTs) using a sampling strategy and an expansion strategy. The sampling strategy of choosing a sample from a sampling pool and unexplored space was applied to guide trees towards the collision-free and high-quality region. Furthermore, the expansion strategy of extending one tree every other k steps was introduced to increase the chance of connecting to the other tree while ensuring that the trees have a small amount of nodes. The proposed approach was tested on three lift problems. The results indicate that the approach can generate high-quality paths in complex situations in a short time, and the paths satisfy the collision constraint, lifting capacity constraint and nonholonomic kinematics constraints of the crawler crane.

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