3D Path Planning and Obstacle Avoidance Algorithms for Obstacle-Overcoming Robots

TL;DR

This paper presents a novel 3D path planning and obstacle avoidance framework for obstacle-overcoming robots, integrating a new A-star based global planner with dynamic local obstacle avoidance, validated through complex environment simulations.

Contribution

It introduces a combined global and local 3D path planning algorithm with a novel A-star variant and dynamic obstacle avoidance for complex scenes.

Findings

Algorithms generate quick, reasonable 3D paths.

Robust local obstacle avoidance in complex environments.

Effective path correction after obstacle avoidance.

Abstract

This article introduces a multimodal motion planning (MMP) algorithm that combines three-dimensional (3-D) path planning and a DWA obstacle avoidance algorithm. The algorithms aim to plan the path and motion of obstacle-overcoming robots in complex unstructured scenes. A novel A-star algorithm is proposed to combine the characteristics of unstructured scenes and a strategy to switch it into a greedy best-first strategy algorithm. Meanwhile, the algorithm of path planning is integrated with the DWA algorithm so that the robot can perform local dynamic obstacle avoidance during the movement along the global planned path. Furthermore, when the proposed global path planning algorithm combines with the local obstacle avoidance algorithm, the robot can correct the path after obstacle avoidance and obstacle overcoming. The simulation experiments in a factory with several complex environments verified the feasibility and robustness of the algorithms. The algorithms can quickly generate a reasonable 3-D path for obstacle-overcoming robots and perform reliable local obstacle avoidance under the premise of considering the characteristics of the scene and motion obstacles.