Local Path Planning with Dynamic Obstacle Avoidance in Unstructured Environments

TL;DR

This paper presents a novel local path planning algorithm for unmanned ground vehicles that effectively avoids dynamic obstacles in unstructured environments by combining tangent-based planning and extrapolation methods, validated through simulations.

Contribution

Introduces a new decision-making algorithm that integrates tangent-based path planning with extrapolation for dynamic obstacle avoidance in unstructured environments.

Findings

Successfully navigates around multiple dynamic obstacles in simulations.

Gradually generates collision-free paths for UGVs in complex scenarios.

Proves effectiveness of the approach in environments with randomly moving obstacles.

Abstract

Obstacle avoidance and path planning are essential for guiding unmanned ground vehicles (UGVs) through environments that are densely populated with dynamic obstacles. This paper develops a novel approach that combines tangentbased path planning and extrapolation methods to create a new decision-making algorithm for local path planning. In the assumed scenario, a UGV has a prior knowledge of its initial and target points within the dynamic environment. A global path has already been computed, and the robot is provided with waypoints along this path. As the UGV travels between these waypoints, the algorithm aims to avoid collisions with dynamic obstacles. These obstacles follow polynomial trajectories, with their initial positions randomized in the local map and velocities randomized between O and the allowable physical velocity limit of the robot, along with some random accelerations. The developed algorithm is tested in several scenarios where many dynamic obstacles move randomly in the environment. Simulation results show the effectiveness of the proposed local path planning strategy by gradually generating a collision free path which allows the robot to navigate safely between initial and the target locations.