D-Point Trigonometric Path Planning based on Q-Learning in Uncertain Environments

TL;DR

This paper introduces a novel D-point trigonometric path planning method based on Q-learning for dynamic, uncertain environments with moving obstacles and targets, demonstrating high efficiency and robustness.

Contribution

It proposes a new Q-learning based path planning approach with optimized state, action, and reward functions for dynamic environments.

Findings

High convergence speed in experiments

High hit rate in path planning tasks

Low dependency on environmental parameters

Abstract

Finding the optimum path for a robot for moving from start to the goal position through obstacles is still a challenging issue. This paper presents a novel path planning method, named D-point trigonometric, based on Q-learning algorithm for dynamic and uncertain environments, in which all the obstacles and the target are moving. We define a new state, action and reward functions for the Q-learning by which the agent can find the best action in every state to reach the goal in the most appropriate path. The D-point approach minimizes the possible number of states. Moreover, the experiments in Unity3D confirmed the high convergence speed, the high hit rate, as well as the low dependency on environmental parameters of the proposed method compared with an opponent approach.