AI based Algorithms of Path Planning, Navigation and Control for Mobile Ground Robots and UAVs

TL;DR

This paper explores machine learning-based path planning and navigation algorithms for mobile ground robots and UAVs, emphasizing collision avoidance and optimal pathfinding in static and dynamic environments through hybrid reactive and reinforcement learning techniques.

Contribution

It introduces a hybrid reactive and Q-learning approach for collision-free navigation in unknown static environments, extended to 3D and dynamic scenarios, demonstrating improved performance through simulations.

Findings

Effective collision avoidance in static environments

Successful extension to 3D environments

Robust path planning in dynamic, cluttered environments

Abstract

As the demands of autonomous mobile robots are increasing in recent years, the requirement of the path planning/navigation algorithm should not be content with the ability to reach the target without any collisions, but also should try to achieve possible optimal or suboptimal path from the initial position to the target according to the robot's constrains in practice. This report investigates path planning and control strategies for mobile robots with machine learning techniques, including ground mobile robots and flying UAVs. In this report, the hybrid reactive collision-free navigation problem under an unknown static environment is investigated firstly. By combining both the reactive navigation and Q-learning method, we intend to keep the good characteristics of reactive navigation algorithm and Q-learning and overcome the shortcomings of only relying on one of them. The proposed method is then extended into 3D environments. The performance of the mentioned strategies are verified by extensive computer simulations, and good results are obtained. Furthermore, the more challenging dynamic environment situation is taken into our consideration. We tackled this problem by developing a new path planning method that utilizes the integrated environment representation and reinforcement learning. Our novel approach enables to find the optimal path to the target efficiently and avoid collisions in a cluttered environment with steady and moving obstacles. The performance of these methods is compared with other different aspects.