Autonomous Robot Navigation Using Deep Reinforcement Learning for Obstacle Avoidance and Path Optimization

This paper compares the performance of a traditional approach for autonomous robot navigation and a new approach for the same problem, all within the framework of reinforcement learning. A new approach for the problem has been introduced and compared against the traditional approach, referred to as the value iteration approach for holonomic robots and the new approach for the same problem in relation to the traditional approach. These approaches include sampling-based policy estimation and interpolation and optimization-based filtering for obstacle avoidance. Actor critic methods for non-holonomic scenarios include Deep Deterministic Policy Gradient (DDPG) and Soft Actor-Critic (SAC) approaches. The simulation of various 2D and 3D scenarios, ranging from simple to complex, shows that value iteration method convergence occurs rapidly, while at the same time a success rate of over 95% can be attained within 31 minutes of virtual time. DRL techniques, however, succeed in attaining an average of 88.5% ± 4.2% in 300 scenarios, where in each case, 35% faster convergence of SAC compared to DDPG could be attained. The efficacy of reinforcement learning approaches to find practical solutions to robot navigation in various scenarios, while meeting constraints, has been proven.