Mobile Robot Path Planning in Dynamic Environments through Globally Guided Reinforcement Learning (2005.05420v2)

Abstract: Path planning for mobile robots in large dynamic environments is a challenging problem, as the robots are required to efficiently reach their given goals while simultaneously avoiding potential conflicts with other robots or dynamic objects. In the presence of dynamic obstacles, traditional solutions usually employ re-planning strategies, which re-call a planning algorithm to search for an alternative path whenever the robot encounters a conflict. However, such re-planning strategies often cause unnecessary detours. To address this issue, we propose a learning-based technique that exploits environmental spatio-temporal information. Different from existing learning-based methods, we introduce a globally guided reinforcement learning approach (G2RL), which incorporates a novel reward structure that generalizes to arbitrary environments. We apply G2RL to solve the multi-robot path planning problem in a fully distributed reactive manner. We evaluate our method across different map types, obstacle densities, and the number of robots. Experimental results show that G2RL generalizes well, outperforming existing distributed methods, and performing very similarly to fully centralized state-of-the-art benchmarks.

• The paper presents a novel G2RL framework that integrates A*-based global guidance with reinforcement learning to efficiently navigate dynamic environments.
• The paper employs a dual-layer approach where global planning provides an initial optimal path while local RL adapts in real time to avoid obstacles.
• The paper demonstrates scalability and robust performance across varied map configurations, offering promising applications for autonomous multi-agent systems.

Mobile Robot Path Planning in Dynamic Environments through Globally Guided Reinforcement Learning

The paper "Mobile Robot Path Planning in Dynamic Environments through Globally Guided Reinforcement Learning" presents an innovative approach to overcome the challenges faced in path planning for mobile robots within large dynamic environments. The authors propose a novel reinforcement learning (RL) framework, named Globally Guided Reinforcement Learning (G2RL), that combines global guidance with local decision-making to enable efficient navigation and obstacle avoidance in such environments.

Methodological Innovation

G2RL introduces a hierarchically structured path planning framework where a global path planning algorithm (e.g., A*) generates a preliminary optimal path—termed "global guidance"—at the outset. Concurrently, the local RL-based planner exploits spatial and temporal observations from the robot's immediate surroundings to make real-time adjustments to its movements. This dual-layer approach ensures that the robot continues to move towards its destination efficiently, avoiding unnecessary recalculations and detours often encountered in traditional reactive path-planning approaches.

A key element of the G2RL framework is the integration of a novel reward structure within the RL component. This reward function is designed to deal with sparsity issues in large environments, encouraging exploration of diverse, potentially optimal paths while maintaining commitment to reaching the target. It differs significantly from prior RL methods that impose strict adherence to a predetermined path, thereby allowing greater flexibility to the robot, reducing detours, and improving adaptability to changes and new obstacles.

Experimental Validation

The paper details a series of experiments conducted across various map configurations and obstacle densities to evaluate the performance of G2RL. The results illustrate that G2RL consistently surpasses existing distributed methods and aligns closely with fully centralized paradigms, which require complete knowledge of dynamic obstacle trajectories. This is particularly noteworthy given G2RL's entirely distributed, reactive nature. It achieves similar levels of efficacy without depending on fully cooperative communications or extensive computational resources, thereby supporting wider scalability and applicability.

Implications and Future Work

The implementation of G2RL holds significant theoretical and practical implications for advancing autonomous navigation systems. By constructing a learning-based framework that scales to large environments—a feat unattainable with traditional methodologies—it sets a precedent for future developments in multi-agent systems. The successful application to multiple robot systems indicates the potential for extensive autonomous deployments in complex, real-world scenarios where centralized solutions are impractical.

Future explorations could delve into enhancing cooperative strategies for multi-robot systems, integrating more dynamic adaptability to unforeseen events, or optimizing the reward framework further to fine-tune performance metrics such as energy efficiency or computational resource use.

Conclusion

In conclusion, the G2RL approach presented in this paper markedly enhances the capability of mobile robots to navigate dynamic environments with a high degree of efficiency and reliability. Its scalability and generalizability are particularly promising for the progression of autonomous multi-agent systems, likely spurring further innovations in the field of robotics path planning.