Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning (1709.10082v3)

Abstract: Developing a safe and efficient collision avoidance policy for multiple robots is challenging in the decentralized scenarios where each robot generate its paths without observing other robots' states and intents. While other distributed multi-robot collision avoidance systems exist, they often require extracting agent-level features to plan a local collision-free action, which can be computationally prohibitive and not robust. More importantly, in practice the performance of these methods are much lower than their centralized counterparts. We present a decentralized sensor-level collision avoidance policy for multi-robot systems, which directly maps raw sensor measurements to an agent's steering commands in terms of movement velocity. As a first step toward reducing the performance gap between decentralized and centralized methods, we present a multi-scenario multi-stage training framework to find an optimal policy which is trained over a large number of robots on rich, complex environments simultaneously using a policy gradient based reinforcement learning algorithm. We validate the learned sensor-level collision avoidance policy in a variety of simulated scenarios with thorough performance evaluations and show that the final learned policy is able to find time efficient, collision-free paths for a large-scale robot system. We also demonstrate that the learned policy can be well generalized to new scenarios that do not appear in the entire training period, including navigating a heterogeneous group of robots and a large-scale scenario with 100 robots. Videos are available at https://sites.google.com/view/drlmaca

Analyzing Decentralized Multi-Robot Collision Avoidance with Deep Reinforcement Learning

The paper "Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning" explores the challenging domain of decentralized multi-robot navigation where collision avoidance must be achieved without centralized control or perfect agent-state awareness. The authors propose an innovative approach that leverages deep reinforcement learning (DRL) to directly map sensor data to robot steering commands. This paper constitutes a substantial contribution to the field, aiming to narrow the performance divide between decentralized and centralized collision avoidance systems.

Core Contributions

1. Decentralized Approach: Unlike traditional models that either rely heavily on centralized control or demand detailed agent state information, this paper introduces a framework where each robot independently makes navigation decisions while only observing raw sensor data. This makes the system robust against real-world sensing uncertainties and eliminates dependency on communication with a central server.
2. Policy Design and Training: The proposed approach employs a multi-scenario, multi-stage training strategy using a policy gradient reinforcement learning framework. This involves training in complex and varied environments, improving the generalization and robustness of the developed policies.
3. Simulation and Validation: The paper rigorously validates the proposed method through simulation across various scenarios, including random environments, group collaborations, and large-scale robot swarms. Notably, the learned policies demonstrate high success rates, efficient pathfinding, and adaptability to unseen conditions.

Performance Evaluation

The results showcase the superiority of the proposed method over existing decentralized approaches like NH-ORCA in terms of efficiency and scalability. Key metrics such as success rate, extra time, and average speed underline the method's practicality in real-world applications:

• Success Rate: Achieves near-perfect scores across different robot densities and scenarios.
• Efficiency: Demonstrates impressive reductions in extra travel time, highlighting effective collision avoidance and path optimization.
• Adaptability: Successfully extends to scenarios involving heterogeneous robot types and large-scale systems with up to 100 robots.

Implications and Future Directions

The research paves the way for decentralized multi-robot systems that can be deployed in unpredictable and dynamic environments. Such capabilities are critical for applications like autonomous warehouse logistics, robotic search and rescue operations, and crowded human environments where traditional methods might falter.

Looking forward, there are several avenues for exploration:

• Real-World Implementation: Transitioning these DRL-based models from simulation to real-world robots, taking into account physical constraints and sensor noise, is essential for practical deployments.
• Integration with Global Planning: While the localized nature of the policy excels in collision avoidance, integrating these policies with global path planning strategies could alleviate limitations in navigating complex environments with dense obstacles.
• Coordination with Non-cooperative Agents: Further developing the ability to adapt to or predict the behavior of non-cooperative agents could enhance performance in mixed-agent environments, like urban settings with both robots and humans.

In conclusion, this paper offers a robust framework for decentralizing multi-robot collision avoidance through advanced reinforcement learning techniques. Though challenges remain, the potential applications and adaptability outlined suggest significant impacts on the future of robotic navigation systems.