Deep-Learned Collision Avoidance Policy for Distributed Multi-Agent Navigation (1609.06838v2)

Abstract: High-speed, low-latency obstacle avoidance that is insensitive to sensor noise is essential for enabling multiple decentralized robots to function reliably in cluttered and dynamic environments. While other distributed multi-agent collision avoidance systems exist, these systems require online geometric optimization where tedious parameter tuning and perfect sensing are necessary. We present a novel end-to-end framework to generate reactive collision avoidance policy for efficient distributed multi-agent navigation. Our method formulates an agent's navigation strategy as a deep neural network mapping from the observed noisy sensor measurements to the agent's steering commands in terms of movement velocity. We train the network on a large number of frames of collision avoidance data collected by repeatedly running a multi-agent simulator with different parameter settings. We validate the learned deep neural network policy in a set of simulated and real scenarios with noisy measurements and demonstrate that our method is able to generate a robust navigation strategy that is insensitive to imperfect sensing and works reliably in all situations. We also show that our method can be well generalized to scenarios that do not appear in our training data, including scenes with static obstacles and agents with different sizes. Videos are available at https://sites.google.com/view/deepmaca.

Deep-Learned Collision Avoidance Policy for Distributed Multi-Agent Navigation

This paper investigates decentralized multi-agent navigation in environments fraught with obstacles and noise. The authors present an end-to-end framework utilizing deep learning to realize a collision avoidance policy, addressing challenges inherent in geometric optimization and sensing inaccuracy in traditional methods. The proposed framework circumvents online geometric optimization and tedious parameter tuning, leveraging a deep neural network (DNN) trained to directly map sensor measurements to steering commands for agents, thereby making real-time acting decisions responsive to dynamic environments and imperfect sensing.

The main contribution lies in formulating multi-agent navigation as a learning problem where the agent's navigation strategy translates sensor inputs to meaningful outputs—specifically, steering commands defined by movement velocity. The trained DNN accommodates noisy sensor inputs and demonstrates capability in environments unseen in training data, including static obstacles and varying agent sizes. This adaptability is achieved by training on simulated environments with varied configurations to ensure diverse scenarios, aligning with robustness goals.

Key Results:

• Simulation Success: The trained DNN displayed significant efficacy across multiple simulated scenarios when compared to ORCA, particularly with scenarios involving intersecting agent paths or differences in agent sizes. The solution improved navigation duration and trajectory lengths in some cases, though with varying "aggressiveness" in approach compared to ORCA.
• Generalization Capability: The policy maintains its efficacy in scenarios not envisioned during training, such as those containing static obstacles, displaying adaptive prioritization in safety margins under differing environmental constraints.

Implications and Speculations:

The implications for practical deployment in autonomous robotics are profound, enabling more reliable navigation systems across varying operational contexts—such as warehouse automation or swarm robotics—where static and dynamic obstacles coexist. The algorithm's resilience against sensing errors and absence of necessities for intense parameter tuning enhances its deployability in less controlled environments.

Looking forward, potential expansions include enhanced architectures like deeper networks or combining with reinforcement learning for complex dynamic scenarios like closely contested spaces or quadrotor navigation. Future investigations could explore real-time adaptation and refinement in truly decentralized setups without reliance on centralized computing facilitation. The approach could scale to broader applications in collaborative robotics, vehicular traffic management, and dynamically adaptive smart environments.

In summary, this paper delineates a method of converting perception directly into action, thus advancing the field of robotics navigation by deploying deep learning paradigms as viable alternatives to stochastic optimization techniques, propelling solutions forward into inherently challenging operational territories.