Deep-Learned Collision Avoidance Policy for Distributed Multi-Agent Navigation
This addresses the challenge of high-speed, low-latency obstacle avoidance for distributed multi-agent systems, offering a solution that reduces the need for parameter tuning and perfect sensing compared to existing methods.
The paper tackles the problem of enabling multiple decentralized robots to navigate reliably in cluttered and dynamic environments by developing a deep neural network-based collision avoidance policy that is robust to sensor noise. The result is a method that generates efficient navigation strategies, validated in simulated and real scenarios, with generalization to unseen conditions like static obstacles and varying agent sizes.
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.