Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/191944
Title: Deep-Learned Collision Avoidance Policy for Distributed Multiagent Navigation
Authors: Pinxin Long;Wenxi Liu;Jia Pan
Year: 2017
Publisher: IEEE
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 multiagent 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 multiagent 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 multiagent 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.
URI: http://localhost/handle/Hannan/191944
volume: 2
issue: 2
More Information: 656,
663
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7812634.pdf1.19 MBAdobe PDF
Title: Deep-Learned Collision Avoidance Policy for Distributed Multiagent Navigation
Authors: Pinxin Long;Wenxi Liu;Jia Pan
Year: 2017
Publisher: IEEE
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 multiagent 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 multiagent 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 multiagent 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.
URI: http://localhost/handle/Hannan/191944
volume: 2
issue: 2
More Information: 656,
663
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7812634.pdf1.19 MBAdobe PDF
Title: Deep-Learned Collision Avoidance Policy for Distributed Multiagent Navigation
Authors: Pinxin Long;Wenxi Liu;Jia Pan
Year: 2017
Publisher: IEEE
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 multiagent 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 multiagent 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 multiagent 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.
URI: http://localhost/handle/Hannan/191944
volume: 2
issue: 2
More Information: 656,
663
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7812634.pdf1.19 MBAdobe PDF