CrowdMove: Autonomous Mapless Navigation in Crowded Scenarios

Abstract

Navigation is an essential capability for mobile robots. In this paper, we propose a generalized yet effective 3M (i.e., multi-robot, multi-scenario, and multi-stage) training framework. We optimize a mapless navigation policy with a robust policy gradient algorithm. Our method enables different types of mobile platforms to navigate safely in complex and highly dynamic environments, such as pedestrian crowds. To demonstrate the superiority of our method, we test our methods with four kinds of mobile platforms in four scenarios. Videos are available at https://sites.google.com/view/crowdmove.

arXiv commentarXiv admin note: text overlap with arXiv:1709.10082

References in corpus (5)

• From Perception to Decision: A Data-driven Approach to End-to-end Motion Planning for Autonomous Ground Robots
• Uncertainty-Aware Reinforcement Learning for Collision Avoidance
• Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation
• Socially Aware Motion Planning with Deep Reinforcement Learning
• Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning

Cited by in corpus (7)

• Mapless Navigation among Dynamics with Social-safety-awareness: a reinforcement learning approach from 2D laser scans
• Realtime Collision Avoidance for Mobile Robots in Dense Crowds using Implicit Multi-sensor Fusion and Deep Reinforcement Learning
• The Emotionally Intelligent Robot: Improving Social Navigation in Crowded Environments
• Learning to Navigate in a VUCA Environment: Hierarchical Multi-expert Approach
• Autonomous Social Distancing in Urban Environments using a Quadruped Robot
• Towards Target-Driven Visual Navigation in Indoor Scenes via Generative Imitation Learning
• EWareNet: Emotion Aware Human Intent Prediction and Adaptive Spatial Profile Fusion for Social Robot Navigation