Publication
Robot navigation anticipative strategies in deep reinforcement motion planning
Conference Article
Conference
Iberian Robotics Conference (ROBOT)
Edition
5th
Pages
67-78
Doc link
http://dx.doi.org/10.1007/978-3-031-21062-4_6
File
Abstract
The navigation of robots in dynamic urban environments, requires elaborated anticipative strategies for the robot to avoid collisions with dynamic objects, like bicycles or pedestrians, and to be human-aware. We have developed and analyzed three anticipative strategies in motion planning taking into account the future motion of the mobile objects that can move up to 18 km/h. First, we have used our hybrid policy resulting from a Deep Deterministic Policy Gradient (DDPG) training and the Social Force Model (SFM), and we have tested it in simulation in four complex map scenarios with many pedestrians. Second, we have used these anticipative strategies in real-life experiments using the hybrid motion planning method and the ROS Navigation Stack with Dynamic Windows Approach (NS-DWA). The results in simulations and real-life experiments show very good results in open environments and also in mixed scenarios with narrow spaces.
Categories
artificial intelligence, learning (artificial intelligence), mobile robots, planning (artificial intelligence).
Author keywords
Deep Reinforcement Learning, Robot Navigation, Social Force Model, Human-Robot Interaction, Anticipation
Scientific reference
O. Gil and A. Sanfeliu. Robot navigation anticipative strategies in deep reinforcement motion planning, 5th Iberian Robotics Conference, 2022, Zaragoza, Spain, Vol 590 of Lecture Notes in Networks and Systems, pp. 67-78, 2023.
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