A local obstacle avoidance method for mobile robots in partially known environment

Authors:
Chaoxia Shi;Yanqing Wang;Jingyu Yang
Affiliations:
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, 210094, China;School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, 210094, China
Venue:
Robotics and Autonomous Systems
Year:
2010

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Abstract

Local obstacle avoidance is a principle capability for mobile robots in unknown or partially known environment. A series of velocity space methods including the curvature velocity method (CVM), the lane curvature method (LCM) and the beam curvature method (BCM) formulate the local obstacle avoidance problem as one of constrained optimization in the velocity space by taking the physical constraints of the environment and the dynamics of the vehicle into account. We present a new local obstacle avoidance approach that combines the prediction model of collision with the improved BCM. Not only does this method inherit the quickness of BCM and the safety of LCM, but also the proposed prediction based BCM (PBCM) can be used to avoid moving obstacles in dynamic environments.