Velocity occupancy space: autonomous navigation in an uncertain, dynamic environment

Abstract

In order to autonomously navigate in an unknown environment, a robotic vehicle must be able to sense obstacles, determine their velocities, and select a collision-free path that will lead quickly to a goal. However, the perceived location and motion of the obstacles will be uncertain due to the limited accuracy of the robot's sensors. Thus, it is necessary to develop a system that can avoid moving obstacles using uncertain sensor data. The method proposed here is based on an occupancy grid -which has been used to avoid stationary obstacles in an uncertain environment -in conjunction with velocity obstacles -which allow a robot to avoid well-known moving obstacles. The combination of these techniques leads to Velocity Occupancy Space (VOS): a search space which allows the robot to avoid moving obstacles and navigate efficiently to a goal using uncertain sensor data. The proposed method is validated by numerous simulation trials. 42R. Bis et al.

Pedestrian Avoidance for Unmanned Ground Vehicles Based on Velocity Occupancy Space

“…Velocity Occupancy Space (VOS) is an obstacle avoidance technique developed by Bis et al [1,2] for use with mobile robots that have sensor uncertainties. This paper focuses on characterizing how VOS changes behavior as parameters are varied and how to adjust them in order to ensure pedestrian safety and comfort.…”

“…Velocity Occupancy Space At a high-level, VOS operates by mapping detected obstacles into a velocity space [1,2]. Then, VOS calculates the velocities that will lead to collisions.…”

Velocity Occupancy Space for Unmanned Ground Vehicles With Actuation Error

“…These terms help to ensure the robot's safety in an uncertain environment. As they are not specifically relevant to the material in this paper, the reader is directed to [2,15] for a complete description.…”

“…In Table 2, the values of the evaluation metrics for the simulations are tabulated [2,15]. However, the obstacle proximity and the acceleration were divided by the number of motion time steps in each simulation so that the results could be more accurately compared between simulation and experimental scenarios with different goal locations.…”

Autonomous mobile robot navigation based on an integrated environment representation designed in dynamic environments

“…However, the problem of avoiding collisions in dynamic environment is much harder. Several works have been developed for dynamic environments like velocity obstacles (Fiorini and Shiller, 1998;Large et al, 2005), collision cones (Chakravarthy and Ghose, 1998), the rolling window method (Zhang and Xi, 2003), inevitable collision state (Fraichard and Asama, 2004), the prediction model for beam curvature method (Shi et al, 2010), and other methods (Jaradata et al, 2011;Yaonana and Yimin, 2011;Zhu and Hua, 2011;Zhong et al, 2014;Bis et al, 2012). This paper is organised as follows.…”