TY - JOUR

T1 - Applying probability navigation function in dynamic uncertain environments

AU - Hacohen, Shlomi

AU - Shoval, Shraga

AU - Shvalb, Nir

N1 - Publisher Copyright:
© 2016 Elsevier B.V.

PY - 2017/1/1

Y1 - 2017/1/1

N2 - This paper introduces a novel motion planning algorithm for stochastic dynamic scenarios. We apply a Probability Navigation Function (PNF), discussed in the authors’ previous research work, to dynamic environments. We first consider the ambient configuration space to be an n- dimensional ball; the robot and the obstacles loci are all known with a Gaussian probability distribution, and both the robot and the obstacles are assumed to have n-dimensional disc shapes. We fuse the geometries of the robot and the obstacles with the localization probability distribution using convolution. We then define a Probability Navigation Function (PNF) φ from the configuration space to R. We also provide a numerical method for the case where the obstacles and the robot shapes are non-symmetric and their probability distributions are non-Gaussian respectively. The PNF is applied to the dynamic case, where the obstacles are moving at different velocities, by calculating consecutive probability navigation functions according to a prediction of the obstacles’ positions and their estimation error covariance. We then apply a simulated annealing scheme on the sequence of motion directions to choose an optimal path. We demonstrate our algorithm for various scenarios.

AB - This paper introduces a novel motion planning algorithm for stochastic dynamic scenarios. We apply a Probability Navigation Function (PNF), discussed in the authors’ previous research work, to dynamic environments. We first consider the ambient configuration space to be an n- dimensional ball; the robot and the obstacles loci are all known with a Gaussian probability distribution, and both the robot and the obstacles are assumed to have n-dimensional disc shapes. We fuse the geometries of the robot and the obstacles with the localization probability distribution using convolution. We then define a Probability Navigation Function (PNF) φ from the configuration space to R. We also provide a numerical method for the case where the obstacles and the robot shapes are non-symmetric and their probability distributions are non-Gaussian respectively. The PNF is applied to the dynamic case, where the obstacles are moving at different velocities, by calculating consecutive probability navigation functions according to a prediction of the obstacles’ positions and their estimation error covariance. We then apply a simulated annealing scheme on the sequence of motion directions to choose an optimal path. We demonstrate our algorithm for various scenarios.

KW - Collision probability

KW - Navigation-function

KW - Obstacle avoidance

UR - http://www.scopus.com/inward/record.url?scp=84997522781&partnerID=8YFLogxK

U2 - 10.1016/j.robot.2016.10.010

DO - 10.1016/j.robot.2016.10.010

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AN - SCOPUS:84997522781

SN - 0921-8890

VL - 87

SP - 237

EP - 246

JO - Robotics and Autonomous Systems

JF - Robotics and Autonomous Systems

ER -