Applying probability navigation function in dynamic uncertain environments

作者：Hacohen Shlomi; Shoval Shraga; Shvalb Nir^*

来源：Robotics and Autonomous Systems, 2017, 87: 237-246.

DOI：10.1016/j.robot.2016.10.010

• Navigation-function
• Collision probability
• Obstacle avoidance

This paper introduces a novel motion planning algorithm for stochastic dynamic scenarios. We apply a Probability Navigation 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 PNF) phi 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.

• 出版日期2017-1