Robot motion is typically determined by searching a graph of a deterministic representation of the free configuration space available to the robot. This approach is successful only if the environment is static, problem degrees-of-freedom are low, long planning times are acceptable, and high-speed computing resources are available. In this paper an alternative approach to motion modeling is presented. The approach has its roots in a path planner developed for a multiple robot arm system operating in a complex task environment characterized by frequent environmental changes due to teleoperation and strict requirements on system response times. The probability of successful motion transit of the robot through a region of space is obtained from a geometric model and captures the conditioning effects on the motion by the presence of objects in the region and arm kinematics. This information is used to guide an on-line search and results in most cases with successful path determination within reasonably short search times. Relationships to diffusion flows and stochastic geometry are explored as are the possibilities of using sensor data directly in the model.
J. Balaram, J. Balaram,
"Probabilistic Methods For Robot Motion Determination", Proc. SPIE 1002, Intelligent Robots and Computer Vision VII, (27 March 1989); doi: 10.1117/12.960327; https://doi.org/10.1117/12.960327