Acquisition of statistical motion patterns in dynamic environments and their application to mobile robot motion planning

In recent papers we have proposed a new path planning approach for mobile robots: Statistical motion planning respects typical obstacle behavior in order to improve pre-planning in dynamic environments. In this paper, we present our experimental system: In a real environment, cameras observe the workspace in order to detect obstacle motions and to derive statistical data. We have developed new techniques based on stochastic trajectories to model obstacle behavior. Collision probabilities are calculated for polygonal objects moving on piecewise linear trajectories. The statistical data can be applied directly; thus, the entire chain from raw sensor data to a stochastic assessment of robot trajectories is closed. Finally, some new work regarding different applications of statistical motion planning is outlined: road-map approaches for pre-planning, expected time to reach the goal, reactive behaviors.