Dynamic global path planning with uncertainty for mobile robots in manufacturing

We propose a probabilistic approach to the problem of global path planning with uncertainty for mobile robots in a dynamic manufacturing environment. To model the changing environment, we use a topological graph weighted by scalar cost functions. The cost functions consist of two elements: a deterministic cost for the known part of the robot's environment, and an uncertainty cost for the unknown part of the environment. Statistical models are built to quantify the unknown part of the environment, forming uncertainty costs for handling unexpected events. These uncertainty costs are dynamically updated by available sensor data when the mobile robot moves around. An optimal path (suboptimal in practice) is then found from the weighted topological graph using dynamic programming.