Local-global concurrent path planning and execution

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Sensor-based discovery (i.e. dynamic) path planning is problematic because the path needs to be continually recomputed as new information is discovered. A process-based client-server approach is presented that permits concurrent sensor-based map and localization-correction updates as well as concurrent path computation and execution. A potential function is created by solving Laplace's equation (i.e. a harmonic function) using an iteration kernal convolved with an occupancy-grid representation of the current free space. The path produced (i.e. by steepest gradient descent on the harmonic function) is optimal in the sense of globally minimizing the distance to the goal as well as locally minimizing a hitting probability. This helps alleviate the influence of uncertainty on path planning. On a regular grid, the computation of the harmonic function is linear in the total number of grid elements, thus constraining this planner to be local. A global planner-provided that an a priori CAD map of the fixed objects exists-provides information to the local planner about the effects of a global goal. An algorithm for the three typical local scenarios found in indoor office-like environments is presented showing how a global goal is projected into the local context for each case. Since objects are sensed on-the-fly it is possible to discover that passage through a hallway or room may be blocked. A zero gradient vector in the local potential function is used to signal a blocked passage and subsequently initiate global replanning. The technique has been used to control a Nomadics 200 robot.