2 research outputs found
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Planning velocity profiles from task-level constraints and environment uncertainties
A method for parameterizing robot trajectories in the presence of uncertainties is presented. The planning process is defined as a problem of constrained optimization and the concept of a task's difficulty is used as an optimization criterion. The task difficulty, as defined by the authors, comprises the combined effects of velocity and uncertainty, mimicking human perception of difficulty in positioning tasks. The success probability is used as a constraint necessary for planning tasks with contradicting requirements. This planning paradigm is demonstrated with an experiment that contains opposing requirements: reaching the obstacle in a given time, but without exceeding certain maximal impact force. The planner is implemented on a real system
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Probability-driven motion planning for mobile robots
This paper proposes a path-planning method for mobile robots in the presence of uncertainty. We analyze environment and control uncertainty and propose methods for incorporating each of them into the planning algorithm. We model the environment using the pyramid structure that encodes the information on occupancy probabilities for each pixel as well as the partial information on conditional probabilities among different pixels. This structure allows for efficient and accurate computation of collision probabilities in the presence of environment uncertainty. The control uncertainty is mainly characterized by its expansion in space and time and is accordingly modeled by a stochastic differential equation that mathematically captures this phenomenon. Models that we develop are inevitably approximate but experiments confirm that they can be used as a reasonable model for motion planning. We have conducted a series of experiments on the mobile platform and some of these results are presented