Planning Multi-Step Error Detection and Recovery Strategies

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Abstract
Robots must plan and execute tasks in the presence of uncer tainty. Uncertainty arises from sensing errors, control errors, and the geometry of the environment. By employing a com bined strategy offorce and position control, a robot program mer can often guarantee reaching the desired final configura tion from all the likely initial configurations. Such motion strategies permit robots to carry out tasks in the presence of significant uncertainty. However, compliant motion strategies are very difficult for humans to specify. For this reason we have been working on the automatic synthesis of motion strategies for robots. In previous work (Donald 1988b; 1989), we presented a framework for computing one-step motion strategies that are guaranteed to succeed in the presence of all three kinds of uncertainty. The motion strategies comprise sensor-based gross motions, compliant motions, and simple pushing motions.

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