Planning Multi-Step Error Detection and Recovery Strategies

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Robots must plan and execute tasks in the presence of uncertainty. Uncertainty arises from sensing errors, control errors, and uncertainty in the geometry of the environment. By employing a combined strategy of force and position control, a robot programmer can often guarantee reaching the desired final configuration 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 [D], 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. However, it is not always possible to find plans that are guaranteed to succeed. For example, if tolerancing errors render an assembly infeasible, the plan executor should stop and signal failure. In such cases the insistence on guaranteed success is too restrictive. For this reason we investigate Error Detection and Recovery (EDR) strategies. EDR plans will succeed or fail recognizably: in these more general strategies, there is no possibility that the plan will fail without the executor realizing it. The EDR framework fills a gap when guaranteed plans cannot be found or do not exist: it provides a technology for constructing plans that might work, but fail in a "reasonable" way when they cannot. We describe techniques for planning multi-step EDR strategies in the presence of uncertainty. Multi-step strategies are considerably more difficult to generate, and we introduce three approaches for their synthesis: these are the Push-forward Algorithm, Failure Mode Analysis, and the Weak EDR Theory. We have implemented the theory in the form of a planner, called LIMITED, in the domain of planar assemblies.
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Cornell University
computer science; technical report
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