Unexpected contingencies in robot execution may induce a cascade of effects, especially when multiple robots are involved. In order to effectively adapt to this, robots need the ability to reason along multiple dimensions at execution time. We propose an approach to closed-loop planning capable of generating configuration plans, i.e., action plans for multirobot systems which specify the causal, temporal, resource and information dependencies between individual sensing, computation, and actuation components. The key feature which enables closed loop performance is that configuration plans are represented as constraint networks, which are shared between the planner and the executor and are continuously updated during execution.We report experiments run both in simulation and on real robots, in which a fault in one robot is compensated through different types of planmodifications at run time.
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