The incorporation of fault tolerance techniques into robot systems improves the reliability, but also increases the hardware and computational requirements in the overall system. It is not always clear how to evaluate the merit, or 'effectiveness' of different fault tolerance approaches for a given application. We present a new set of performance criteria designed to measure and compare the effectiveness of robot fault tolerance strategies. The measures, which are designed to evaluate fault tolerance/performance/cost tradeoffs, can also be used to evaluate pure performance or pure fault tolerance strategies. We show their usefulness using a variety of proposed fault tolerance approaches in the literature focusing on multiprocessor control architectures.

Due to the coupling in the dynamics equations, coarse-grain parallelism of robot control algorithms is particularly difficult. We have developed a new algorithm based on the Newton-Euler dynamics formulation that overcomes the serial nature of these equations, allowing a high level of parallelism. Our controller uses data from a previous control step in current calculations to allow many more tasks to be executed in parallel, thus providing higher control update rates. The use of 'stale' data is an effective solution to the speedup problem, but presents some special difficulties. One stability issue when using 'stale' data that is encountered in previous approaches is discussed here, along with a partial solution to the problem.