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Multi-robot systems must be able to maintain performance when robots get delayed during execution. For mobile robots, one source of delays is congestion. Congestion occurs when robots deployed in shared physical spaces interact, as robots present in the same area simultaneously must manoeuvre to avoid each other. Congestion can adversely affect navigation performance, and increase the duration of navigation actions. In this paper, we present a multi-robot planning framework which utilises learnt probabilistic models of how congestion affects navigation duration. Central to our framework is a probabilistic reservation table which summarises robot plans, capturing the effects of congestion. To plan, we solve a sequence of single-robot time-varying Markov automata, where transition probabilities and rates are obtained from the probabilistic reservation table. We also present an iterative model refinement procedure for accurately predicting execution-time robot performance. We evaluate our framework with extensive experiments on synthetic data and simulated robot behaviour.

The present study investigated the influence of mood state (positive vs. negative) on the cognitive control process of conflict adaptation. A task-switching paradigm was applied, allowing to assess conflict adaptation both within tasks and across tasks. A success-failure manipulation was applied for mood induction. Within-task conflict adaptation tended to be larger in negative mood than in positive mood, in line with previous findings in the literature. Across-task conflict adaptation was also observed, but only in the positive mood group: Participants in positive mood showed larger conflict adaptation than participants in negative mood. We suggest that different cognitive mechanisms underlie this double dissociation: Within-task conflict adaptation is enhanced in negative mood because response conflict is aversive in nature, and is therefore congruent to negative mood states. Between-task conflict adaptation is only present in positive mood because of less distinct task representations under positive mood, leading conflict adaptation to transfer to the other task. The data show that influences of mood state on cognitive control are multifaceted, with some control processes being enhanced and others attenuated in negative relative to positive mood. (PsycINFO Database Record

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