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Complex, multi-objective missions require the coordination of heterogeneousrobots at multiple inter-connected levels, such as coalition formation,scheduling, and motion planning. This challenge is exacerbated by dynamicchanges, such as sensor and actuator failures, communication loss, andunexpected delays.  We introduce Dynamic Iterative Task Allocation Graph Search (D-ITAGS) to\textit{simultaneously} address coalition formation, scheduling, and motionplanning in dynamic settings involving heterogeneous teams. D-ITAGS achievesresilience via two key characteristics: i) interleaved execution, and ii)targeted repair. \textit{Interleaved execution} enables an effective search forsolutions at each layer while avoiding incompatibility with other layers.\textit{Targeted repair} identifies and repairs parts of the existing solutionimpacted by a given disruption, while conserving the rest. In addition toalgorithmic contributions, we provide theoretical insights into the inherenttrade-off between time and resource optimality in these settings and derivemeaningful bounds on schedule suboptimality.  Our experiments reveal that i) D-ITAGS is significantly faster thanrecomputation from scratch in dynamic settings, with little to no loss insolution quality, and ii) the theoretical suboptimality bounds consistentlyhold in practice.

D-ITAGS: A Dynamic Interleaved Approach to Resilient Task Allocation, Scheduling, and Motion Planning