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

TL;DR

D-ITAGS is a novel dynamic, resilient approach for multi-layered task allocation, scheduling, and motion planning in heterogeneous robot teams, effectively handling disruptions with interleaved execution and targeted repair.

Contribution

It introduces D-ITAGS, a new algorithm that integrates coalition formation, scheduling, and motion planning with resilience features and provides theoretical bounds on solution suboptimality.

Findings

D-ITAGS outperforms recomputation in dynamic scenarios with faster response times.

Solution quality remains high with minimal loss despite dynamic disruptions.

Theoretical suboptimality bounds are validated in practical experiments.

Abstract

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