Dual Reactive Planning for Heterogeneous Robots With Evolving Capabilities in Unknown Environments

Abstract

Heterogeneous robot teams executing Linear Temporal Logic (LTL) missions are usually modeled with fixed robot capabilities. In practice, capabilities may change during execution through tool acquisition, sensor activation, or module reconfiguration, making previously infeasible tasks executable and altering the remaining allocation. We present Dual Reactive Planning (DRP), a dual-reactive execution architecture for online LTL execution in initially unknown environments without assuming a complete prior map. DRP combines a shared automaton-based progress monitor with two replanning layers: local heuristic reassignment for environmental discoveries that change path costs, and mission-level reallocation for capability acquisitions that change task executability. The formulation targets missions whose allocation-relevant structure is specified through declared precedence relations and capability transitions. We formalize capability evolution through deterministic transition functions triggered by task completion and provide a scoped termination argument and complexity analysis. Comparative studies, targeted 2–4 robot capability-evolution benchmarks, and Gazebo experiments show that DRP solves all tested scenarios, whereas a capability-agnostic reactive baseline does not. Accounting for capability change during execution improves both mission completion and efficiency in the evaluated settings.