Semantic Feasibility Reasoning for Heterogeneous Multi-Robot Task Allocation

In heterogeneous multi-robot systems, allocating tasks efficiently requires determining whether each robot can actually carry out a given task. In existing multi-robot task allocation research, however, such task feasibility has typically been handled inside a particular optimizer or symbolic planner, while spatial traversability has been assessed against static criteria that cannot capture the changes induced by a robot’s loaded state. This paper proposes an ontology-based semantic feasibility reasoning method for heterogeneous multi-robot task allocation. The proposed method defines semantic models for robots, tasks, and places and applies hybrid reasoning, combining declarative reasoning with procedural evaluation, to determine multi-axis capability conditions and loaded-state place reachability. The reasoning result is formalized as an allocator-independent ReasonerOutput that serves as a common input for diverse allocation algorithms. In experiments spanning four scenarios over three fleet configurations and four allocators, together with an ablation study on 200 randomized instances at each of three problem scales, the proposed ReasonerOutput consistently functioned as a shared semantic feasibility constraint. The experiments further showed that both the fleet composition and the loaded state of the target item affect assignment feasibility. These results indicate that, for the static one-shot assignment setting evaluated here, the proposed method makes the task feasibility of heterogeneous robots explicit through semantic reasoning and allows allocators of differing algorithmic character to draw on this feasibility in common.