Autonomous Task Planning for Heterogeneous Multi-Agent Systems

This paper presents a solution to the automatic task planning problem for multi-agent systems. A formal framework is developed based on the Nondeterministic Finite Automata with $\epsilon$-transitions, where given the capabilities, constraints and failure modes of the agents involved, an initial state of the system and a task specification, an optimal solution is generated that satisfies the system constraints and the task specification. The resulting solution is guaranteed to be complete and optimal; moreover a heuristic solution that offers significant reduction of the computational requirements while relaxing the completeness and optimality requirements is proposed. The constructed system model is independent from the initial condition and the task specification, alleviating the need to repeat the costly pre-processing cycle for solving other scenarios, while allowing the incorporation of failure modes on-the-fly. Two case studies are provided: a simple one to showcase the concepts of the proposed methodology and a more elaborate one to demonstrate the effectiveness and validity of the methodology.Comment: Long version of paper submitted to the IEEE ICRA 2023 Conferenc

Automated Module Composition

We define an abstract problem of module composition (MC). In MC, modules are seen as black boxes with input and output ports. The objective is, given a set of available modules, to instantiate some of them (one or more times) and connect their ports, in order to obtain a target module. A general compatibility relation defines which ports can be connected to each other. Constraints are imposed on the number of instances of each module and the number of copies of each port. A linear objective function can be given to minimize the total cost of module instances and port copies