We propose a new formulation for the multi-robot task planning and allocation problem that incorporates (a) precedence relationships between tasks; (b) coordination for tasks allowing multiple robots to achieve increased efficiency; and (c) cooperation through the formation of robot coalitions for tasks that cannot be performed by individual robots alone. In our formulation, the tasks and the relationships between the tasks are specified by a task graph. We define a set of reward functions over the task graph's nodes and edges. These functions model the effect of robot coalition size on task performance while incorporating the influence of one task's performance on a dependent task. Solving this problem optimally is NP-hard. However, using the task graph formulation allows us to leverage min-cost network flow approaches to obtain approximate solutions efficiently. Additionally, we explore a mixed integer programming approach, which gives optimal solutions for small instances of the problem but is computationally expensive. We also develop a greedy heuristic algorithm as a baseline. Our modeling and solution approaches result in task plans that leverage task precedence relationships and robot coordination and cooperation to achieve high mission performance, even in large missions with many agents.

This paper investigates servicing waypoints in a wide area using collaborative deployments of vehicles with heterogeneous range and mobility constraints. We formulate a joint planning problem for a single transport truck and multiple service drones in which the truck is constrained to a road and must deploy a team of range-constrained drones to visit waypoints. The need to deploy, collect, and redeploy drones over multiple flights introduces both route finding and scheduling aspects to this problem. We solve large problem instances by decoupling our approach into a service drone route finding phase and a transport truck scheduling phase. Numerical simulations explore the qualitative character of the driving schedule and the quantitative marginal value of adding additional drones to the team as a function of agent number and relative speed. The combination of road network constraints and range constraints make this problem especially relevant to wide area forestry, last-mile delivery, and ecological monitoring applications.