Long-term monitoring of numerous dynamic targets can be tedious for a human operator and infeasible for a single robot, e.g., to monitor wild flocks, detect intruders, search and rescue. Fleets of autonomous robots can be effective by acting collaboratively and concurrently. However, the online coordination is challenging due to the unknown behaviors of the targets and the limited perception of each robot. Existing work often deploys all robots available without minimizing the fleet size, or neglects the constraints on their resources such as battery and memory. This work proposes an online coordination scheme called LOMORO for collaborative target monitoring, path routing and resource charging. It includes three core components: (I) the modeling of multi-robot task assignment problem under the constraints on resources and monitoring intervals; (II) the resource-aware task coordination algorithm iterates between the high-level assignment of dynamic targets and the low-level multi-objective routing via the Martin's algorithm; (III) the online adaptation algorithm in case of unpredictable target behaviors and robot failures. It ensures the explicitly upper-bounded monitoring intervals for all targets and the lower-bounded resource levels for all robots, while minimizing the average number of active robots. The proposed methods are validated extensively via large-scale simulations against several baselines, under different road networks, robot velocities, charging rates and monitoring intervals.
Illustration of the considered scenario. Top-Left and Top-Right: 3–4 UAVs are actively monitoring 10 targets (in red) within a road network, with their static (t = 12 s) and online (t = 381 s) plans. Middle-Left: Batteries of 6 robots during the online execution of 400 s. Bottom-Left: Intervals from the last monitoring of 10 targets. Middle-Right: Average number of active robots, the time when replans take place, and their computation time. Bottom-Right: The robots responsible for each target during any consecutive replans. Each target has 2 rows, with the lower representing the target node and the upper the intersection target node respectively.
Illustration of another considered scenario. Top-Left and Top-Right: Trajectories of 8 robots actively monitoring 15 targets in the considered scenario at t = 84 s and t = 347 s respectively. Bottom: The action of 8 robots at each time during the simulation of 400 s.
Illustration of scalability analysis results. The number of active robots with an increasing number of targets, concerning γn/βs (a), Tm (b), and Cn (c). The computation time with an increasing number of targets, concerning Cn (d).
Illustration of the generalization circumstances. (a): Previous plan result at t = 20 s when a robot accidentally crashes. (b): New replan after the robot crashed. (c): Trajectories of 6 robots monitoring 15 targets that can dynamically enter and exit the road network over a period of 400 s.
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