Multi-Robot Persistent Coverage with stochastic task costs

We propose the Stochastic Multi-Robot Persistent Coverage Problem (SMRPCP) and correspondant methodology to compute an optimal schedule that enables a fleet of energy-constrained unmanned aerial vehicles to repeatedly perform a set of tasks while maximizing the frequency of task completion and preserving energy reserves via recharging depots. The approach enables online modeling of uncertain task costs and yields a schedule that adapts according to an evolving energy expenditure model. A fast heuristic method is formulated that enables online generation of a schedule that concurrently maximizes task completion frequency and avoids the risk of individual robot energy-depletion and consequential platform failure. Failure mitigation is introduced through a recourse strategy that routes robots based on acceptable levels of risk. Simulation and experimental results evaluate the efficacy of the proposed methodology and demonstrate online system-level adaptation due to increasingly certain costs models acquired during the deployment execution.

Authors

• Derek Mitchell
• Nilanjan Chakraborty
• Katia P. Sycara
• Nathan Michael

Keywords

• Stochastic processes
• Adaptation models
• Robot sensing systems
• Monte Carlo methods
• Energy loss
• Schedules
• Task Cost
• Energy Consumption
• Simulation Results
• Energy Demand
• Risk Of Failure
• Unmanned Aerial Vehicles
• Cost Model
• Heuristic Method
• Acceptable Level Of Risk
• Individual Robots
• Convergence Rate
• Target Location
• Kalman Filter
• Local Setting
• Task Execution
• Energy Availability
• Consumption Cost
• Energy Gain
• Mixed Integer Linear Programming
• Impact Of Risk
• Integer Constraints
• Energy Constraints
• Energy Depletion
• Probability 1
• Acceptable Risk
• Optimal Deployment
• Flow Constraints
• Current Expenditure
• Cost Matrix
• Energy Capacity