Multi-robot task allocation problem with multiple nonlinear criteria using branch and bound and genetic algorithms

Abstract The paper proposes the formulation of a single-task robot (ST), single-robot task (SR), time-extended assignment (TA), multi-robot allocation (MRTA) problem with multiple, nonlinear criteria using discrete variables that drastically reduce computation burden. Obtaining an is addressed by Branch and Bound (B&B) algorithm in low scale problems genetic (GA) specifically developed for proposed larger problems. GA crossover mutation strategies design ensure descendant allocations each generation will maintain certain level feasibility, reducing greatly range possible descendants, accelerating their convergence to sub-optimal allocation. MRTA algorithms are simulated analyzed context thermosolar power plant, which spatially distributed Direct Normal Irradiance (DNI) estimated heterogeneous fleet composed both aerial ground unmanned vehicles. Three optimization simultaneously considered: distance traveled, time required complete energetic feasibility. Even though this uses plant as case study, can be applied any multi-criteria cost function equivalent way. performance response compared four different scenarios. results show B&B find global optimal solution reasonable robots six tasks. For problems, approaches much less time. Moreover, trade-off between accuracy easily carried out tuning parameters according available computational power.