A hybrid many-objective competitive swarm optimization algorithm for large-scale multirobot task allocation problem

Xue, Fei; Dong, Tingting; You, Siqing; Liu, Yan; Tang, Hengliang; Chen, Lei; Yang, Xi; Li, Juntao

doi:10.1007/s13042-020-01213-4

Cited by 20 publications

(1 citation statement)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…-Deterministic, such as gradient and Hessian-based methods [23,24]. -Stochastic, such as meta-heuristic algorithms [25] like the Ant Colony Algorithm [26], Cuckoo Search [27][28][29], Particle Swarm Optimization (PSO) [30][31][32] or genetic algorithm (GA) [33] among others.…”

Section: Introductionmentioning

confidence: 99%

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

Martín

Frejo

García

et al. 2021

Intel Serv Robotics

View full text Add to dashboard Cite

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

show abstract