Integrated task assignment and path optimization for cooperating uninhabited aerial vehicles using genetic algorithms

Eugene Edison, Tal Shima

Research output: Contribution to journalArticlepeer-review


The problem of integrating task assignment and planning paths for a group of cooperating uninhabited aerial vehicles, servicing multiple targets, is addressed. In the problem of interest the uninhabited aerial vehicles need to perform multiple consecutive tasks cooperatively on each ground target. A Dubins car model is used for motion planning, taking into account each vehicle's specific constraint of minimum turn radius. By using a finite set to define the visitation angle of a vehicle over a target we pose the integrated problem of task assignment and path optimization in the form of a graph. This new approach results in suboptimal trajectory assignments. Refining the visitation angle discretization allows for an improved solution. Due to the computational complexity of the resulting combinatorial optimization problem, we propose genetic algorithms for the stochastic search of the space of solutions. We distinguish between two cases of vehicle group composition: homogeneous, where all vehicles are identical; and heterogeneous, where the vehicles may have different operational capabilities and kinematic constraints. The performance of the genetic algorithms is demonstrated through sample runs and a Monte Carlo simulation study. Results show that the algorithms quickly provide good feasible solutions, and find the optimal solution for small sized problems.

Original languageEnglish
Pages (from-to)340-356
Number of pages17
JournalComputers and Operations Research
Issue number1
StatePublished - Jan 2011


  • Combinatorial optimization
  • Dubins' car
  • Genetic algorithms
  • Task assignment
  • Unmanned aerial vehicles

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Modelling and Simulation
  • Management Science and Operations Research


Dive into the research topics of 'Integrated task assignment and path optimization for cooperating uninhabited aerial vehicles using genetic algorithms'. Together they form a unique fingerprint.

Cite this