Ant Colony Optimization for Routing and Tasking Problems for Teams of UAVs

Thei E Zaza, Arthur G Richards

Research output: Contribution to conferenceConference Paperpeer-review

27 Citations (Scopus)


This paper presents an enhanced version of the ant colony optimization (ACO) for solving an improved model of vehicles routing problem (VRP), which is utilized for Unmanned Aerial Vehicle (UAV) task loitering and route planning. The improved VRP incorporates collision avoidance penalties, not only for the intersections between the vehicle's routes, but also for their departure and landing times. The ant colony algorithm uses a single objective function, consisting of penalties, ensuring in that way that the solutions with intersections will be evaluated. The ACO is a variation of the already known multi-colony algorithm where several ant colonies are assigned to different loitering steps for the same route between two tasks. Numerical experiments and comparison to previous work are illustrated to demonstrate the efficiency of the proposed algorithm.
Original languageEnglish
Publication statusPublished - 9 Jul 2014
Event2014 UKACC International Conference on Control - Loughborough, United Kingdom
Duration: 9 Jul 201411 Jul 2014


Conference2014 UKACC International Conference on Control
Country/TerritoryUnited Kingdom


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