Evolutionary Trajectory Planner for Multiple UAVs in Realistic Scenarios

Dept. de Comput. y Autom., Univ. Complutense de Madrid, Madrid, Spain
IEEE Transactions on Robotics (Impact Factor: 2.57). 09/2010; DOI: 10.1109/TRO.2010.2048610
Source: IEEE Xplore

ABSTRACT This paper presents a path planner for multiple unmanned aerial vehicles (UAVs) based on evolutionary algorithms (EAs) for realistic scenarios. The paths returned by the algorithm fulfill and optimize multiple criteria that 1) are calculated based on the properties of real UAVs, terrains, radars, and missiles and 2) are structured in different levels of priority according to the selected mission. The paths of all the UAVs are obtained with the multiple coordinated agents coevolution EA (MCACEA), which is a general framework that uses an EA per agent (i.e., UAV) that share their optimal solutions to coordinate the evolutions of the EAs populations using cooperation objectives. This planner works offline and online by means of recalculating parts of the original path to avoid unexpected risks while the UAV is flying. Its search space and computation time have been reduced using some special operators in the EAs. The successful results of the paths obtained in multiple scenarios, which are statistically analyzed in the paper, and tested against a simulator that incorporates complex models of the UAVs, radars, and missiles, make us believe that this planner could be used for real-flight missions.

  • Source
    Journal of Intelligent and Robotic Systems 01/2014; 73((1-4)):737-762. · 0.83 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Path planning technique is important to Unmanned Aerial Vehicle (UAV). Evolutionary Algorithms (EAs) have been widely used in planning path for UAV. In these EA-based path planners, Cartesian coordinate system and polar coordinate system are commonly used to codify the path. However, either of them has its drawback: Cartesian coordinate systems result in an enormous search space, whilst polar coordinate systems are unfit for local modifications resulting e.g., from mutation and/ or crossover. In order to overcome these two drawbacks, we solve the UAV path planning in a new coordinate system. As the new coordinate system is only a rotation of Cartesian coordinate system, it is inherently easy for local modification. Besides, this new coordinate system has successfully reduced the search space by explicitly dividing the mission space into several subspaces. Within this new coordinate system, an Estimation of Distribution Algorithms (EDAs) based path planner is proposed in this paper. Some experiments have been designed to test different aspects of the new path planner. The results show the effectiveness of this planner.
    IEEE Congress on Evolutionary Computation, Beijing, China; 07/2014
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: An Unmanned Aerial Vehicle (UAV) is an aircraft without onboard human pilot, which motion can be remotely and / or autonomously controlled. Using multiple UAVs, i.e. a fleet, offers various advantages compared to the single UAV scenario, such as longer mission duration, bigger mission area or the load balancing of the mission payload. For collaboration purposes, it is assumed that the UAVs are equipped with ad hoc communication capabilities and thus form a special case of mobile ad hoc networks. However, the coordination of one or more fleets of UAVs, in order to fulfill collaborative missions, raises multiples issues in particular when UAVs are required to act in an autonomous fashion. Thus, we propose a decentralised and localised algorithm to control the mobility of the UAVs. This algorithm is designed to perform surveillance missions with network connectivity constraints, which are required in most practical use cases for security purposes as any UAV should be able to be contacted at any moment in case of an emergency. The connectivity is maintained via a tree-based overlay network, which root is the base station of the mission, and created by predicting the future positions of one-hop neighbours. This algorithm is compared to the state of the art contributions by introducing new quality metrics to quantify different aspect of the area coverage process (speed, exhaustivity and fairness). Numerical results obtained via simulations show that the maintenance of the connectivity has a slight negative impact on the coverage performances while the connectivity performances are significantly better.
    Proceedings of the 11th ACM international symposium on Mobility management and wireless access; 11/2013