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: 2.43). 09/2010; 26(4):619 - 634. DOI: 10.1109/TRO.2010.2048610
Source: IEEE Xplore


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.

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Available from: Luis de la Torre Cubillo, Jun 14, 2015
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    • "Since the purpose of this paper is not to construct a new set of realistic evaluation functions, we directly employ or derive some existing representative functions in the literature [1], [4], [40] to include several key factors in UAV path planning. Detailed technical justifications of the chosen functions can be found in the corresponding studies, i.e., [1], [4], [40]. Generally, these factors restrict the paths in a geometric manner. "
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    ABSTRACT: Evolutionary algorithm-based unmanned aerial vehicle (UAV) path planners have been extensively studied for their effectiveness and flexibility. However, they still suffer from a drawback that the high-quality waypoints in previous candidate paths can hardly be exploited for further evolution, since they regard all the waypoints of a path as an integrated individual. Due to this drawback, the previous planners usually fail when encountering lots of obstacles. In this paper, a new idea of separately evaluating and evolving waypoints is presented to solve this problem. Concretely, the original objective and constraint functions of UAVs path planning are decomposed into a set of new evaluation functions, with which waypoints on a path can be evaluated separately. The new evaluation functions allow waypoints on a path to be evolved separately and, thus, high-quality waypoints can be better exploited. On this basis, the waypoints are encoded in a rotated coordinate system with an external restriction and evolved with JADE, a state-of-the-art variant of the differential evolution algorithm. To test the capabilities of the new planner on planning obstacle-free paths, five scenarios with increasing numbers of obstacles are constructed. Three existing planners and four variants of the proposed planner are compared to assess the effectiveness and efficiency of the proposed planner. The results demonstrate the superiority of the proposed planner and the idea of separate evolution.
    IEEE Transactions on Robotics 10/2015; 31(5):1-17. DOI:10.1109/TRO.2015.2459812 · 2.43 Impact Factor
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    • "The example in Fig. 7 shows how the proposed method can also be used successfully to follow parameterized curves. These kinds of curves appear naturally when a UAV must operate in realistic scenarios, [40]. In this case the position of the reference point, with respect to the global reference frame, is parameterized as a third order polynomial of s. "
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    ABSTRACT: This paper presents a new nonlinear path- following guidance method for autonomous vehicles, which integrates two guidance laws that have given good results independently in applications in autonomous ground, marine, and air vehicles. This new technique retains the best aspects of its two supporting methods, whereas it overcomes some of their drawbacks. It uses the control law by Park et al. to command the vehicle position towards a reference point. However, the position of the reference point is controlled in a different way: instead of being calculated to stay at a fixed distance forward of the vehicle, we use a strategy, inspired in the works by other authors, that controls the speed of the reference point to maintain its position at the given distance. This change does not increment the number of parameters to tune the algorithm and makes it applicable to any initial conditions and parameterized paths. The paper also analyzes the stability of this new nonlinear guidance control law and shows its effectiveness under different simulations.
    2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, Washington; 05/2015
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    • "Consequently, these problems have usually been addressed using other types of metaheuristics such as Particle Swarm Optimization (PSO) [11] [5]. EAs have only been used indirectly in these cases by some authors that concentrated on the off-line evolution of control strategies for the samplers that were able to use information from the environment for real-time improvements [12] [13]. "
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    ABSTRACT: This work proposes a set of modifications to the Differential Evolution algorithm in order to make it more efficient in solving a particular category of problems, the so called Constrained Sampling problems. In this type of problems, which are usually related to the on-line real-world application of evolution, it is not always straightforward to evaluate the fitness landscapes due to the computational cost it implies or to physical constraints of the specific application. The fact is that the sampling or evaluation of the offspring points within the fitness landscape generally requires a decoding phase that implies physical changes over the parents or elements used for sampling the landscape, whether through some type of physical migration from their locations or through changes in their configurations. Here we propose a series of modifications to the Differential Evolution algorithm in order to improve its efficiency when applied to this type of problems. The approach is compared to a standard DE using some common real-coded benchmark functions and then it is applied to a real constrained sampling problem through a series of real experiments where a set of Unmanned Aerials Vehicles is used to find shipwrecked people.
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