Autonomous task allocation for multi-UAV systems based on the locust elastic behavior

Kurdi, Heba; Aloboud, Ebtesam; Alalwan, Maram; Alhassan, Sarah; Alotaibi, Ebtehal Turki Saho; Bautista, Guillermo; How, Jonathan P.

doi:10.1016/j.asoc.2018.06.006

Cited by 54 publications

(47 citation statements)

References 25 publications

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“…Swarms and their self-organisation borrows much from the observation of bees [ 20 ], locusts [ 21 ], birds and fish [ 22 , 23 , 24 , 25 ]. The close resemblance between unmanned aircraft and insects or animals has been researched [ 26 ], specifically in terms of collision avoidance and in collaborative intelligence [ 27 ], while Can et al [ 28 ] applies the rules of particle physics to swarms.…”

Section: Related Workmentioning

confidence: 99%

Cross-Entropy as a Metric for the Robustness of Drone Swarms

Cofta

Ledziński

Śmigiel

et al. 2020

Entropy

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Due to their growing number and increasing autonomy, drones and drone swarms are equipped with sophisticated algorithms that help them achieve mission objectives. Such algorithms vary in their quality such that their comparison requires a metric that would allow for their correct assessment. The novelty of this paper lies in analysing, defining and applying the construct of cross-entropy, known from thermodynamics and information theory, to swarms. It can be used as a synthetic measure of the robustness of algorithms that can control swarms in the case of obstacles and unforeseen problems. Based on this, robustness may be an important aspect of the overall quality. This paper presents the necessary formalisation and applies it to a few examples, based on generalised unexpected behaviour and the results of collision avoidance algorithms used to react to obstacles.

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Section: Related Workmentioning

confidence: 99%

Cross-Entropy as a Metric for the Robustness of Drone Swarms

Cofta

Ledziński

Śmigiel

et al. 2020

Entropy

View full text Add to dashboard Cite

show abstract

“…The trajectory planning problem is complex, as (1), (2), (6), (7) and (8) are all nonlinear constraints. Hence reasonable estimations of the costs of potential trajectories are required.…”

Section: Mumtmp Problem Formulationmentioning

confidence: 99%

“…To improve the computational efficiency, distributed methods have been studied. The max-mum method has been widely used to solve the task assignment problem for distributed multiple robots [8][9][10]. The auction-based algorithm has also been widely used in task assignment, but the communication loads among team members are usually too heavy.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, in each round of auction, only one single task is solved, and it is easy for the solution to fall into local optima when a UAV performs multiple tasks [11,12]. The bionics-based optimization method could solve the task assignment of multiple UAVs quickly; however, it is mainly used in scenarios where each UAV performs a single task [8].…”

Section: Introductionmentioning

confidence: 99%

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A Hierarchical Cooperative Mission Planning Mechanism for Multiple Unmanned Aerial Vehicles

et al. 2019

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In this paper, the cooperative multi-task online mission planning for multiple Unmanned Aerial Vehicles (UAVs) is studied. Firstly, the dynamics of unmanned aerial vehicles and the mission planning problem are studied. Secondly, a hierarchical mechanism is proposed to deal with the complex multi-UAV multi-task mission planning problem. In the first stage, the flight paths of UAVs are generated by the Dubins curve and B-spline mixed method, which are defined as “CBC)” curves, where “C” stands for circular arc and “B” stands for B-spline segment. In the second stage, the task assignment problem is solved as multi-base multi-traveling salesman problem, in which the “CBC” flight paths are used to estimate the trajectory costs. In the third stage, the flight trajectories of UAVs are generated by using Gaussian pseudospectral method (GPM). Thirdly, to improve the computational efficiency, the continuous and differential initial trajectories are generated based on the “CBC” flight paths. Finally, numerical simulations are presented to demonstrate the proposed approach, the designed initial solution search algorithm is compared with existing methods. These results indicate that the proposed hierarchical mission planning method can produce satisfactory mission planning results efficiently.

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“…Most of these visible studies mainly focus on these two aspects. For efficient allocation tasks among multiple UAVs during a mission, Kurdi et al proposed a new autonomous bio-inspired approach that allows each UAV to adjust the task assignment according to its individual status and mission parameters, which was inspired by the nature of locust elastic behavior [32]. For the UAV air combat task assignment problem, Chen et al established a task allocation model and put forward a discrete particle swarm optimization algorithm [33].…”

Section: Mechanisms For Mission-level Cooperationmentioning

confidence: 99%

Multi-Constraint Optimized Planning of Tasks on Virtualized-Service Pool for Mission-Oriented Swarm Intelligent Systems

et al. 2019

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With the emergence of swarm intelligent systems, especially the swarming of aircraft and ground vehicles, cooperation in multiple dimensions has becoming one of the great challenges. How to dynamically schedule the resources within a swarm intelligent system and optimize the execution of tasks are all vital aspects for such systems. Focusing on this topic, in this paper, one new task planning mechanism with multiple constraints is proposed to solve such dynamic programming problems. Concretely, several fundamental models, covering three-level task models and resource-service pool models, are put forward and defined first. Considering the limitations of swarm systems running within complicated cyber-physical space, multi-dimension constraints for tasks scheduling and execution are further modeled and established. On this basis, we mapped this planning problem to an optimization searching problem, and then proposed a Genetic-Algorithm-based mechanism. All these works have been verified with simulated cooperation scenes. Experimental results show that this new mechanism is efficient to solve such resource-related and mission-oriented cooperation problems in complicated environments.

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Autonomous task allocation for multi-UAV systems based on the locust elastic behavior

Cited by 54 publications

References 25 publications

Cross-Entropy as a Metric for the Robustness of Drone Swarms

Cross-Entropy as a Metric for the Robustness of Drone Swarms

A Hierarchical Cooperative Mission Planning Mechanism for Multiple Unmanned Aerial Vehicles

Multi-Constraint Optimized Planning of Tasks on Virtualized-Service Pool for Mission-Oriented Swarm Intelligent Systems

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