An iterative strategy for task assignment and path planning of distributed multiple unmanned aerial vehicles

Yao, Weiran; Qi, Naiming; Wan, Neng; Liu, Yongbei

doi:10.1016/j.ast.2019.01.061

Cited by 88 publications

(42 citation statements)

References 32 publications

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“…Table 6 summarizes the results of these two path estimation methods for this study case. In Table 6, the mission time denotes the total time to perform all the targets, the system utility was calculated by Equation (6). It can be seen that, the mission time using the straight line in the path estimation was longer than that using the PH curve, which also led to a lower system utility.…”

Section: Effects Of Different Path Estimation Methods On Task Allocationmentioning

confidence: 99%

“…Sharma et al [5] proposed a cooperative ad-hoc network framework for multi-UAV using a neural network, cooperative task allocation and path planning algorithms can be used in the framework in different applications. Yao et al [6] developed an iterative mission planning framework for the task allocation and path planning problems, an improved auction algorithm with an iterative strategy was proposed in the task allocation process. When multi-UAV is used to perform multi-task, the task allocation and path planning process are coupled [7], as the performance of task allocation depends on an accurate path length of UAVs, and the path planning depends on the UAV's task allocation results.…”

Section: Of 19mentioning

confidence: 99%

“…These UAVs could arrive simultaneously to the target T 2 . Using the proposed algorithms, the formed coalitions could obtain the maximum system utility in Equation (6). The UAV U 4 was selected to be the part of the two coalitions C 1 and C 2 .…”

Section: General Scenariomentioning

confidence: 99%

“…Each particle represents a solution to the task allocation problem. The fitness of each particle can be calculated using Equation (6). If the task allocation Figure 6a shows the average mission time using the two path estimation methods when the number of targets increased.…”

Section: Comparison Of Three Task Allocation Algorithmsmentioning

confidence: 99%

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A Hierarchical Mission Planning Method for Simultaneous Arrival of Multi-UAV Coalition

et al. 2019

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A hierarchical mission planning method was proposed to solve a simultaneous attack mission planning problem for multi-unmanned aerial vehicles (UAVs). The method consisted of three phases aiming to decouple and solve the mission planning problem. In the first phase, the Pythagorean hodograph (PH) curve was used in the path estimation process for each UAV, which also served as the input for the task allocation process. In the second phase, a task allocation algorithm based on a negotiation mechanism was proposed to assign the targets. Considering the resource requirement, time-dependent value of targets and resource consumption of UAVs, the proposed task allocation algorithm can generate a feasible allocation strategy and get the maximum system utility. In the last phase, a path planning method was proposed to generate a simultaneous arrival PH path for each UAV considering UAV’s kinematic constraint and collision avoidance with obstacles. The comparison simulations showed that the path estimation process using the PH curve and the proposed task allocation algorithm improved the system utility, and the hierarchical mission planning method has potential in a real mission.

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Section: Effects Of Different Path Estimation Methods On Task Allocationmentioning

confidence: 99%

Section: Of 19mentioning

confidence: 99%

Section: General Scenariomentioning

confidence: 99%

Section: Comparison Of Three Task Allocation Algorithmsmentioning

confidence: 99%

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A Hierarchical Mission Planning Method for Simultaneous Arrival of Multi-UAV Coalition

et al. 2019

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“…This would soon become unrealistic as N R increases. Yao et al [22] implemented a reestimation mechanism to reject the task assignment results when unrealistic task completion time occurs during the path planning stage. To the best of our knowledge, no existing work has considered collision mitigation within the task assignment problem.…”

Section: Introductionmentioning

confidence: 99%

Collision-aware Task Assignment for Multi-Robot Systems

Varadharajan

Beltrame

2019

2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS)

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We propose a novel formulation of the collisionaware task assignment (CATA) problem and a decentralized auction-based algorithm to solve the problem with optimality bound. Using a collision cone, we predict potential collisions and introduce a binary decision variable into the local reward function for task bidding. We further improve CATA by implementing a receding collision horizon to address the stopping robot scenario, i.e. when robots are confined to their task location and become static obstacles to other moving robots. The auction-based algorithm encourages the robots to bid for tasks with collision mitigation considerations. We validate the improved task assignment solution with both simulation and experimental results, which show significant reduction of overlapping paths as well as deadlocks.

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Multiple Missiles Launch Assignment Based on AHP and Genetic Algorithm

Liu

Jin-shi

2020

Man-Machine-Environment System Engineering

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An iterative strategy for task assignment and path planning of distributed multiple unmanned aerial vehicles

Cited by 88 publications

References 32 publications

A Hierarchical Mission Planning Method for Simultaneous Arrival of Multi-UAV Coalition

A Hierarchical Mission Planning Method for Simultaneous Arrival of Multi-UAV Coalition

Collision-aware Task Assignment for Multi-Robot Systems

Multiple Missiles Launch Assignment Based on AHP and Genetic Algorithm

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