A two-phased evolutionary approach for intelligent task assignment &amp; scheduling

In recent years, there have been increasing reports of missing tourists around the world. The use of unmanned aerial vehicles (UAVs) can significantly improve the performance of search and rescue operations. However, planning the search paths of UAVs can be a highly complex optimization problem, and one of the most challenging tasks in the problem formulation is the estimation of target location probability distribution over time. This paper presents a problem of scheduling multiple UAVs to search for missing tourists and proposes a method for estimating tourist location probabilities which change with topographic features, weather conditions, and time. To solve the problem efficiently, we propose a hybrid evolutionary algorithm which consists of the main algorithm and a sub-algorithm. The main algorithm uses specific migration and mutation operators to evolve a population of main solutions, and the sub-algorithm combines a problem-specific heuristic and tabu search method to optimize each UAV path. The experimental results on a wide variety of test instances (including five real-world instances) demonstrate the performance advantages of the proposed method.INDEX TERMS Unmanned aerial vehicle (UAV), path planning, discrete-time optimization, evolutionary algorithms.

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Wheeled Robot Path Planning in Natural Environment

Kwaśniewski

Gosiewski

2020

2020 International Conference Mechatronic Systems and Materials (MSM)

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A two-phased evolutionary approach for intelligent task assignment & scheduling

Cited by 4 publications

References 19 publications

UAV Path Planning Using Optimization Approaches: A Survey

UAV Path Planning Using Optimization Approaches: A Survey

Evolutionary Planning of Multi-UAV Search for Missing Tourists

Wheeled Robot Path Planning in Natural Environment

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