Coverage Path Planning Methods Focusing on Energy Efficient and Cooperative Strategies for Unmanned Aerial Vehicles

Fevgas, Georgios; Λάγκας, Θωμάς; Argyriou, Vasileios; Sarigiannidis, Panagiotis

doi:10.3390/s22031235

Cited by 45 publications

(20 citation statements)

References 81 publications

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“…Collisions and forbidden areas were outlined in [ 44 ]. In [ 45 , 46 ], the authors proposed a hybrid decomposition method and estimated a cellular decomposition method for dividing the coverage area into triangular shapes. A spiral design was proposed in [ 47 ] to facilitate path planning in areas with complex coverage.…”

Section: Preliminariesmentioning

confidence: 99%

Bio-Inspired Optimization-Based Path Planning Algorithms in Unmanned Aerial Vehicles: A Survey

Poudel

Arafat

Moh

2023

Sensors

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Advancements in electronics and software have enabled the rapid development of unmanned aerial vehicles (UAVs) and UAV-assisted applications. Although the mobility of UAVs allows for flexible deployment of networks, it introduces challenges regarding throughput, delay, cost, and energy. Therefore, path planning is an important aspect of UAV communications. Bio-inspired algorithms rely on the inspiration and principles of the biological evolution of nature to achieve robust survival techniques. However, the issues have many nonlinear constraints, which pose a number of problems such as time restrictions and high dimensionality. Recent trends tend to employ bio-inspired optimization algorithms, which are a potential method for handling difficult optimization problems, to address the issues associated with standard optimization algorithms. Focusing on these points, we investigate various bio-inspired algorithms for UAV path planning over the past decade. To the best of our knowledge, no survey on existing bio-inspired algorithms for UAV path planning has been reported in the literature. In this study, we investigate the prevailing bio-inspired algorithms extensively from the perspective of key features, working principles, advantages, and limitations. Subsequently, path planning algorithms are compared with each other in terms of their major features, characteristics, and performance factors. Furthermore, the challenges and future research trends in UAV path planning are summarized and discussed.

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Section: Preliminariesmentioning

confidence: 99%

Bio-Inspired Optimization-Based Path Planning Algorithms in Unmanned Aerial Vehicles: A Survey

Poudel

Arafat

Moh

2023

Sensors

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“…There are two different approaches: (global) planning of all movements before execution and situation-dependent movements -possible with local plans for a few next steps. A good overview of algorithms for movement planning for many mobile systems can be found for example in [4], [5]. There are algorithms based on multiagent reinforcement learning [6], on Multi-Agent Path Planning (MAPF) [11], with evolutionary algorithms [9], graph [4] and grid-based [7] solutions, combinations of algorithms for solving partial problems [12].…”

Section: State Of the Artmentioning

confidence: 99%

Area reconnaissance with heterogeneous groups of autonomous systems

Tchouchenkov,

Segor,

Buller

et al. 2023

Emerging Imaging and Sensing Technologies for Security and Defence VIII

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In the last years Unmanned Vehicles for different environments (UxVs) have been recognized as relevant game changers and key technologies for a wide range of military and civilian applications. Even parallel deployment of heterogeneous autonomous assets as swarms or cooperating teams is no longer science fiction, but a realistic operational scenario. Their effectiveness can be significantly increased by optimization of number, capabilities and application strategy of assigned assets, particularly in time-critical tasks (e.g. the search for missing persons, disaster relief and modern warfare operations). Especially, the movements of the vehicles must be carefully managed. However, the use of small UxVs is often accompanied by limitations like short mission time and limited sensor coverage, which in turn can be compensated by the intelligent assignment of vehicles with increased autonomy in cooperating groups.An important basis for efficient and effective autonomous reconnaissance, especially in swarming or teaming scenarios, is movement optimization considering the capabilities of the heterogeneous vehicles equipped with different sensor systems operating in a combined mission. For this purpose, algorithms have been developed at Fraunhofer IOSB that enable appropriate planning and dynamic processing in very different situations for heterogeneous groups of cooperating vehicles. In the following, two exploration methods for reconnaissance missions are described in more detail and their possibilities are discussed and compared.

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“…For multi-UAV path planning, some researchers [ 10 , 11 ] have proposed solutions for multi-UAV collision avoidance systems based on centralized algorithms. Centralized algorithms rely on a central server, which is used to communicate with each agent and generate global control commands based on the observations of all UAVs.…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV Path Planning in GPS and Communication Denial Environment

Wei

Wang

et al. 2023

Sensors

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This paper proposes a feature fusion algorithm for solving the path planning problem of multiple unmanned aerial vehicles (UAVs) using GPS and communication denial conditions. Due to the blockage of GPS and communication, UAVs cannot obtain the precise position of a target, which leads to the failure of path planning algorithms. This paper proposes a feature fusion proximal policy optimization (FF-PPO) algorithm based on deep reinforcement learning (DRL); the algorithm can fuse image recognition information with the original image, realizing the multi-UAV path planning algorithm without an accurate target location. In addition, the FF-PPO algorithm adopts an independent policy for multi-UAV communication denial environments, which enables the distributed control of UAVs such that multi-UAVs can realize the cooperative path planning task without communication. The success rate of our proposed algorithm can reach more than 90% in the multi-UAV cooperative path planning task. Finally, the feasibility of the algorithm is verified by simulations and hardware.

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Coverage Path Planning Methods Focusing on Energy Efficient and Cooperative Strategies for Unmanned Aerial Vehicles

Cited by 45 publications

References 81 publications

Bio-Inspired Optimization-Based Path Planning Algorithms in Unmanned Aerial Vehicles: A Survey

Bio-Inspired Optimization-Based Path Planning Algorithms in Unmanned Aerial Vehicles: A Survey

Area reconnaissance with heterogeneous groups of autonomous systems

Multi-UAV Path Planning in GPS and Communication Denial Environment

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