A survey on the application of path-planning algorithms for multi-rotor UAVs in precision agriculture

Basiri, Amin; Mariani, Valerio; Silano, Giuseppe; Aatif, Muhammad; Iannelli, Luigi; Glielmo, Luigi

doi:10.1017/s0373463321000825

Cited by 49 publications

(31 citation statements)

References 95 publications

(123 reference statements)

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“…The recent survey on various UAV path planning and collision avoidance algorithms can be seen in (Basiri et al 2022 ; Khan et al 2021a , b ). In order to solve the high dimensional problems with good accuracy and avoid sudden changes in its path for UAV applications, the aforementioned algorithm may not be an efficient choice.…”

Section: State-of-the Artmentioning

confidence: 99%

Optimal energy efficient path planning of UAV using hybrid MACO-MEA* algorithm: theoretical and experimental approach

Esakki

Elangovan

Tamilarasan

et al. 2022

J Ambient Intell Human Comput

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Autonomous mission capabilities with optimal path are stringent requirements for Unmanned Aerial Vehicle (UAV) navigation in diverse applications. The proposed research framework is to identify an energy-efficient optimal path to achieve the designated missions for the navigation of UAVs in various constrained and denser obstacle prone regions. Hence, the present work is aimed to develop an optimal energy-efficient path planning algorithm through combining well known modified ant colony optimization algorithm (MACO) and a variant of A*, namely the memory-efficient A* algorithm (MEA*) for avoiding the obstacles in three dimensional (3D) environment and arrive at an optimal path with minimal energy consumption. The novelty of the proposed method relies on integrating the above two efficient algorithms to optimize the UAV path planning task. The basic design of this study is, that by utilizing an improved version of the pheromone strategy in MACO, the local trap and premature convergence are minimized, and also an optimal path is found by means of reward and penalty mechanism. The sole notion of integrating the MEA* algorithm arises from the fact that it is essential to overcome the stringent memory requirement of conventional A* algorithm and to resolve the issue of tracking only the edges of the grids. Combining the competencies of MACO and MEA*, a hybrid algorithm is proposed to avoid obstacles and find an efficient path. Simulation studies are performed by varying the number of obstacles in a 3D domain. The real-time flight trials are conducted experimentally using a UAV by implementing the attained optimal path. A comparison of the total energy consumption of UAV with theoretical analysis is accomplished. The significant finding of this study is that, the MACO-MEA* algorithm achieved 21% less energy consumption and 55% shorter execution time than the MACO-A*. moreover, the path traversed in both simulation and experimental methods is 99% coherent with each other. it confirms that the developed hybrid MACO-MEA* energy-efficient algorithm is a viable solution for UAV navigation in 3D obstacles prone regions.

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Section: State-of-the Artmentioning

confidence: 99%

Optimal energy efficient path planning of UAV using hybrid MACO-MEA* algorithm: theoretical and experimental approach

Esakki

Elangovan

Tamilarasan

et al. 2022

J Ambient Intell Human Comput

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“…The problem is modeled as a special orienteering problem (OP), namely mixed-integer semidefinite programming (MISDP), solving by a heuristic algorithm. In addition, the literature [26] thoroughly surveys the various path planning techniques for UAVs in different applications, while the literature [27] focuses on the path planning approaches for multi-rotor UAVs in precision agriculture applications. However, the above-mentioned studies are only a single path planning, while without considering the simultaneous optimization of path planning and related tasks.…”

Section: Uav-enabled Techniquesmentioning

confidence: 99%

Energy-Sensitive Trajectory Design and Restoration Areas Allocation for UAV-Enabled Grassland Restoration

Jiao¹,

Wang²,

Yang³

et al. 2022

Preprint

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Grassland restoration is a critical means to safeguard grassland ecological degradation. This work considers the maximization restoration areas problem for UAV-enabled grassland restoration method in a restoration process, which is a precise restoration scheme. We first formulate the problem as a multivariable combinatorial optimization problem. By analyzing the characteristics of the optimization problem, it can be decomposed into the two stages: UAV trajectory design and restoration areas allocation. On this basis, the problem can be regarded as a composite problem of traveling salesman problem (TSP) and multidimensional knapsack problem (MKP). Unlike the single combinatorial optimization problem, the coupling relationship between them makes the problem difficult to directly solve by employing the single stage traditional methods. To effectively solve without ignoring the dependence between the two stages, we develop a cooperative optimization algorithm based on heuristic algorithm and population-based incremental learning (PBIL) incorporated with a maximum-residual-energy-based local search (MRELS) strategy, called CHAPBILM, to deal with this problem under constraint conditions of the UAV energy, the total seeds weight, and the number of restored areas. The simulation studies demonstrate that the proposed cooperative optimization method is effective for UAV-enabled grassland restoration problem and can significantly outperform the noncooperative optimization methods, which also verifies the dependency relationship between UAV trajectory design and restoration areas allocation.

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“…A guaranteed network recovery from partitioning requires accurate information about the current network state. The proposed network recovery techniques in the literature can be improved substantially by employing the unmanned and autonomous aerial sensing (UAS) mechanism 14,15 . The extensive advantages of unmanned aerial vehicles (UAVs) can be exploited to deal with network partition recovery problems.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed network recovery techniques in the literature can be improved substantially by employing the unmanned and autonomous aerial sensing (UAS) mechanism. 14,15 The extensive advantages of unmanned aerial vehicles (UAVs) can be exploited to deal with network partition recovery problems. The controlled mobility, adaptive altitude, line-ofsight communication links, and obstacle avoidance systems in UAVs [16][17][18] can be utilized to get the exact information about the partitioned network.…”

mentioning

confidence: 99%

Partition detection and recovery by UAVs in damaged WSANs using N‐angle clustering

Zear

Ranga

Bhushan

2022

Int J Communication

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Connectivity is essential in wireless sensor and actor networks (WSANs) deployed for data collection and monitoring applications. WSANs serving in inhospitable environments are prone to damage, and sometimes, the concurrent multiple node failures in the same area may cause network partitioning. The autonomous network recovery in harsh environments is challenging because the nodes in different partitions cannot determine the scope of the damage. This paper proposes an approach called partition detection and recovery by unmanned aerial vehicles (UAVs) in damaged WSANs using N-angle clustering (N-anglePDRU), which consists of all the phases required to accumulate information about the network partitions and reconnect them successfully. The algorithm is evaluated for different randomly generated network topologies according to various state-of-the-art parameters such as detection and recovery time, messages transmitted, relay node count, and distance traveled by UAVs. The proposed approach is also compared with the existing algorithms, and the obtained results demonstrate that N-anglePDRU recovers the partitioned network in less recovery time with minimum relay nodes and distance traveled.

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A survey on the application of path-planning algorithms for multi-rotor UAVs in precision agriculture

Cited by 49 publications

References 95 publications

Optimal energy efficient path planning of UAV using hybrid MACO-MEA* algorithm: theoretical and experimental approach

Optimal energy efficient path planning of UAV using hybrid MACO-MEA* algorithm: theoretical and experimental approach

Energy-Sensitive Trajectory Design and Restoration Areas Allocation for UAV-Enabled Grassland Restoration

Partition detection and recovery by UAVs in damaged WSANs using N‐angle clustering

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