A review on graph search algorithms for optimal energy efficient path planning for an unmanned air vehicle

Debnath, Sanjoy; Omar, Rosli; Latip, Nor Badariyah Abdul; Shely, Shasha; Nadira, Elia; Melor, Che Ku Nor Che Ku; Chakraborty, Tapan Kumar; Natarajan, Elango

doi:10.11591/ijeecs.v15.i2.pp743-749

Cited by 31 publications

(12 citation statements)

References 18 publications

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“…Recently, the works in [ 56 , 57 ] utilized deep reinforcement learning techniques, such as Q-learning, as a promising solution to solve the problem of real-time drone path planning in unknown dynamic environments. Alternatively, heuristics intelligent optimization algorithms have also been widely used in recent years to solve the local path planning optimization problems, such as graph-based algorithms [ 58 ], heuristic search algorithms [ 59 ], field-based algorithms [ 60 ], and intelligent optimization algorithms [ 61 ].…”

Section: Related Workmentioning

confidence: 99%

LoRa Communications as an Enabler for Internet of Drones towards Large-Scale Livestock Monitoring in Rural Farms

Behjati

Noh

Alobaidy

et al. 2021

Sensors

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Currently, smart farming is considered an effective solution to enhance the productivity of farms; thereby, it has recently received broad interest from service providers to offer a wide range of applications, from pest identification to asset monitoring. Although the emergence of digital technologies, such as the Internet of Things (IoT) and low-power wide-area networks (LPWANs), has led to significant advances in the smart farming industry, farming operations still need more efficient solutions. On the other hand, the utilization of unmanned aerial vehicles (UAVs), also known as drones, is growing rapidly across many civil application domains. This paper aims to develop a farm monitoring system that incorporates UAV, LPWAN, and IoT technologies to transform the current farm management approach and aid farmers in obtaining actionable data from their farm operations. In this regard, an IoT-based water quality monitoring system was developed because water is an essential aspect in livestock development. Then, based on the Long-Range Wide-Area Network (LoRaWAN®) technology, a multi-channel LoRaWAN® gateway was developed and integrated into a vertical takeoff and landing drone to convey collected data from the sensors to the cloud for further analysis. In addition, to develop LoRaWAN®-based aerial communication, a series of measurements and simulations were performed under different configurations and scenarios. Finally, to enhance the efficiency of aerial-based data collection, the UAV path planning was optimized. Measurement results showed that the maximum achievable LoRa coverage when operating on-air via the drone is about 10 km, and the Longley–Rice irregular terrain model provides the most suitable path loss model for the scenario of large-scale farms, and a multi-channel gateway with a spreading factor of 12 provides the most reliable communication link at a high drone speed (up to 95 km/h). Simulation results showed that the developed system can overcome the coverage limitation of LoRaWAN® and it can establish a reliable communication link over large-scale wireless sensor networks. In addition, it was shown that by optimizing flight paths, aerial data collection could be performed in a much shorter time than industrial mission planning (up to four times in our case).

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Section: Related Workmentioning

confidence: 99%

LoRa Communications as an Enabler for Internet of Drones towards Large-Scale Livestock Monitoring in Rural Farms

Behjati

Noh

Alobaidy

et al. 2021

Sensors

View full text Add to dashboard Cite

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“…Relevant scholars generate path points, construct a connected graph in free space, and convert the solution space of the path planning problem into a topological space, so that the complexity of the problem is independent of the complexity of the environment and the dimension of the planning space [18]. The disadvantage of this method is that when the obstacles in the planning space are dense or there are narrow passages, the efficiency of the method will become low [19,20]. In addition, because this method randomly samples the signpost nodes when constructing the connected graph, it is easy to cause the final search path to deviate from the optimal.…”

Section: Introductionmentioning

confidence: 99%

Distributed Path Planning of Unmanned Aerial Vehicle Communication Chain Based on Dual Decomposition

Wei

2021

Wireless Communications and Mobile Computing

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Limited by the insufficiency of single UAV’s load and flight time capabilities, the multi-UAV (unmanned aerial vehicle) collaboration to improve mission efficiency and expand mission functions has become the focus of current UAV theory and application research. In this paper, the research on UAV global path planning is carried out using the ant colony algorithm, and an indoor UAV path planning model based on the ant colony algorithm is constructed. In order to improve the efficiency of the algorithm, enhance the adaptability and robustness of the algorithm, a distributed path planning algorithm based on the dual decomposition UAV communication chain is proposed. This algorithm improves the basic ant colony algorithm from the aspects of path selection, pheromone update, and rollback strategy in view of the inherent shortcomings of the ant colony algorithm. In order to achieve the best performance of the algorithm, this paper analyzes each parameter in the ant colony algorithm in depth and obtains the optimal combination of parameters. The construction method of the Voronoi diagram was improved, and the method was simulated to verify that the method can obtain a Voronoi diagram path that is safer than the original method under certain time conditions. Through the principle analysis and simulation verification of the Dijkstra algorithm and the dual decomposition ant colony algorithm, it is concluded that the dual decomposition ant colony algorithm is more efficient in pathfinding. Finally, through simulation, it was verified that the dual decomposition ant colony algorithm can plan a safe and reasonable flight path for multiple UAV formation flights in an offline state and achieve offline global obstacle avoidance for multiple UAVs.

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“…Since many robots work in crowded and cluttered environments, the obstacle avoidance problem, in general, is not straightforward to solve [ 3 ]. Most of the traditional path planning algorithms can be classified into several categories: graph search methods [ 4 ], sampling-based method [ 5 ], and intelligent bionic methods [ 6 ]. The most widely used graph search algorithm is the A* search method [ 7 ], which was developed by Hart in 1968.…”

Section: Introductionmentioning

confidence: 99%

Improved Distorted Configuration Space Path Planning and Its Application to Robot Manipulators

Xie

Zhou

Yang

2020

Sensors

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Real-time obstacle avoidance path planning is critically important for a robot when it operates in a crowded or cluttered workspace. At the same time, the computational cost is a big concern once the degree of freedom (DOF) of a robot is high. A novel path planning strategy, the distorted configuration space (DC-space) method, was proposed and proven to outperform the traditional search-based methods in terms of computational efficiency. However, the original DC-space method did not sufficiently consider the demands on automatic planning, convex space preservation, and path optimization, which makes it not practical when applied to the path planning for robot manipulators. The treatments for the problems mentioned above are proposed in this paper, and their applicability is examined on a three DOFs robot. The experiments demonstrate the effectiveness of the proposed improved distorted configuration space (IDCS) method on rapidly finding an obstacle-free path. Besides, the optimized IDCS method is presented to shorten the generated path. The performance of the above algorithms is compared with the classic Rapidly-exploring Random Tree (RRT) searching method in terms of their computation time and path length.

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A review on graph search algorithms for optimal energy efficient path planning for an unmanned air vehicle

Cited by 31 publications

References 18 publications

LoRa Communications as an Enabler for Internet of Drones towards Large-Scale Livestock Monitoring in Rural Farms

LoRa Communications as an Enabler for Internet of Drones towards Large-Scale Livestock Monitoring in Rural Farms

Distributed Path Planning of Unmanned Aerial Vehicle Communication Chain Based on Dual Decomposition

Improved Distorted Configuration Space Path Planning and Its Application to Robot Manipulators

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