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

Abstract

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.

Hybrid algorithms in path planning for autonomous navigation of unmanned aerial vehicle: a comprehensive review

“…The advantages of the methods combining smart algorithms are mainly related to cost, execution time, and convergence speed. Finding the best path quickly with minimum cost [43,47,57], consuming little energy [46,55,60,67], having fast convergence speed [44-46, 48, 50, 55, 59, 66, 67], short execution time [3,45,63,65], simple [49,53,54] are the main strengths of this type of algorithm. In addition, rapidly respond to pop-up obstacles [3], least number of UAVs [52], able to operate in real-time and work in complex environments with large number of obstacles [61], and strong adaptability to the environments [67] are also considered the special advantages of some algorithms such as APPATT, IPSO-GA, GA-LRO, MACO-MEA * .…”

Research on a Multi-Strategy Improved Sand Cat Swarm Optimization Algorithm for Three-Dimensional UAV Trajectory Path Planning

“…In general, the probability distribution model selects some dominant individuals from the current population for evaluation in each iteration, and then samples to generate a new population based on the constructed Gaussian probability distribution model, which serves as the sample for the next distribution model. The distribution estimation learning strategy effectively balances exploration and exploitation while enhancing the algorithm's performance [35].…”

Single-drone energy efficient coverage path planning with multiple charging stations for surveillance

“…Although this study is like our work, one minimized total time spent during the route rather than total energy consumption. In addition to 2-dimensional studies, there are also instances of 3-dimensional (3D) CPP articles [26][27]. Bircher et al [26] developed the routing optimization model and mostly focused on 3D structure inspections; while Balasubramanian et al [27] determined the optimum route by considering different 3D static obstacles.…”

“…In addition to 2-dimensional studies, there are also instances of 3-dimensional (3D) CPP articles [26][27]. Bircher et al [26] developed the routing optimization model and mostly focused on 3D structure inspections; while Balasubramanian et al [27] determined the optimum route by considering different 3D static obstacles. Besides, there are some past studies that focused on energy-efficient CPP which is the main topic of this study.…”

“…Besides, there are some past studies that focused on energy-efficient CPP which is the main topic of this study. Balasubramanian et al [27] used the ant colony optimization model to calculate the 3-dimensional energy-efficient route. Vasquez-Gomez et al [28] developed an efficient route planning algorithm for the coverage of the convex regions of the drones, based on different starting and end points, but the algorithm did not guarantee optimality.…”