Optimal Path Planning Based on Hybrid Genetic-Cuckoo Search Algorithm

Liu

2023

IEEE Access

Space robots have a broad application prospect in the aerospace industry. It is difficult for space robots to keep running for a long time due to limited fuel. In addition, it is impossible to replenish the fuel for space robots at any time due to the unique working environment. Therefore, a proper path is crucial for the effective operation of the space robot. In this paper, we investigate the allocation of exploration tasks and the path planning of space robots jointly. The goal is to minimize the completion latency of exploration tasks. We propose two algorithms named subbranch insertion task allocation (SI-TA) and parallel search task allocation (PS-TA) to solve the problem. We also customize an algorithm named random path planning task allocation (RTA) as the baseline. At last, we implement extensive experiments to demonstrate that proposed algorithms can obtain lower completion latency than RTA. Compared with RTA, the proposed algorithms SI-TA and PS-TA can reduce completion latency by at least 20% and 40%, respectively. Moreover, both algorithms work more stably than RTA.

Section: A Simulation Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Joint Task Allocation and Path Planning for Space Robot

Liu

2023

IEEE Access

“…The authors in [17] integrated a genetic algorithm and simulated an annealing algorithm to generate a path with the capability of obstacle avoidance. In a 3D environment, a hybrid genetic-cuckoo search algorithm was designed in [18] to generate an optimal path and can alleviate the conflict between optimality and delay.…”

Section: Related Workmentioning

confidence: 99%

“…The bionic has been widely adopted to solve complex optimization since its appearance. Researchers have proposed different biological-based solutions to tackle path planning for space robots [14,15,16,17,18,19]. The above works mainly study the single space robot path planning.…”

Section: Introductionmentioning

confidence: 99%

Joint Task Allocation and Path Planning for Space Robot

Zhang

et al. 2022

Preprint

Space robots have a wide application prospect in the aerospace industry. Due to the limited fuel for the space robots, which cannot support the space robots to run for a long time. In addition, the special working environment makes it impossible to replenish the fuel for the robot at any time. Therefore, a proper path is crucial for the operation of space robots. In order to ensure the operation of space robots, we optimize the allocation of exploration tasks and the selection of space robot paths, jointly. We propose two combination algorithms named Parallel Search and Task Allocation (PS-TA) and Subbranch Insertion and Task Allocation (SI-TA) to optimize the path and the task allocation , intend to obtain the minimum completion latency. We also construct Random Path Planning and Task Allocation (RTA) as the baseline. At last, we provide extensive experiments to demonstrate that proposed algorithms can obtain lower completion latency compared with RTA. Furthermore, SI-TA is more energy-efficient than PS-TA.

“…Traditional algorithms include Dijkstra algorithm and A * algorithm. Intelligent bionic algorithms include ant colony algorithm [1][2][3][4][5], genetic algorithm [6][7][8][9], and simulated annealing algorithm. From the development of traditional algorithm and intelligent bionic algorithm to their combination, it reflects the development of path planning technology.…”

Section: Introductionmentioning

confidence: 99%

Research on Operation and Maintenance Route Planning of Offshore Wind Farm Based on Multiagent

Sheng

Wireless Communications and Mobile Computing

et al. 2022

Recently, the ant colony algorithm has been widely used in the field of path planning, which is a key technology required for ship operations in offshore wind farms to improve navigation efficiency and power generation. However, the ant colony algorithm has the defects of a long search time and stagnation in the early stage of the operation and maintenance path planning of offshore wind farms, and it easily falls into the problem of local optima; furthermore, the ant colony algorithm uses incremental construction to build a complete itinerary path, it takes a lot of time to search the path, and only the suboptimal solution is obtained. To address the above problems, in this paper, we propose a multi-agent-based operation and maintenance model for offshore wind farms. Specifically, the introduction of the heuristic factor can optimize the local optimal solution and make the ant colony algorithm clearer when searching for the target. Based on this, increasing the pheromone adjustment factor can eliminate the invalid path from searching and select a high-quality path. In addition, by integrating the genetic algorithm, it is possible to select, cross, and mutate to simulate the natural evolution process to search for the optimal solution, reduce the similarity of the paths constructed by the ant colony, reduce the probability of algorithm stagnation, improve the convergence speed, and improve the time efficiency and solution accuracy of the algorithm. Simulation experiments on a series of benchmark datasets show that the proposed GA-PACO algorithm achieves better performance in global search and path planning than the existing three algorithms.