Path Planning and Control of a Quadrotor UAV Based on an Improved APF Using Parallel Search

Huang, Tianpeng; Huang, Deqing; Qin, Na; Li, Yanan

doi:10.1155/2021/5524841

Cited by 25 publications

(17 citation statements)

References 39 publications

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“…However, the conventional APF is affected by local minima, which causes UAVs to become stuck before they reach the target. In [ 82 ], an improved APF approach, IAPF, was used to design a collision-free UAV path. The proposed IAPF avoids the local minimum problem during path optimization.…”

Section: Bio-inspired Algorithms For Uav Path Planningmentioning

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: Bio-inspired Algorithms For Uav Path Planningmentioning

confidence: 99%

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

Poudel

Arafat

Moh

2023

Sensors

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“…Aiming at the generation and optimization of the search path, currently, the general research goal is to maximize the efficiency of task execution by effectively modeling the obstacle area [19,20], and then, the artificial potential field (APF) method [9], the improved RRT * algorithm [21][22][23][24], and heuristic algorithms have been employed to generate an optimal path while solving spatial conflicts. The Circular Arc Trajectory method was used to model the irregular obstacle area, which could simplify the computational complexity of the obstacle avoidance problem [19].…”

Section: Related Workmentioning

confidence: 99%

“…The parameter setting in the traditional TPM algorithm [7,8] is not comprehensive enough to make full use of the target motion state data, so it cannot predict the target trajectory well. In addition, the traditional APF algorithm [9] is commonly used to deal with the obstacle avoidance problem but is easy to fall into a local optimum under an uncertain environment.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Search Optimization of an Unknown Dynamic Target Based on the Modified TPM

Cheng

et al. 2022

International Journal of Aerospace Engineering

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Aiming to address the problem of unknown dynamic target trajectory prediction and search path optimization in unmanned aerial vehicle (UAV) swarm path planning, this paper proposes a target search algorithm based on a modified target probability map (TPM). First, using the TPM, the proposed algorithm generates a high-probability distribution region of a target with directionality to fit the target trajectory and realizes the trajectory prediction of an unknown dynamic target. Then, the distributed ant colony (ACO) algorithm and the artificial potential field (APF) algorithm are combined to generate and optimize the UAV swarm search result and return path with the goal of maximizing task execution efficiency. Finally, the Monte Carlo simulation method is used to analyze the effectiveness of the proposed algorithm, and the results are evaluated from five aspects, including the number of targets captured. The simulation results show that under the condition of an unknown dynamic target trajectory, the average target captured rate and average unknown region search rate of the MTPM method were higher than that of the traditional TPM method, and the performance was improved by 14.6% and 10.7%, respectively.

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“…Recently, different local path planning methods have been proposed. Huang et al (2021) proposed a parallel search algorithm to solve the problem of local minimum and unreachable target with nearby obstacles in artificial potential field method. Xu et al (2020) proposed an algorithm named max-speed aware velocity obstacle algorithm to avoid high-speed obstacles considering obstacle speeds larger than the robot’s maximum speed.…”

Section: Introductionmentioning

confidence: 99%

Multiple interaction strategies for mobile robots based on improved dynamic window approach in unknown dynamic environments

He,

Zhang,

Zhang

et al. 2023

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Purpose The purpose of this paper is to solve the problem that mobile robots are still based on reactive collision avoidance in unknown dynamic environments leading to a lack of interaction with obstacles and limiting the comprehensive performance of mobile robots. A dynamic window approach with multiple interaction strategies (DWA-MIS) is proposed to solve this problem. Design/methodology/approach The algorithm firstly classifies the moving obstacle movement intention, based on which a rule function is designed to incorporate positive incentives to motivate the robot to make correct avoidance actions. Then, the evaluation mechanism is improved by considering the time cost and future information of the environment to increase the motion states. Finally, the optimal objective function is designed based on genetic algorithm to adapt to different environments with time-varying multiparameter optimization. Findings Faced with obstacles in different states, the mobile robot can choose a suitable interaction strategy, which solves the limitations of the original DWA evaluation function and avoids the defects of reactive collision avoidance. Simulation results show that the algorithm can efficiently adapt to unknown dynamic environments, has less path length and iterations and has a high comprehensive performance. Originality/value A DWA-MIS is proposed, which increases the interaction capability between mobile robots and obstacles by improving the evaluation function mechanism and broadens the navigation strategy of DWA at a lower computational cost. After real machine verification, the algorithm has a high comprehensive performance based on real environment and provides a new idea for local path planning methods.

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Path Planning and Control of a Quadrotor UAV Based on an Improved APF Using Parallel Search

Cited by 25 publications

References 39 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

Cooperative Search Optimization of an Unknown Dynamic Target Based on the Modified TPM

Multiple interaction strategies for mobile robots based on improved dynamic window approach in unknown dynamic environments

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