An improved fuzzy‐controlled local path planning algorithm based on dynamic window approach DOI
Aizun Liu, Chong Liu, Lei Li

et al.

Journal of Field Robotics, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 23, 2024

Abstract With the increasingly complex operating environment of mobile robots, intelligent requirements robots are getting higher and higher. Navigation technology is core robot research, path planning an important function navigation. Dynamic window approach (DWA) one most popular local algorithms nowadays. However, there also some problems. DWA algorithm easy to fall into optimal solution without guidance global path. The traditional use key nodes as temporary target points. guiding ability points will be weakened in cases, which still leads solutions such being trapped by a “C”‐shaped obstacle or go around outside dense area. In environment, if deviates too far from path, serious consequences may caused. Therefore, we proposed trajectory similarity evaluation based on dynamic time warping method provide better guidance. other problem poor adaptability environments due fixed weights. And, designed fuzzy controller improve environments. Experiment results show that reduces execution 0.7% mileage 2.1%, 10.8% improves average distance between obstacles at path's danger 50%, simulated terrain finishing rate experiments 25%.

Language: Английский

Improved Grey Wolf Algorithm: A Method for UAV Path Planning DOI Creative Commons
Xingyu Zhou, Guoqing Shi, Jiandong Zhang

et al.

Drones, Journal Year: 2024, Volume and Issue: 8(11), P. 675 - 675

Published: Nov. 14, 2024

The Grey Wolf Optimizer (GWO) algorithm is recognized for its simplicity and ease of implementation, has become a preferred method solving global optimization problems due to adaptability search capabilities. Despite these advantages, existing Unmanned Aerial Vehicle (UAV) path planning algorithms are often hindered by slow convergence rates, susceptibility local optima, limited robustness. To surpass limitations, we enhance the application GWO in UAV improving trajectory evaluation function, factor, position update method. We propose collaborative model that includes constraint analysis an function. Subsequently, Enhanced (NI–GWO) introduced, which optimizes coefficient using nonlinear function integrates Dynamic Window Approach (DWA) into based on fitness individual wolves, enabling it perform dynamic obstacle avoidance tasks. In final stage, simulation platform employed evaluate compare effectiveness original improved algorithms. Simulation results demonstrate proposed NI–GWO can effectively solve problem UAVs uncertain environments. Compared Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), GWO, MP–GWO algorithms, achieve optimal value significant advantages terms average length, time, number collisions,

Language: Английский

Citations

7
Dynamic path planning of UAV with least inflection point based on adaptive neighborhood A* algorithm and multi-strategy fusion DOI Creative Commons
Longyan Xu, Mao Xi, Ren Gao

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 12, 2025

Planning a safe and efficient global path in complex three-dimensional environment is challenging optimization task. Existing planning algorithms are faced with problems such as lengthy path, too many inflection points insufficient dynamic obstacle avoidance performance. In order to solve these challenges, this paper proposes algorithm (MSF-MTPO) multi-strategy fusion achieve the least point optimization. Initially, an adaptive extended neighborhood A* designed, which dynamically adjusts extension range according distribution of obstacles around current location, selecting optimal travel direction step size each time reduce redundant paths unnecessary nodes. Then, combined two-way search mechanism, starting from original end point, opposite node searched target respectively, so number nodes time. further improve efficiency, trajectory correction method designed eliminate on premise ensuring safety. Fourthly, problem deviation or excessive softening caused by limited control existing smoothing methods, local tangent circle proposed, effectively improves smoothness basis retaining superiority path. Finally, used guiding artificial potential field avoid falling into optimum realize avoidance. addition, performance compared several advanced different environments, MSF-MTPO has lowest cost scenarios, proves effectiveness stability UAV 3D planning.

Language: Английский

Citations

0
Research on the A* Algorithm Based on Adaptive Weights and Heuristic Reward Values DOI Creative Commons
Xizheng Wang, Gang Li, Zijian Bian

et al.

World Electric Vehicle Journal, Journal Year: 2025, Volume and Issue: 16(3), P. 144 - 144

Published: March 4, 2025

Aiming at the problems of A* algorithm’s long running time, large number search nodes, tortuous paths, and planned paths being prone to colliding with corner points obstacles, adaptive weighting reward value theory are proposed improve it. Firstly, diagonal-free five-way based on coordinate changes is used make algorithm purposeful. Meanwhile, in order path security, diagonal filtered out when there obstacles neighborhood. Secondly, a radial basis function act as coefficient heuristic adjust proportion functions accordingly distance. Again, optimize cost using provided by target point so that current away from local optimum. Finally, secondary optimization performed increase distance between barriers, optimized smoothed Bessel curves. Typical working conditions selected, verified through simulation tests. Simulation tests show not only shortens planning time improves security but also reduces nodes about 76.4% average turn angle 71.7% average.

Language: Английский

Citations

0
Path planning for mobile robots in complex environments based on enhanced sparrow search algorithm and dynamic window approach DOI
Yixuan Luo, Shusen Lin, Yifan Wang

et al.

Robotica, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 24

Published: May 16, 2025

Abstract Traditional path planning algorithms often encounter challenges in complex dynamic environments, including local optima, excessive lengths, and inadequate obstacle avoidance. Thus, the development of innovative is essential. This article addresses mobile robot where traditional methods converge to leading suboptimal struggle with To overcome these limitations, we propose an integrated algorithm, enhanced sparrow search algorithm combined window approach (ESSA-DWA). The first utilizes ESSA for global planning, followed by facilitated DWA. Specifically, incorporates Tent chaotic initialization enhance population diversity, effectively mitigating risk premature convergence optima. Moreover, adjustments inertia weight during process enable adaptive balance between exploration exploitation. integration a strategy further refines individual updates, thereby improving performance. smoothness, Floyd employed optimization, ensuring more continuous trajectory. Finally, combination DWA uses key nodes from generated as reference points ensures that closely follows while also enabling real-time detection effectiveness has been validated through both simulations practical experiments, offering efficient viable solution problem.

Language: Английский

Citations

0
Path planning strategies for logistics robots: Integrating enhanced A‐star algorithm and DWA DOI Creative Commons

Xianyang Zeng,

Jiawang Zhang,

Wenhui Yin

et al.

Electronics Letters, Journal Year: 2024, Volume and Issue: 60(22)

Published: Nov. 1, 2024

Abstract Path planning is the key part in process of transportation conducted by logistics robots, and there often exist some problems with it. The path designed not always smooth enough its search efficiency low, for example. As a common global algorithm, A‐star based on traditional which unable to solve problem uneven movement robots. Through improving heuristic function weighing dynamically, removing redundant points star algorithm Floyd setting safe distance prevent robot from collision at same time, finally curved be more appropriate robot. MATLAB simulation before after improvement shows that turning advanced reduced 61.5% average compared algorithm. length decreased 2.4% traversing 58.5%. At DWA introduces dynamic weight coefficients, can dynamically adjust coefficients when encountering obstacles, so as safely reach target point.

Language: Английский

Citations

1
A multi-robot conflict elimination path planning approach for dynamic environments DOI

Yang Liu,

Mengru Yang,

Annan Wang

et al.

Measurement Science and Technology, Journal Year: 2024, Volume and Issue: 36(1), P. 016340 - 016340

Published: Dec. 20, 2024

Abstract Path planning plays a crucial role in multi-robot systems, and its effectiveness directly impacts the system’s performance. A conflict-elimination path method (CEPP) for dynamic environments is proposed. The fuses adaptive dynamic-window algorithm (ADWA) with Repulsive function-based optimized A* (R–A*) to deal (MRPP) introduces safe area radius priority strategy solve collision conflict problem. Among them, ADWA first adds time cost target point distance evaluation function original weights accelerate efficiency of robot finding point. Then detection waiting mechanism introduced problem that cannot find endpoint. Finally, CEPP MRPP verified by simulation. Meanwhile, compared analyzed traditional fusion (A*-DWA), simulation results show average running length this are better than A*-DWA algorithm.

Language: Английский

Citations

1
Trajectory planning for AGV based on the improved artificial potential field- A* algorithm DOI
Wei Liu, Linfeng Chen,

Rongjun Wang

et al.

Measurement Science and Technology, Journal Year: 2024, Volume and Issue: 35(9), P. 096312 - 096312

Published: June 11, 2024

Abstract There are many redundant nodes and inflection points in the path planned by traditional A* algorithm, leading to inefficient trajectory planning of automatic guided vehicle (AGV) multi-static obstacles environment. The artificial potential field (APF) algorithm suffers from problem unreachable objectives falling into optimal local value. This article studies optimization AGVs improve algorithm’s iteration efficiency shorten trajectory’s total length. establishes forward kinematic unified robot description format model AGV proposes APF-A* for planning. search cost number turns effectively optimized. simulates results compared with before optimization, optimized time is 60% less than that optimization. experimental platform built, verification experiment carried out. show studied this achieves smoothing length

Language: Английский

Citations

0
Multi-Robot Cooperative Navigation in Dynamic Environments using Deep Reinforcement Learning in ROS DOI
Shuangshuang Wu,

Jianchuang Wu,

Wenbai Chen

et al.

Published: Aug. 9, 2024

Language: Английский

Citations

0
An improved fuzzy‐controlled local path planning algorithm based on dynamic window approach DOI
Aizun Liu, Chong Liu, Lei Li

et al.

Journal of Field Robotics, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 23, 2024

Abstract With the increasingly complex operating environment of mobile robots, intelligent requirements robots are getting higher and higher. Navigation technology is core robot research, path planning an important function navigation. Dynamic window approach (DWA) one most popular local algorithms nowadays. However, there also some problems. DWA algorithm easy to fall into optimal solution without guidance global path. The traditional use key nodes as temporary target points. guiding ability points will be weakened in cases, which still leads solutions such being trapped by a “C”‐shaped obstacle or go around outside dense area. In environment, if deviates too far from path, serious consequences may caused. Therefore, we proposed trajectory similarity evaluation based on dynamic time warping method provide better guidance. other problem poor adaptability environments due fixed weights. And, designed fuzzy controller improve environments. Experiment results show that reduces execution 0.7% mileage 2.1%, 10.8% improves average distance between obstacles at path's danger 50%, simulated terrain finishing rate experiments 25%.

Language: Английский

Citations

0