A Mobile Robot Path Planning Method Based on Dynamic Multipopulation Particle Swarm Optimization DOI Creative Commons
Yunjie Zhang, Ning Li,

Yadong Chen

et al.

Journal of Robotics, Journal Year: 2024, Volume and Issue: 2024(1)

Published: Jan. 1, 2024

To overcome the limitations of particle swarm optimization (PSO) in mobile robot path planning, including issues such as premature convergence and sensitivity to local optima, this study proposes a novel approach, dynamic multipopulation (DMPSO). First, (MPSO) framework is extended by introducing strategy that adjusts number subpopulations real‐time. This designed enhance algorithm’s search capabilities accelerate its convergence. Second, inertia weights learning factors within algorithm are refined achieve balance between global exploration exploitation. Furthermore, an initialization based on fitness variance developed improve population diversity, mitigate convergence, ability locate optima. Lastly, positive feedback acceleration factor introduced optimize positions, thereby improving accelerating Simulation experiments have validated DMPSO offers improved capabilities, enhanced precision, more rapid rate. In comparison PSO, reduces length 3% decreases iterations 17%.

Language: Английский

Development of Metaheuristic Algorithms for Efficient Path Planning of Autonomous Mobile Robots in Indoor Environments DOI Creative Commons

Nattapong Promkaew,

Sippawit Thammawiset,

Phiranat Srisan

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 22, P. 102280 - 102280

Published: May 21, 2024

Application of efficient path planning algorithms for Autonomous Mobile Robots (AMRs) in environments with obstacles is a significant challenge robotics research. Existing methods, such as A-star (A*) algorithm, can provide optimal paths but suffer from high computational complexity and may not be suitable dynamic environments. This study explores the potential three metaheuristic - Improved Particle Swarm Optimization (IPSO), Grey Wolf Optimizer (IGWO), Artificial Bee Colony (ABC) algorithm – high-speed smooth paths. These are selected due to their ability find near-optimal solutions efficiently, avoid local optima, adapt changing In this study, researchers designed built an AMR using Raspberry Pi 4 microcontroller main processing unit, working conjunction Arduino Mega controlling DC motor drive through MDD10A driver circuit. The robot equipped RPLiDAR A1 sensor read 360-degree distance values mapping obstacle avoidance. experimental results clearly indicate that algorithms, especially ABC, calculate up 19% shorter than A* while requiring only one-tenth time. Moreover, ABC demonstrates superior motion smoothness when applied actual robot, enabling it better rapidly work represents step developing robots ready support real-world operations industries, logistics, healthcare, or various service sectors, helping increase efficiency reduce operating costs future.

Language: Английский

Citations

13
Progress in Construction Robot Path-Planning Algorithms: Review DOI Creative Commons
Shichen Fu, Detao Yang, Mei Zhou

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(3), P. 1165 - 1165

Published: Jan. 24, 2025

Construction robots are increasingly becoming a significant force in the digital transformation and intelligent upgrading of construction industry. Path planning is crucial for advancement building robot technology. Based on understanding site information, this paper categorizes path-planning algorithms into two types: global local path-planning. Local path further divided classical algorithms, reinforcement learning algorithms. Using classification framework, summarizes latest research developments analyzes advantages disadvantages various introduces several optimization strategies, presents results these optimizations. Furthermore, common environmental modeling methods, quality evaluation criteria, commonly used sensors robots, future development technologies swarm-based also discussed. Finally, explores trends field. The aim to provide references related research, enhance capabilities promote

Language: Английский

Citations

1
Two-layer path planning framework for WMRs in dynamic environments: Optimized ant colony algorithm and dynamic window approach DOI Creative Commons
Hongshuo Liu, Ming Yue,

Minghao Liu

et al.

Transactions of the Institute of Measurement and Control, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 8, 2025

This paper proposes a two-layer path planning method for wheeled mobile robots (WMRs), where an improved ant colony optimization (ACO) and optimized dynamic window approach (DWA) algorithms are used, at the global local layer, respectively. allows WMRs to plan high-quality under complex scenarios, while costing less traveling time energy consumption. At level of planning, modified ACO algorithm is presented which incorporates duplicate counter, new heuristic function smoothing operation enhance feasibility robustness planning. based on DWA, composed by evaluation obstacle avoidance sub-function proposed save cost, enhancing ability avoid moving obstacles. study aims efficiency effectiveness using combination DWA algorithms, such that can be applied multi-obstacle environment execute objects avoidance. Finally, multi-blockage involved with obstacles simulated verify method.

Language: Английский

Citations

0
Application of improved sparrow search algorithm and dynamic window method in mobile robot path planning and real‐time obstacle avoidance DOI Creative Commons

Leyang Shen,

Muhammad Khairi Faiz

The Journal of Engineering, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

Abstract In complex dynamic environments, robot path planning faces challenges in multi‐objective optimization, such as length, smoothness and obstacle avoidance capability. To address this, this paper proposes an improved sparrow search algorithm based on chaotic initialization the golden positive cosine strategy for planning. Diverse initial populations are generated through mapping to enhance global capability avoid falling into local optima. The optimizes individual position updates accelerate convergence ensure smoothness. Results demonstrate that proposed method outperforms (SSA), with improvements of 21.1% 16.3% 14.2% After achieving window approach (IDWA) is employed real‐time dynamically adjusting size speed, density target distance adaptively expand or shrink space, thereby improving flexibility efficiency. Simulation results show surpasses SSA terms smoothness, computational efficiency both static environments.

Language: Английский

Citations

0
“Reinforcement learning particle swarm optimization based trajectory planning of autonomous ground vehicle using 2D LiDAR point cloud” DOI

Ambuj,

Harsh Nagar, Ayan Paul

et al.

Robotics and Autonomous Systems, Journal Year: 2024, Volume and Issue: 178, P. 104723 - 104723

Published: May 21, 2024

Language: Английский

Citations

3
Optimization Algorithms and Their Applications and Prospects in Manufacturing Engineering DOI Open Access

Juan Song,

Bangfu Wang,

Xiaohong Hao

et al.

Materials, Journal Year: 2024, Volume and Issue: 17(16), P. 4093 - 4093

Published: Aug. 17, 2024

In modern manufacturing, optimization algorithms have become a key tool for improving the efficiency and quality of machining technology. As computing technology advances artificial intelligence evolves, these are assuming an increasingly vital role in parameter processes. Currently, development response surface method, genetic algorithm, Taguchi particle swarm algorithm is relatively mature, their applications process quite extensive. They used as objectives roughness, subsurface damage, cutting forces, mechanical properties, both special machining. This article provides systematic review application developmental trends within realm practical engineering production. It delves into classification, definition, current state research concerning manufacturing processes, domestically internationally. Furthermore, it offers detailed exploration specific real-world scenarios. The evolution geared towards bolstering competitiveness future industry fostering advancement greater efficiency, sustainability, customization.

Language: Английский

Citations

3
Fusion Algorithm Based on Improved A* and DWA for USV Path Planning DOI
Changyi Li,

Lei Yao,

Chao Mi

et al.

Journal of Marine Science and Application, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 1, 2024

Language: Английский

Citations

0
A Mobile Robot Path Planning Method Based on Dynamic Multipopulation Particle Swarm Optimization DOI Creative Commons
Yunjie Zhang, Ning Li,

Yadong Chen

et al.

Journal of Robotics, Journal Year: 2024, Volume and Issue: 2024(1)

Published: Jan. 1, 2024

To overcome the limitations of particle swarm optimization (PSO) in mobile robot path planning, including issues such as premature convergence and sensitivity to local optima, this study proposes a novel approach, dynamic multipopulation (DMPSO). First, (MPSO) framework is extended by introducing strategy that adjusts number subpopulations real‐time. This designed enhance algorithm’s search capabilities accelerate its convergence. Second, inertia weights learning factors within algorithm are refined achieve balance between global exploration exploitation. Furthermore, an initialization based on fitness variance developed improve population diversity, mitigate convergence, ability locate optima. Lastly, positive feedback acceleration factor introduced optimize positions, thereby improving accelerating Simulation experiments have validated DMPSO offers improved capabilities, enhanced precision, more rapid rate. In comparison PSO, reduces length 3% decreases iterations 17%.

Language: Английский

Citations

0