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Article
Computer Science, Software Engineering
Sanat K. Pattnaik et al.
Summary: A novel constrained multi-objective function was formulated for mobile robot path planning, and a chemical reaction optimization algorithm was used to solve the problem. Results showed that the proposed method outperformed state-of-the-art methods in various aspects.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Xinliang Xu et al.
Summary: Autonomous group particle swarm optimization is a modified approach that achieves higher optimization performance by introducing Levy flights and dynamic weight strategies.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2022)
Article
Multidisciplinary Sciences
Fei-Fei Li et al.
Summary: This paper proposes an algorithm that integrates the improved artificial fish swarm algorithm with continuous segmented Bezier curves for the path planning and smoothing of mobile robots. The algorithm addresses the low accuracy, inflection points, and long planning paths in traditional artificial fish swarm algorithms by introducing feasible solutions, step sizes based on Dijkstra's algorithm. It also overcomes convergence and degradation issues through the use of dynamic feedback horizon and adaptive step sizes. The algorithm ensures continuous paths in both orientation and curvature, achieving 100% planning accuracy in simulations and satisfying the kinematic characteristics of the mobile robot.
SCIENTIFIC REPORTS
(2022)
Article
Operations Research & Management Science
Arnaud Flori et al.
Summary: This paper proposes a new algorithm called QUAntum Particle Swarm Optimization (QUAPSO) based on quantum superposition and inspired by the Kangaroo Algorithm to simplify the settings and improve the local search efficiency of PSO. Experimental results demonstrate that QUAPSO outperforms six well-known algorithms on a set of 30 test functions.
COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
(2022)
Article
Computer Science, Interdisciplinary Applications
Changgeng Li et al.
Summary: This paper proposes an improved A* algorithm to address the issues of long calculation time, large turning angles, and unsmoothed path in traditional A* algorithm. By introducing bidirectional alternating search, exponential attenuation weighted heuristic function, path node filtering function, and the use of Bezier curves, the algorithm achieves improved search efficiency and smoothness in robot path planning.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Hardware & Architecture
Ting-Wei Zhang et al.
Summary: The paper introduces a new hybrid algorithm based on genetic algorithm and firefly algorithm to address the issue of firefly algorithm easily trapped into local optimal solution. The experimental results demonstrate that the new algorithm can improve accuracy and performance, enhancing the robot's reaction ability and computing power in path planning.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Chemistry, Analytical
Jiabin Yu et al.
Summary: This study proposes a hybrid multi-target path planning algorithm to solve the traversal problem of an unmanned cruise ship in an unknown obstacle environment. The algorithm transforms the multi-target path planning problem into a traveling salesman problem and improves the convergence speed by optimizing the GWO algorithm. Experimental results demonstrate the effectiveness and applicability of the proposed method.
Article
Engineering, Marine
Jiabin Yu et al.
Summary: This paper proposes a path planning algorithm based on the improved D*Lite algorithm for an unmanned surface vessel in an unknown environment. By improving the path cost function and introducing interpolation method and Dubins algorithm, the algorithm obtains a shortest and smooth collision-free path. Experimental results show that the proposed algorithm outperforms the traditional D*Lite algorithm in terms of planning time, path length, and smoothness.
Article
Mathematics
Hao Ma et al.
Summary: This paper proposes to combine the improved artificial potential field method with the rapidly exploring random tree (RRT) algorithm to plan the path. By combining the RRT algorithm to solve the path oscillation, and designing a model predictive control (MPC) trajectory tracking controller with constraints, the method verifies the optimality and conformity of the planned path. Simulation results show that the method effectively solves the problem of path oscillation and can plan the optimal path according to different environments and vehicle motion.
Article
Computer Science, Interdisciplinary Applications
Thanh Sang-To et al.
Summary: This paper introduces an improved Grey Wolf Optimizer algorithm (LGWO), which enhances the algorithm's performance and convergence speed by changing the direction of movement of the leader wolf and the hunting strategy of the wolves. The algorithm shows good performance on classical benchmarks, CEC 2019 functions, and engineering problems.
ADVANCES IN ENGINEERING SOFTWARE
(2022)
Article
Energy & Fuels
Weimin Li et al.
Summary: In this paper, an improved probabilistic roadmap (IPRM) algorithm is proposed to solve the energy consumption problem of multi-UAV path planning with an angle. The algorithm optimizes the sampling space of the PRM algorithm and curves the rotation angle using the sampling third-order B-spline curve method to improve the explicitness and smoothness of the obtained path. The results of the simulation demonstrate the advantages of the IPRM algorithm over other algorithms in certain aspects.
Article
Chemistry, Analytical
Yangqi Ou et al.
Summary: This paper proposes a method to improve the A* (A-Star) algorithm for path planning. The method equips the mobile robot platform with a lidar and inertial measurement unit (IMU), constructs a two-dimensional grid map using the Hdl_graph_slam mapping algorithm, and uses the improved A* algorithm for path planning of the mobile robot. The algorithm introduces path smoothing strategy and safety protection mechanism to improve the smoothness of the path and facilitate the movement of the robot in actual operation. It also eliminates redundant points and minimal corner points by judging the presence of obstacles between two path nodes. Furthermore, the algorithm introduces steering cost model and adaptive cost function to enhance the search efficiency. Experimental results demonstrate the effectiveness of the proposed algorithm, with reductions in average path search time by 13% and average search extension node by 11%. These improvements address the issues of excessive turning points and slow search speed in traditional A* algorithm for path planning.
Article
Computer Science, Hardware & Architecture
Yulan Zhou et al.
Summary: This research proposes an airport AGV path optimization model based on the ant colony optimization algorithm combined with the Dijkstra algorithm (ACO-DA). The model takes into account obstacles in the environment and reduces the running time and path cost of AGVs through baggage pickup sequencing and path planning.
SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS
(2022)
Article
Mathematics
Xiaodong Li et al.
Summary: The article discusses the distribution of emergency perishable materials after a disaster and proposes a multi-period distribution optimization model and an improved algorithm. By handling the uncertainty in demand, it can minimize cost penalties, delivery damage, and unmet demand. Experimental results confirm the better performance of the improved algorithm.
Article
Engineering, Mechanical
Haitao Min et al.
Summary: This paper introduces an environment description method combining global navigation layer and local planning layer, as well as a local motion planning algorithm based on the improved A* algorithm for autonomous driving vehicles in unstructured environment. The improved algorithm avoids profile collision by setting redundant security space and considers path curvature cost in heuristic function design, resulting in smoother paths and avoiding vehicle contours collision.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING
(2021)
Article
Chemistry, Analytical
Jin-Gu Kang et al.
Summary: The paper introduces a rewiring method based on triangular inequality for the RRT-Connect robot path-planning algorithm to improve planning time, showing better performance in terms of both planning time and path length compared to the RRT algorithm and even the RRT-Connect algorithm in various environments through simulation experiments.
Article
Computer Science, Interdisciplinary Applications
Changwei Miao et al.
Summary: The IAACO algorithm proposed in this paper improves upon traditional ACO for indoor mobile robot path planning by introducing new factors and multi-objective performance metrics. It optimizes the convergence and global search abilities of ACO, allowing the robot to achieve global optimization paths with higher real-time and stability in path planning.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Engineering, Electrical & Electronic
Lin Zhang et al.
Summary: An improved localized particle swarm optimization algorithm is proposed in this article to address the shortcomings of local minimum, premature, and low efficiency. The algorithm is enhanced in inertia weights, acceleration factors, and localization, increasing diversity to overcome premature and applying the smoothing principle in path planning. Comparative study shows that the proposed algorithm outperforms basic particle swarm optimization and A-star algorithms in terms of path length, running time, path optimal degree, and stability.
IEEE SENSORS JOURNAL
(2021)
Article
Multidisciplinary Sciences
Huanwei Wang et al.
Summary: The EBHSA* algorithm improves path planning efficiency and robustness by introducing expansion distance, bidirectional search, heuristic function optimization, and smoothing. Simulation tests demonstrate excellent performance, and real-world application on a robot verifies its effectiveness.
Article
Mathematical & Computational Biology
Qisong Song et al.
Summary: The study aims to enhance the efficiency and safety of mobile robot path planning by combining machine learning algorithms, improved random tree algorithms, and a hybrid genetic and ant colony optimization algorithm. In local path planning, machine learning algorithms train models for real-time obstacle avoidance decision making, while in global path planning, greedy algorithms and B-spline curves are used to optimize path length, and genetic and ant colony algorithms are fused to find the best solution.
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Xuezhen Cheng et al.
Summary: The IPSO-GWO algorithm combines grey wolf optimization and PSO algorithms, utilizing chaos and adaptive inertia weight to address issues in traditional PSO algorithm, enhancing global search capability and convergence speed.
FRONTIERS IN NEUROROBOTICS
(2021)
Article
Mathematical & Computational Biology
Kun Hao et al.
Summary: An adaptive genetic algorithm based on collision detection (AGACD) is proposed to address the limitations of the basic genetic algorithm in path planning field. By introducing Delphi weight method and collision detection method, improving population initialization, crossover/mutation operators, and optimization operator, the AGACD effectively solves problems of the basic genetic algorithm, optimizing the quality of convergent individuals and reducing the running time of the algorithm program.
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Leila Pasandi et al.
Summary: The paper introduces the MASA method, which can provide the most optimal route for pedestrian tourists by merging multiple factors with different weights and meeting personalized travel needs. By integrating the Ant Colony Optimization algorithm and a modified A* algorithm, multi-weighted graphs are generated to propose the most suitable tour route.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Interdisciplinary Applications
Laith Abualigah et al.
Summary: This paper introduces a novel population-based optimization method, AO, inspired by the behaviors of eagles during hunting. Through a series of experiments, the superior performance of AO in finding optimal solutions for various problems is demonstrated and compared with other meta-heuristic methods.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Xiaobing Yu et al.
Summary: Grey wolf optimizer is a novel swarm intelligent algorithm with superior optimization capacity. However, it is easy to trap into local optimum when solving complex and multimodal functions. The proposed opposition-based learning grey wolf optimizer incorporates a jumping rate to help the algorithm jump out of local optimum, and dynamically adjusts the coefficient to balance exploration and exploitation.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Engineering, Multidisciplinary
Chuanhu Niu et al.
Summary: This paper proposes a global dynamic path planning method based on an improved A* algorithm, which optimizes the heuristic function to improve efficiency, uses a path optimization strategy to make the path smoother, and combines with the dynamic window method to enhance real-time obstacle avoidance.
MATHEMATICAL PROBLEMS IN ENGINEERING
(2021)
Article
Chemistry, Analytical
Xin Yuan et al.
Summary: In this paper, an improved bat algorithm combined with the dynamic window approach is designed for better performance in path planning. The constructed undirected weighted graph with virtual points provides the robot with path switch strategies. Hybrid path planning methods can significantly reduce path length compared to the dynamic window approach.
Article
Robotics
Jing Zhang et al.
Summary: This article presents a novel path planning algorithm for autonomous land vehicles, which improves upon the shortcomings of the traditional A-Star algorithm by combining the artificial potential field method. The proposed algorithm enhances efficiency in path planning and makes the path smoother for easier control of autonomous land vehicles.
INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS
(2021)
Article
Computer Science, Information Systems
Gang Tang et al.
Summary: This research presents a path planning method based on the geometric A-star algorithm, which optimizes node filtering and path curves using a grid method, showing effectiveness in AGV applications.
Article
Computer Science, Information Systems
Tao Qiuyun et al.
Summary: This paper studies the AGV path planning problem in smart manufacturing workshops, establishes a mathematical model and proposes an improved particle swarm optimization algorithm to obtain an optimal path. Experimental results show that the algorithm can improve the efficiency of AGV in material transportation.
Article
Computer Science, Interdisciplinary Applications
Konstantinos Zervoudakis et al.
COMPUTERS & INDUSTRIAL ENGINEERING
(2020)
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Mathematical & Computational Biology
Jianwei Ma et al.
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
(2020)
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Computer Science, Artificial Intelligence
Qamar Askari et al.
KNOWLEDGE-BASED SYSTEMS
(2020)
Article
Computer Science, Artificial Intelligence
Yunfeng Zhang et al.
KNOWLEDGE-BASED SYSTEMS
(2020)
Article
Energy & Fuels
Arooj Tariq Kiani et al.
Article
Computer Science, Information Systems
Mingshan Xie et al.
IEEE INTERNET OF THINGS JOURNAL
(2020)
Review
Robotics
Shang Erke et al.
INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS
(2020)
Review
Engineering, Multidisciplinary
B. K. Patle et al.
DEFENCE TECHNOLOGY
(2019)
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Mathematics
Fanrong Kong et al.
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Computer Science, Artificial Intelligence
Juan Li et al.
INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION
(2019)
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Hilal Arslan et al.
TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
(2019)
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Multidisciplinary Sciences
Han-ye Zhang et al.
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Computer Science, Interdisciplinary Applications
Seyedali Mirjalili et al.
ADVANCES IN ENGINEERING SOFTWARE
(2017)
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Computer Science, Artificial Intelligence
Seyedali Mirjalili et al.
NEURAL COMPUTING & APPLICATIONS
(2016)
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Computer Science, Artificial Intelligence
Seyedali Mirjalili
KNOWLEDGE-BASED SYSTEMS
(2016)
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Automation & Control Systems
Md. Arafat Hossain et al.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2015)
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Computer Science, Interdisciplinary Applications
Seyedali Mirjalili et al.
ADVANCES IN ENGINEERING SOFTWARE
(2014)
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Mathematics, Applied
Daniel-Ioan Curiac et al.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2014)
Article
Automation & Control Systems
Vincent Roberge et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2013)
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Yudong Zhang et al.
SCIENTIFIC WORLD JOURNAL
(2013)
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Esmat Rashedi et al.
INFORMATION SCIENCES
(2009)
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Computer Science, Artificial Intelligence
Dan Simon
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2008)
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M. Fatih Tasgetiren et al.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2007)
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Robotics
Dave Ferguson et al.
JOURNAL OF FIELD ROBOTICS
(2006)
