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Bio-inspired Obstacle Avoidance: From Animals to Intelligent Agents

Ruben Nuredini
2018 Journal of Computers  
We present an overview of most noteworthy, elaborated, and interesting biologically-inspired approaches for solving the obstacle avoidance problem.  ...  We categorize these approaches into three groups: nature inspired optimization, reinforcement learning, and biorobotics.  ...  However, they usually yield insights for potential application in engineering as a secondary benefit.  ... 
doi:10.17706/jcp.13.2.146-153 fatcat:7p737skmc5ahfpv66io5sldlaa

PATH FINDING BASED ON ARTIFICIAL INTELLIGENCE TECHNIQUES: A REVIEW

Dr. Eyad I. Abbas, Dr. Sundus D. Hasan, Rawaa Jawad
2020 International Journal of Engineering Applied Sciences and Technology  
The purpose of this paper is to review the modeling, optimization criteria and solution algorithms for the path planning of mobile robot.  ...  Path finding reduce the wear and capital investment of mobile robot. Several methodologies have been proposed and reported in the literature for the path planning of mobile robot.  ...  Xiao H., Liao L. and Zhou F. (2007)" Mobile Robot Path Planning Based on Q-ANN". IEEE International Conference on Automation and Logistics, 21.  ... 
doi:10.33564/ijeast.2020.v05i04.013 fatcat:6ayyqxh5enbrplfunh5vkx2vva

Reinforcement Learning-Based Path Planning Algorithm for Mobile Robots

ZiXuan Liu, Qingchuan Wang, Bingsong Yang, Kalidoss Rajakani
2022 Wireless Communications and Mobile Computing  
A robot path planning algorithm based on reinforcement learning is proposed.  ...  The algorithm discretizes the information of obstacles around the mobile robot and the direction information of target points obtained by LiDAR into finite states, then reasonably designs the number of  ...  Therefore, Q-learning has begun to develop in combination with other methods [13] . In this paper, a path planning algorithm of mobile robot based on reinforcement learning is proposed.  ... 
doi:10.1155/2022/1859020 fatcat:vnoxlmjptvg2bodcfqd36glwom

An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning

Ben Beklisi Kwame Ayawli, Ryad Chellali, Albert Yaw Appiah, Frimpong Kyeremeh
2018 Journal of Advanced Transportation  
This paper presents an overview of nature-inspired, conventional, and hybrid path planning strategies employed by researchers over the years for mobile robot path planning problem.  ...  There are countless research contributions from researchers aiming at finding solution to autonomous mobile robot path planning problems.  ...  [221] used Ant-Q reinforcement learning based on Ant Colony approach algorithm as a technique to address mobile robot path planning and obstacle avoidance problem.  ... 
doi:10.1155/2018/8269698 fatcat:fpadrzacozbdnkcqkjduokmoc4

A Novel GRU-RNN Network Model for Dynamic Path Planning of Mobile Robot

Jianya Yuan, Hongjian Wang, Changjian Lin, Dawei Liu, Dan Yu
2019 IEEE Access  
A dynamic path planning method based on a gated recurrent unit-recurrent neural network model is proposed for the problem of path planning of a mobile robot in an unknown space.  ...  INDEX TERMS Mobile robot, gated recurrent unit-recurrent neural network, dynamic path planning, ant colony optimization, artificial potential field.  ...  CONCLUSION This paper introduces the development of a mobile robot collision avoidance algorithm based on improved ACO and APF and designs a new GRU-RNN network model for dynamic path planning of mobile  ... 
doi:10.1109/access.2019.2894626 fatcat:vg3khcjygzeq5i2ekvm6bh4dim

Path Planning for Wheeled Mobile Robot in Partially Known Uneven Terrain

Bo Zhang, Guobin Li, Qixin Zheng, Xiaoshan Bai, Yu Ding, Awais Khan
2022 Sensors  
Second, facilitated by the terrain model, we use A☆ algorithm to plan a global path for the robot based on the partially known map.  ...  Finally, the Q-learning method is employed for local path planning to avoid locally detected obstacles in close range as well as impassable terrain areas when the robot tracks the global path.  ...  In [28] , a local path planning method was designed based on a Q-learning algorithm for a robot to avoid locally detected obstacles.  ... 
doi:10.3390/s22145217 pmid:35890897 pmcid:PMC9322884 fatcat:j2smmjxju5bvxjyceh4adlalem

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Anish Pandey
2017 International Robotics & Automation Journal  
Several techniques have been applied by the various researchers for mobile robot navigation and obstacle avoidance.  ...  Figure 1: General classification of the Deterministic algorithm, Nondeterministic (Stochastic) algorithm, and Evolutionary algorithm used for mobile robot navigation.  ...  In [53] , the authors have combined the multi-layer feed forward artificial neural network with Q-reinforcement learning method to construct a robust path-planning algorithm for the mobile robot.  ... 
doi:10.15406/iratj.2017.02.00023 fatcat:m6viumq36zf5zbfeexua475gjy

Path-Integral-Based Reinforcement Learning Algorithm for Goal-Directed Locomotion of Snake-Shaped Robot

Qi Yongqiang, Yang Hailan, Rong Dan, Ke Yi, Lu Dongchen, Li chunyang, Liu Xiaoting
2021 Discrete Dynamics in Nature and Society  
This paper proposes a goal-directed locomotion method for a snake-shaped robot in 3D complex environment based on path-integral reinforcement learning.  ...  Simulation results show that the planned path can avoid all obstacles and reach the destination smoothly and swiftly.  ...  Figure 5 : 5 Planned path based on the ant colony algorithm. Figure 6 Figure 4 64 : e planned path of the snake-shaped robot with climbing ability. : e change of best individual fitness.  ... 
doi:10.1155/2021/8824377 doaj:33a53d5e43a24153b6e86a9db7cf64af fatcat:zkzw2b62nvbtzp3my3kivs7vee

Path Planning of Industrial Wheeled Robots Based on Wireless Communication and Machine Learning Algorithms

Jingmin Li
2022 Mobile Information Systems  
Based on the reinforcement Q learning and BP network, this work studies the path planning of industrial wheeled robots.  ...  The algorithm first designs robot states and actions based on reinforcement Q learning and grid map and establishes Q matrix.  ...  Path Planning Based on Q-CM Learning is section proposes an industrial wheeled robot path planning algorithm with Q-CM on basis of grid map. e algorithm utilizes a reinforcement Q learning algorithm to  ... 
doi:10.1155/2022/3386116 doaj:ea6c6f2dc32c408f9f6c150ae1bd2217 fatcat:kjwheiaj75hfjp3rdkutke5a2u

An Effective Dynamic Path Planning Approach for Mobile Robots Based on Ant Colony Fusion Dynamic Windows

Liwei Yang, Lixia Fu, Ping Li, Jianlin Mao, Ning Guo
2022 Machines  
To further improve the path planning of the mobile robot in complex dynamic environments, this paper proposes an enhanced hybrid algorithm by considering the excellent search capability of the ant colony  ...  optimization (ACO) for global paths and the advantages of the dynamic window approach (DWA) for local obstacle avoidance.  ...  [29] proposed Deep Q-Network (DQN), deep reinforcement learning has continued to make breakthroughs, and some researchers now try to solve path planning problems by deep reinforcement learning.  ... 
doi:10.3390/machines10010050 fatcat:zd52bcpvhraufagjr272oxs5pe

APPROACH TO BUILDING A GLOBAL MOBILE AGENT WAY BASED ON Q-LEARNING

Vitalii Martovytskyi, Oleksandr Ivaniuk
2020 Сучасний стан наукових досліджень та технологій в промисловості  
The purpose of the work is to create an algorithm for planning the route of autonomous mobile systems in space using the Q-learning algorithm.  ...  The research is based on scientific articles and other materials from foreign conferences and archives in the field of machine learning, deep learning and deep reinforcement learning.  ...  Conclusions This article presents an approach to building a global mobile agent path based on Q-learning. When changing the local environment, Q-learning uses local path planning.  ... 
doi:10.30837/itssi.2020.13.043 fatcat:a7xhv3sep5ejlarc6ymlye3lje

Wheeled Mobile Robot Path Planning and Path Tracking Controller Algorithms: A Review

Oluwaseun. O. Martins, Department of Mechatronics Engineering, Federal University Oye-Ekiti Ekiti State, Nigeria, Adefemi. A. Adekunle, Samuel. B. Adejuyigbe, Oluwole. H. Adeyemi, Michael. O. Arowolo
2020 Journal of Engineering Science and Technology Review  
The former evaluates and identifies an obstacle free path for a mobile robot to traverse within its environment and the later deals with the controller design for a mobile robot to track the reference  ...  Thus, this paper therefore, presents a review of wheeled mobile robot path planning algorithms and path tracking control algorithms applied within the last decennium.  ...  Sichkar [92] evaluates the performance of Q-Learning algorithm and its modification SARSA reinforcement algorithm for global path planning for mobile robots.  ... 
doi:10.25103/jestr.133.17 fatcat:qvad4rqszbf5nm2oymv4i2sroa

Multi-Destination Path Planning Method Research of Mobile Robots Based on Goal of Passing through the Fewest Obstacles

Hongchao Zhuang, Kailun Dong, Yuming Qi, Ning Wang, Lei Dong
2021 Applied Sciences  
The optimal mobile node of path planning is gained. According to the Q-learning algorithm, the parameters of the reward function are optimized to obtain the q value of the path.  ...  In order to effectively solve the inefficient path planning problem of mobile robots traveling in multiple destinations, a multi-destination global path planning algorithm is proposed based on the optimal  ...  This research compares the use of NSGA_II algorithm to solve the path planning problem. It compares the three representations of integer coding, binary coding and mixed coding.  ... 
doi:10.3390/app11167378 fatcat:ryydgil2tzcvnn62aazb43g6py

A Path-Planning Approach Based on Potential and Dynamic Q-Learning for Mobile Robots in Unknown Environment

Bing Hao, He Du, Jianshuo Zhao, Jiamin Zhang, Qi Wang, Amparo Alonso-Betanzos
2022 Computational Intelligence and Neuroscience  
Experiments undertaken on simulated maps confirm that the PDQL when used for the path-planning problem of mobile robots in an unknown environment outperforms the state-of-the-art algorithms with respect  ...  To solve the path-planning problem of mobile robots in an unknown environment, a potential and dynamic Q-learning (PDQL) approach is proposed, which combines Q-learning with the artificial potential field  ...  are all dependent on the path planning of mobile robots (MRs).  ... 
doi:10.1155/2022/2540546 pmid:35694567 pmcid:PMC9184183 fatcat:ypamlpoaybdt3ctsm5hc2mu25y

Motion planning for mobile Robots–focusing on deep reinforcement learning: A systematic Review

Huihui Sun, Weijie Zhang, Runxiang YU, Yujie Zhang
2021 IEEE Access  
INDEX TERMS Mobile robot; Deep reinforcement learning; Motion planning  ...  According to the surveys, the potential research directions of motion-planning algorithms serving for mobile robots are enlightened.  ...  Conventional algorithm is preferred for global motion planning in static environment, and then deep reinforcement learning algorithm is used for dynamic obstacle avoidance.  ... 
doi:10.1109/access.2021.3076530 fatcat:53kdh5cfgvcang5xymf4vrqx2e
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