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Bio-inspired Obstacle Avoidance: From Animals to Intelligent Agents
2018
Journal of Computers
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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
2020
International Journal of Engineering Applied Sciences and Technology
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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
2022
Wireless Communications and Mobile Computing
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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
2018
Journal of Advanced Transportation
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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
2019
IEEE Access
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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
2022
Sensors
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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
2017
International Robotics & Automation Journal
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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
2021
Discrete Dynamics in Nature and Society
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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
2022
Mobile Information Systems
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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
2022
Machines
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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
2020
Сучасний стан наукових досліджень та технологій в промисловості
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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
2020
Journal of Engineering Science and Technology Review
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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
2021
Applied Sciences
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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
2022
Computational Intelligence and Neuroscience
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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
2021
IEEE Access
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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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