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local path planning Method of the Self-propelled Model Based on Reinforcement Learning in Complex Conditions

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Journal of Marine Science and Application 2014年第3期13卷 333-339页

作者： Yi Yang Yongjie Pang Hongwei Li Rubo Zhang Science and Technology on Underwater Vehicle Laboratory Harbin Engineering University Harbin 150001 China College of Electromechanical & Information Engineering Dalian Nationalities University Dalian 116600 China

Conducting hydrodynamic and physical motion simulation tests using a large-scale self-propelled model under actual wave conditions is an important means for researching environmental adaptability of ships. During the navigation test of the self-propelled model, the complex environment including various port facilities, navigation facilities, and the ships nearby must be considered carefully, because in this dense environment the impact of sea waves and winds on the model is particularly significant. In order to improve the security of the self-propelled model, this paper introduces the Q learning based on reinforcement learning combined with chaotic ideas for the model＇s collision avoidance, in order to improve the reliability of the local path planning. Simulation and sea test results show that this algorithm is a better solution for collision avoidance of the self navigation model under the interference of sea winds and waves with good adaptability.

关键词： self-propelled model local path planning Q learning obstacle avoidance reinforcement learning

local path planning based on improved Dynamic window approach

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Local path planning based on improved Dynamic window approac...

第40届中国控制会议

作者： Yong Li Qidan Zhu College of Intelligent Systems Science and Engineering Harbin Engineering University

An improved dynamic window method is designed as a local path planning *** at the problem of unreasonable path selection in traditional dynamic window approach when it is close to the target point and dense obstacles,an improved dynamic window approach is proposed.A new objective function is introduced in the algorithm to improve the speed selection when the robot approaches the target *** number of obstacles is introduced into the velocity evaluation function to improve the problem that dynamic window approach is easy to bypass dense *** experiments in different environments show the effectiveness of the improved algorithm.

关键词： Mobile robot Dynamic Window Approach local path planning

local path planning of Mobile Robot Based on Artificial Potential Field

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Local Path Planning of Mobile Robot Based on Artificial Pote...

第三十九届中国控制会议

作者： Wang Di Li Caihong Guo Na Song Yong Gao Tengteng Liu Guoming School of Computer Science and Technology Shandong University of Technology School of Mechanical Electrical&Information EngineeringShandong University

When a mobile robot plans its local path under the environment with obstacles using the artificial potential field strategy,it may fall into the dead zone and fails to reach the target point because of the problem of local minimum point exited in the traditional *** order to solve the problem,the left turning potential field method and virtual target point method are proposed based on artificial potential *** left turning potential field method forces the robot to turn at the local minimum point and jump out of here;the virtual target point method is to set the virtual target point at an appropriate position when the robot is at the local minimum *** the robot should ignore the impact of the target point and obstacles on it,so that it can turn around and jump out of the local minimum *** simulation and test,it is proved that the mobile robot can bypass the local minimum point and reach the target point successfully by the designed methods.

关键词： Artificial potential field method Left turning artificial potential field method Virtual target point method local path planning Mobile robot

local path planning of autonomous vehicles based on A* algorithm with equal-step sampling

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Local path planning of autonomous vehicles based on A* algor...

第37届中国控制会议

作者： WANG Yijing LIU Zhengxuan ZUO Zhiqiang LI Zheng Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University

path planning is an important component in the study of autonomous vehicles. And local path planning is a pivotal issue, which will greatly influence the safety and comfort of the vehicle. On the premise of security, this paper proposes a novel A* algorithm with equal-step sampling based on vehicle kinematics model to improve the comfort of the path as much as possible. The algorithm is based on vehicle kinematics and introduces an enhanced cost function. The simulation results illustrate the feasibility of this approach on lane-keep and obstacle avoidance in different scenarios. In addition, the real vehicle test results of "Smart Driving" autonomous vehicle of Tianjin University demonstrate the capability of real-time implementation.

关键词： Autonomous vehicles local path planning Vehicle kinematics model Equal-step sampling

Double BP Q-Learning Algorithm for local path planning of Mobile Robot

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Journal of Computer and Communications 2021年第6期9卷 138-157页

作者： Guoming Liu Caihong Li Tengteng Gao Yongdi Li Xiaopei He School of Computer Science and Technology Shandong University of Technology Zibo China

Aiming at the dimension disaster problem, poor model generalization ability and deadlock problem in special obstacles environment caused by the increase of state information in the local path planning process of mobile robot, this paper proposed a Double BP Q-learning algorithm based on the fusion of Double Q-learning algorithm and BP neural network. In order to solve the dimensional disaster problem, two BP neural network fitting value functions with the same network structure were used to replace the two Q value tables in Double Q-Learning algorithm to solve the problem that the Q value table cannot store excessive state information. By adding the mechanism of priority experience replay and using the parameter transfer to initialize the model parameters in different environments, it could accelerate the convergence rate of the algorithm, improve the learning efficiency and the generalization ability of the model. By designing specific action selection strategy in special environment, the deadlock state could be avoided and the mobile robot could reach the target point. Finally, the designed Double BP Q-learning algorithm was simulated and verified, and the probability of mobile robot reaching the target point in the parameter update process was compared with the Double Q-learning algorithm under the same condition of the planned path length. The results showed that the model trained by the improved Double BP Q-learning algorithm had a higher success rate in finding the optimal or sub-optimal path in the dense discrete environment, besides, it had stronger model generalization ability, fewer redundant sections, and could reach the target point without entering the deadlock zone in the special obstacles environment.

关键词： Mobile Robot local path planning Double BP Q-Learning BP Neural Network Transfer Learning

On local path planning for the Mobile Robot based on QL Algorithm

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On Local Path Planning for the Mobile Robot based on QL Algo...

第37届中国控制会议

作者： SONG Li LI Caihong WANG Xiaoyu ZHANG Ning FU Hao School of Computer Science and Technology Shandong University of Technology School of Computer Software Tianjin University

This paper proposes a local path planning method tor the mobile robot based on the Q-Learnmg（QL） algorithm to improve the common problems existed in the traditional method,such as the slow convergence rate,the dilemma between exploration and exploitation,and the complex obstacles in the ***,the variables of state and action are designed and discretized according to the path planning *** a Q-value function matrix is used to store the reinforcement value,and a reward function is constructed according to the requirements of obstacle avoidance and shortest *** solve the balance problem of exploration and exploitation and improve the convergence speed,the ε-balance strategies and the selection algorithm of actions are designed to improve the learning process of *** training of QL,the optimal pairs of state and action are obtained,further the optimal control rules are achieved and used to perform local path *** order to prevent the incomplete visiting problem for the pairs of state and action,the steering rules are appended to improve the efficiency of the path ***,the designed method has been *** results show that the robot can plan an optimal or sub-optimal path while avoiding obstacles,even in a complicated environment.

关键词： mobile robot local path planning QL control rules ε-balance strategies selection algorithm of actions

Research on Security of Key Algorithms in Intelligent Driving System

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Chinese Journal of Electronics 2019年第1期28卷 29-38页

作者： LIU Shangdong WU Ye JI Yimu CHEN Chen BI Qiang JIAO Zhipeng GONG Jian WANG Ruchuan School of Computer Science and Engineering Southeast University School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Provincial Key Laboratory of Computer Network Technology Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks

With the rapid development of the smart driving technology, the security of smart driving algorithms is becoming more and more important. Four core smart driving algorithms are determined by studying the architecture of smart driving algorithm. These algorithms comprise local path planning, pedestrian detection, lane detection and obstacle detection. The security issues of these algorithms are investigated by closely examining the work carried out by the algorithms. We found that there are vulnerabilities in all four algorithms. These vulnerabilities can cause abnormality and even road accidents for the smart cars. The final experiment shows that the vulnerabilities of these algorithms do exist under certain circumstances and therefore have high security risks. This study will lay a foundation to improve the security of the smart driving system.

关键词： Smart driving algorithm local path planning Pedestrian detection Lane line detection Obstacle detection

Novel Algorithm for Mobile Robot path planning in Constrained Environment

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Computers, Materials & Continua 2022年第5期71卷 2697-2719页

作者： Aisha Muhammad Mohammed A.H.Ali Sherzod Turaev Ibrahim Haruna Shanono Fadhl Hujainah Mohd Nashrul Mohd Zubir Muhammad Khairi Faiz Erma Rahayu Mohd Faizal Rawad Abdulghafor Faculty of Manufacturing Engineering Universiti Malaysia Pahang(UMP)Pekan26600Malaysia Department of Mechanical Engineering Faculty of EngineeringUniversity of Malaya50603Kuala LumpurMalaysia Department of Computer Science and Software Engineering College of Information TechnologyUnited Arab Emirates UniversityAl AinUnited Arab Emirates Faculty of Electrical and Electronics Engineering Universiti Malaysia Pahang(UMP)Pekan26600Malaysia Department of Mechatronics Engineering Faculty of TechnologyBayero UniversityKano(BUK)700241Nigeria Computer Science and Engineering Department Chalmers and University of Gothenburg41296GothenburgSweden Department of Artificial Intelligence Faculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala LumpurMalaysia Department of Computer Science Faculty of Information and Communication TechnologyInternational Islamic University Malaysia53100Kuala LumpurMalaysia

This paper presents a development of a novel path planning algorithm, called Generalized Laser simulator (GLS), for solving the mobilerobot path planning problem in a two-dimensional map with the presence ofconstraints. This approach gives the possibility to find the path for a wheelmobile robot considering some constraints during the robot movement inboth known and unknown environments. The feasible path is determinedbetween the start and goal positions by generating wave of points in alldirection towards the goal point with adhering to constraints. In simulation,the proposed method has been tested in several working environments withdifferent degrees of complexity. The results demonstrated that the proposedmethod is able to generate efficiently an optimal collision-free path. Moreover,the performance of the proposed method was compared with the A-star andlaser simulator (LS) algorithms in terms of path length, computational timeand path smoothness. The results revealed that the proposed method hasshortest path length, less computational time and the best smooth path. Asan average, GLS is faster than A∗ and LS by 7.8 and 5.5 times, respectivelyand presents a path shorter than A∗ and LS by 1.2 and 1.5 times. In orderto verify the performance of the developed method in dealing with constraints, an experimental study was carried out using a Wheeled Mobile Robot(WMR) platform in labs and roads. The experimental work investigates acomplete autonomous WMR path planning in the lab and road environmentsusing a live video streaming. local maps were built using data from a live video streaming with real-time image processing to detect segments of theanalogous-road in lab or real-road environments. The study shows that theproposed method is able to generate shortest path and best smooth trajectoryfrom start to goal points in comparison with laser simulator.

关键词： path planning generalized laser simulator wheeled mobile robot global path panning local path planning

A path planning algorithm based on RRT and SARSA（λ） in unknown and complex conditions

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A path planning algorithm based on RRT and SARSA（λ） in un...

第32届中国控制与决策会议

作者： ZOU Qijie ZHANG Yue LIU Shihui Information Engineering Faculty Dalian University

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

The path planning problem of wheeled mobile robot is different from the traditional method in the environment of outer space which is completely *** robots are needed to explore unknown environments while adapting to terrain changes,it is a more challenging *** this paper,a Rapidly-exploring Random Tree（RRT） path planning algorithm based on reinforcement learning SARSA（λ） optimization（RL-RRT） is proposed to solve the local path planning problem in unknown and complex ***-RRT method improves the performance of RRT algorithm by adding optimization for selection of extension points,introducing the idea of biased goal,using task return function,target distance function and angle restriction,ensuring the randomness of RRT,reducing the number of invalid nodes and optimizing the planning performance.A simulation platform is built under Robot Operating System（ROS） and MATLAB to test the role of multi-objective optimization in path *** simulation results show that the RL-RRT method enables the wheeled mobile robot to reach the target node smoothly and steadily in complex and unknown environments without collision with obstacles,which verifies the reliability and effectiveness of the method.

关键词： local path planning RRT SARSA(λ) wheeled mobile robot unknown and complex environments