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Comparison and Analysis of Obstacle Avoiding path planning of Mobile Robot by Using Ant Colony Optimization and Teaching Learning Based Optimization Techniques 1st

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1st International Conference on Information and Communication Technology for Intelligent Systems (ICTIS)

作者： Ansari, A. Q. Ibraheem Katiyar, Sapna Jamia Millia Islamia New Delhi India

ISBN: (纸本)9783319309279;9783319309262

Now a day, one of the prime concerns of mobile robot is path planning, in the area of industrial robotics. A path planning optimization method was proposed to calculate shortest collision free path from source to destination by avoiding static as well as dynamic obstacles. Therefore, it is necessary to select appropriate optimization technique for optimization of paths. Such problems can be solved by metaheuristic methods. This research paper demonstrates the comparison and analysis of two Soft Computing Techniques i.e. Ant Colony Optimization (ACO) and Teaching Learning Based Optimization (TLBO) by simulating respective algorithms for finding shortest path of a Mobile Robot by Obstacle avoidance & path re-planning and path Tracking. Both of these techniques seem to be a promising technique with relatively competitive performances. The ACO has been more widely used in that and it gives good solution with smaller numbers of predetermined parameters in comparison with other algorithms.

关键词： path planning algorithm Teaching learning based optimization (TLBO) Ant colony optimization (ACO) Metaheuristic Pheromone and obstacle avoidance

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Combining Voronoi Graph and Spline-Based Approaches for a Mobile Robot path planning 14th

Combining Voronoi Graph and Spline-Based Approaches for a Mo...

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14th International Conference on Informatics in Control, Automation and Robotics (ICINCO)

作者： Magid, Evgeni Lavrenov, Roman Svinin, Mikhail Khasianov, Airat Kazan Fed Univ Higher Sch Informat Technol & Informat Syst Intelligent Robot Dept Kremlyovskaya Str 35 Kazan Russia Ritsumeikan Univ Coll Informat Sci & Engn Robot Dynam & Control Lab Kyoto Japan Kazan Fed Univ Higher Sch Informat Technol & Informat Syst Kremlyovskaya Str 35 Kazan Russia

ISBN: (纸本)9783030112929;9783030112912

Potential function based methods play significant role in both global and local path planning. While these methods are characterized with good reactive behaviour and implementation simplicity, they suffer from a well-known problem of getting stuck in local minima of a navigation function. In this paper we propose a modification of our original spline-based path planning algorithm for a mobile robot navigation, which succeeds to solve local minima problem and considers additional criteria of start and target points visibility to help optimizing the path selection. We apply a Voronoi graph based path as an input for iterative multi criteria optimization algorithm and present a path finding strategy within different homotopies that uses the new method. The algorithm was implemented in Matlab environment and demonstrated significantly better results than the original approach. The comparison was based on success rate, number of iterations and running time of the algorithms. In total, several thousands tests were performed in 18 different simulated environments.

关键词： Robotics path planning algorithm Voronoi diagram Potential field Mobile robot Simulated experiments MATLAB

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A novel fuzzy inference method for urban incomplete road weight assignment

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地球空间信息科学学报（英文版） 2024年第6期27卷中插15,2008-2022页

作者： Longhao Wang Xiaoping Rui College of Hydrology and Water Resources Hohai UniversityNanjingChina Key Laboratory of Water Cycle and Related Land Surface Processes Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina College of Resources and Environment University of Chinese Academy of SciencesBeijingChina School of Earth Science and Engineering Hohai UniversityNanjingChina

One of the keys in time-dependent routing is determining the weight of each road network link based on traffic *** facilitate the estimation of the road's weight,Global Position System(GPS)data are commonly used in obtaining real-time traffic ***,the information obtained by taxi-GPS does not cover the entire road *** at incom-plete traffic information on urban roads,this paper proposes a novel fuzzy inference *** considers the combined effect of road grade,traffic information,and other spatial *** the third law of geography as the basic premise,that is,the more similar the geogra-phical environment,the more similar the characteristics of the geographical target will *** method uses a Typical Link Pattern(TLP)model to describe the geographical *** TLP represents typical road sections with complete ***,it determines the relationship between roads lacking traffic information and the TLPs according to their related *** obtaining the TLPs,this method ascertains the weight of road links by calculating their similarities with TLPs based on the theory of fuzzy *** at road links at different places,the dividing-conquering strategy and globe algorithm are also introduced to calculate the *** two strategies are used to address the excessively fragmented or lengthy *** experimental results with the case of Newcastle show robustness in that the average Root Mean Square Error(RMSE)is 1.430 mph,and the bias is 0.2％;the overall RMSE is 11.067 mph,and the bias is 0.6％.This article is the first to combine the third law of geography with fuzzy inference,which significantly improves the estimation accuracy of road weights with incomplete *** application and validation show that the method can accurately predict vehicle speed under incomplete information.

关键词： Weight assignment path planning algorithm fuzzy inference road network

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3-D Trajectory planning of Aerial Vehicles Using RRT

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2017年第3期25卷 1116-1123页

作者： Pharpatara, P. Herisse, B. Bestaoui, Y. Off Natl Etud & Rech Aerosp F-91761 Palaiseau France Geoinformat & Space Technol Dev Agcy Publ Org Bangkok 10210 Thailand Univ Evry Val dEssonne Lab Informat Biol Integrat & Syst Complexes F-91020 Evry France

This brief presents a trajectory planning algorithm for aerial vehicles traveling in 3-D space while avoiding obstacles. The nature of the obstacles can be, for example, radar detection areas, cooperating and non-cooperating vehicles, and so on. Thus, it is a complex trajectory planning problem. The proposed planner is based on the optimal rapidly exploring random tree (RRT*) algorithm. Artificial potential fields are combined with the RRT* algorithm to accelerate the convergence speed to a suboptimal solution by biasing the random state generation. The performance of this framework is demonstrated on a complex missile application in a heterogeneous environment. Indeed, since the air density decreases exponentially with altitude, the maneuverability of the aerial vehicle depending on aerodynamic forces also decreases exponentially with altitude. To face this problem, the shortest paths of Dubins-like vehicles traveling in a heterogeneous environment are used to build the metric. In the simulation results, this framework can find the first solution with fewer iterations than the RRT and the RRT* algorithm. Moreover, the final solution obtained within a given number of iterations is closer to an optimal solution regarding the considered criterion.

关键词： Aerial robotics complex environment optimal control optimal rapidly exploring random tree (RRT*) algorithm path planning algorithm

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Distributed multi-vehicle task assignment in a time-invariant drift field with obstacles

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IET CONTROL THEORY AND APPLICATIONS 2019年第17期13卷 2886-2893页

作者： Bai, Xiaoshan Yan, Weisheng Cao, Ming Xue, Dong Univ Groningen Fac Sci & Engn NL-9747 AG Groningen Netherlands Northwestern Polytech Univ Sch Marine Sci & Technol 127 West Youyi Rd Xian 710072 Shaanxi Peoples R China Tech Univ Munich Dept Elect & Comp Engn Arcisstr 21 D-80209 Munich Germany

This study investigates the task assignment problem where a fleet of dispersed vehicles needs to visit multiple target locations in a time-invariant drift field with obstacles while trying to minimise the vehicles' total travel time. The vehicles have different capabilities, and each kind of vehicles can visit a certain type of the target locations;each target location might require to be visited more than once by different kinds of vehicles. The task assignment problem has been proven to be NP-hard. A path planning algorithm is first designed to minimise the time for a vehicle to travel between two given locations through the drift field while avoiding any obstacle. The path planning algorithm provides the travel cost matrix for the target assignment, and generates routes once the target locations are assigned to the vehicles. Then, a distributed algorithm is proposed to assign the target locations to the vehicles using only local communication. The algorithm guarantees that all the visiting demands of every target will be satisfied within a total travel time that is at worst twice of the optimal when the travel cost matrix is symmetric. Numerical simulations show that the algorithm can lead to solutions close to the optimal.

关键词： remotely operated vehicles mobile robots distributed algorithms computational complexity minimisation total travel time travel cost matrix multivehicle task assignment time-invariant drift field task assignment problem dispersed vehicles multiple target locations path planning algorithm target assignment distributed algorithm numerical simulation

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Practical motion planning for car-parking control in narrow environment

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IET CONTROL THEORY AND APPLICATIONS 2010年第1期4卷 129-139页

作者： Kim, D. Chung, W. Park, S. Korea Univ Dept Mech Engn Seoul South Korea

The automatic parking assist system is one of the key components in future automobiles. path planning of a car-like robot is difficult because of non-holonomic constraints. In this study, the authors propose a practical path planning algorithm for the car-parking control problem. Reachable regions from a goal can be easily computed using the proposed scheme. A variety of candidate paths can be generated by using conventional back-propagation scheme. Finally, we can obtain optimal solutions with respect to performance measures such as collision safety, moving distance, control efforts and so forth. The presented simulation results clearly show that the proposed scheme provides useful solutions.

关键词： moving distance Spatial variables control automatic parking assist system back-propagation scheme Road-traffic system control car-like robot automobiles collision safety control efforts motion planning path planning car parking control path planning algorithm

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Tool path Optimization for Complex Cavity Milling Based on Reinforcement Learning Approach

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IEEE ACCESS 2023年 11卷 66793-66807页

作者： Wan, Yi Xu, Wei Zuo, Tian-Yu Nanjing Xiaozhuang Univ Sch Environm Sci Nanjing 211171 Peoples R China Sanjiang Univ Sch Mech & Elect Engn Nanjing 210012 Peoples R China Nanjing Univ Informat Sci & Technol Sch Automat Nanjing 210044 Peoples R China

In the machining of parts, tool paths for complex cavity milling often have different generation options, as opposed to simple machining features. The different tool path generation options influence the machining time and cost of the part during the machining process. Decision makers prefer tool path solutions that have fewer blanking lengths, which means that the machining process is more efficient. Therefore, in order to reduce costs and increase efficiency, it is necessary to carefully design the tool path generation for the features to be machined on the part, especially for complex cavity milling features. However, solutions to the problem of optimal design of tool paths for complex cavity milling features have not been well developed in current research work. In this paper, we present a systematic solution for complex cavity milling tool path generation based on reinforcement learning. First, a grid converter is executed for converting the 3D geometry of the cavity milling feature into a matrix of planar grid points recognisable by the program, set according to the cutting parameters. Afterwards, the tool path generation process is refined and modelled as a Markov decision process. Ultimately, a tool path generation solution combining the A* algorithm with the Q-learning algorithm is executed. The agent iterates through trial and error to construct an optimal tool path for a given cavity milling task. Three case experiments demonstrate the feasibility of the proposed approach. The superiority of the reinforcement learning-based approach in terms of solution speed and solution quality is further demonstrated by comparing the proposed approach with the evolutionary computational techniques currently popular in research for solving tool path optimisation design problems.

关键词： Tool path optimization cavity milling path planning algorithm reinforcement learning Q-learning algorithm

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Waypoint Navigation of Small-Scale UAV incorporating Dynamic Soaring

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JOURNAL OF NAVIGATION 2011年第1期64卷 29-44页

作者： Ariff, O. K. Go, T. H. Nanyang Technol Univ Singapore Singapore

The latest attempts at improving small scale autonomously guided Uninhabited Aerial Vehicles (UAVs) have concentrated around the increase of range and speed. One of these ways is to incorporate dynamic slope soaring manoeuvres as part of the flight path. This is in contrast to most conventional path-planning algorithms where waypoint guidance is merged with terrain avoidance or contour following capability. Additionally, current trajectory optimization techniques are iterative and so have a considerable computational load. The proposed algorithm is based on Dubin's curves, and is therefore optimal by definition. Being non-iterative, it is comparatively a more efficient algorithm. Hence, a key advantage of the proposed technique is that the desired trajectory is generated quickly in real time with minimum computational load while satisfying the spatial constraints of dynamic slope soaring.

关键词： dynamic soaring path planning algorithm Dubin's curve differential geometry

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Improved value iteration network for path planning

Improved value iteration network for path planning

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作者： Shijia Chai Department of Electronic Engineering Tsinghua University

In this study,a series of improved path planning algorithms are designed for path planning tasks in autonomous control based on deep reinforcement *** Value Iteration Network(VIN) is used to deal with the path planning *** VIN performs well on small size maps,but when it comes to a bigger size of map on test set,the success rate *** order to solve the problem that origin VIN lacks long-distance multi-step planning ability on large maps and generalization ability is insufficient,a three-step improvement was *** of all,in view of the inconvenient data flow and the disappearance of gradients caused by the network being too deep,the jump connection structure is used to obtain the deeper VIN,in which the accuracy of the experiment is ***,with the purpose of solving the problem that the complexity of the model is greatly increased due to the deepening of the network,Batch normalization is used to obtain a new network with dueling architecture plus batch normalization layer,which further accelerates the convergence speed of the ***,to deal with the global path planning problem on the big map,the hierarchical network structure is adopted for hierarchical value iteration,and the Hierarchical Structure VIN is *** Hierarchical Structure VIN,the long-term planning ability and generalization ability of the algorithm have been significantly improved,and the algorithm could figure out the large-scale and complex path planning problem.

关键词： path planning algorithm deep reinforcement learning deep learning hierarchical structure

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The Development of the Battle Field Simulation Program using the Survivability on the Probability Map

The Development of the Battle Field Simulation Program using...

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9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Kim, Min-Ho Lee, Min-Cheol Pusan Natl Univ Control & Automat Syst Devis Busan 609735 South Korea Pusan Natl Univ Sch Mech Engn Busan 609735 South Korea

ISBN: (纸本)9781467331104

There are a lot of researches on the path planning algorithm for those of the unmanned vehicle. Previous researches related to the path planning mainly focused on finding the shortest path on the given map. However, on the battle field, if the vehicle just moves along the shortest path, it could not reach the goal, because of the enemy's attack. Therefore it's necessary that the new path planning algorithm for the unmanned vehicle on the battle field. In this paper, we will suggest a new path planning algorithm for the vehicle on the battle field using extended A* algorithm with survivability. For this, we will define the survivability and develop the additional cost function to find the optimal path in this situation. To verify the proposed algorithm, we developed the simulation program and the results will be shown.

关键词： Unmanned Vehicle Survival Probability path planning algorithm A* algorithm

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