The Research on Path Planning Algorithm of RoboCup Based on PFNPGA

2010 ◽  
Vol 20-23 ◽  
pp. 1192-1198
Author(s):  
Xian Yi Cheng ◽  
Qian Zhu ◽  
Zhen Wen Zhang
Keyword(s):  
Path Planning ◽  
Optimization Problem ◽  
Planning Process ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
The Poor ◽  
Starting Point ◽  
Planning Methods ◽  
Planning Algorithm ◽  
Path Planning Algorithm

To improve the poor efficiency in path planning that caused by not taking RoboCup’s stamina, character, dynamic starting point, dynamic endpoint and other factors into consideration in the path planning process, the RoboCup path planning is generalized as a multi-objective optimization problem in the paper, and proposes RoboCup’s sport model with dynamic multi-objective path planning which is based on RoboCup’s stamina triple model, and a path planning algorithm that is suited for RoboCup is advanced based on PFNPGA ( Penalty Function Niche Pareto Genetic Algorithm）. The experiment in a real environment shows that, by comparing with traditional path planning methods, the algorithm in the paper can get more reasonable path at the premise of guarantee RoboCup have relative high stamina values.

scholarly journals Development and Implementation of Spline-based Path Planning Algorithm in ROS/Gazebo Environment

2019 ◽  
Vol 18 (1) ◽  
pp. 57-84 ◽  
Author(s):  
Lavrenov Lavrenov ◽  
Evgeni Magid ◽  
Matsuno Fumitoshi ◽  
Mikhail Svinin ◽  
Jackrit Suthakorn
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Cost Function ◽  
Working Environment ◽  
Autonomous Mobile Robots ◽  
Dynamic Tuning ◽  
Starting Point ◽  
Geometric Point ◽  
Planning Algorithm ◽  
Path Planning Algorithm

Path planning for autonomous mobile robots is an important task within robotics field. It is common to use one of the two classical approaches in path planning: a global approach when an entire map of a working environment is available for a robot or local methods, which require the robot to detect obstacles with a variety of onboard sensors as the robot traverses the environment. In our previous work, a multi-criteria spline algorithm prototype for a global path construction was developed and tested in Matlab environment. The algorithm used the Voronoi graph for computing an initial path that serves as a starting point of the iterative method. This approach allowed finding a path in all map configurations whenever the path existed. During the iterative search, a cost function with a number of different criteria and associated weights was guiding further path optimization. A potential field method was used to implement some of the criteria. This paper describes an implementation of a modified spline-based algorithm that could be used with real autonomous mobile robots. Equations of the characteristic criteria of a path optimality were further modified. The obstacle map was previously presented as intersections of a finite number of circles with various radii. However, in real world environments, obstacles’ data is a dynamically changing probability map that could be based on an occupancy grid. Moreover, the robot is no longer a geometric point. To implement the spline algorithm and further use it with real robots, the source code of the Matlab environment prototype was transferred into C++ programming language. The testing of the method and the multi criteria cost function optimality was carried out in ROS/Gazebo environment, which recently has become a standard for programming and modeling robotic devices and algorithms. The resulting spline-based path planning algorithm could be used on any real robot, which is equipped with a laser rangefinder. The algorithm operates in real time and the influence of the objective function criteria parameters are available for dynamic tuning during a robot motion.

Multi-objective Evolutive Multi-thread Path-Planning Algorithm Considering Real-Time Extreme Weather Events

2019 ◽  
pp. 137-146
Author(s):  
Alfredo Cristóbal-Salas ◽  
Andrei Tchernykh ◽  
Sergio Nesmachnow ◽  
Bardo Santiago-Vicente ◽  
Raúl Alejandro Luna-Sánchez ◽  
...  
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Multi Objective ◽  
Weather Events ◽  
Planning Algorithm ◽  
Path Planning Algorithm

scholarly journals Autonomous Robots Path Planning: An Adaptive Roadmap Approach

2013 ◽  
Vol 373-375 ◽  
pp. 246-254 ◽  
Author(s):  
Mohamed Elbanhawi ◽  
Milan Simic ◽  
Reza Jazar
Keyword(s):  
Computational Complexity ◽  
Path Planning ◽  
Autonomous Robots ◽  
Autonomous Mobile Robots ◽  
Current Position ◽  
Military Applications ◽  
Planning Methods ◽  
Planning Algorithm ◽  
Real Time Applications ◽  
Path Planning Algorithm

Developing algorithms that allow robots to independently navigate unknown environments is a widely researched area of robotics. The potential for autonomous mobile robots use, in industrial and military applications, is boundless. Path planning entails computing a collision free path from a robots current position to a desired target. The problem of path planning for these robots remains underdeveloped. Computational complexity, path optimization and robustness are some of the issues that arise. Current algorithms do not generate general solutions for different situations and require user experience and optimization. Classical algorithms are computationally extensive. This reduces the possibility of their use in real time applications. Additionally, classical algorithms do not allow for any control over attributes of the generated path. A new roadmap path planning algorithm is proposed in this paper. This method generates waypoints, through which the robot can avoid obstacles and reach its goal. At the heart of this algorithm is a method to control the distance of the waypoints from obstacles, without increasing its computational complexity. Several simulations were run to illustrate the robustness and adaptability of this approach, compared to the most commonly used path planning methods.

Non-Nominal Path Planning of Assembly Processes

2005 ◽  
Author(s):  
Johan S. Carlson ◽  
Rikard So¨derberg ◽  
Robert Bohlin ◽  
Lars Lindkvist ◽  
Tomas Hermansson
Keyword(s):  
Path Planning ◽  
Automotive Industry ◽  
Planning Process ◽  
State Of The Art ◽  
Cost Effective ◽  
Worst Case ◽  
Positioning Systems ◽  
Planning Algorithm ◽  
On Line ◽  
Path Planning Algorithm

One important aspect in the assembly process design is to assure that there exist a collision-free assembly path for each part and subassembly. In order to reduce the need of physical verification the automotive industry use digital mock-up tool with collision checking for this kind of geometrical assembly analysis. To manually verify assembly feasibility in a digital mock-up tool can be hard and time consuming. Therefore, the recent development of efficient and effective automatic path planning algorithm and tools are highly motivated. However, in real production, all equipment, parts and subassemblies are inflicted by geometrical variation, often resulting in conflicts and on-line adjustments of off-line generated assembly paths. To avoid problems with on-line adjustments, state-of-the-art tools for path-planning can handle tolerances by a general clearance for all geometry. This is a worst-case strategy, not taking account for how part and assembly variation propagates through the positioning systems of the assembly resulting in geometry areas of both high and low degree of variation. Since, this latter approach results in unnecessary design changes or in too tight tolerances we have developed a new algorithm and working procedure enabling and supporting a more cost effective non-nominal path planning process for assembly operations. The basic idea of the paper is to combine state of the art technology within variation simulation and automatic path planning. By integrating variation and tolerance simulation results into the path planning algorithm we can allow the assembly path going closer to areas of low variation, while avoiding areas of high variation. The benefits of the proposed approach are illustrated on an industrial case from the automotive industry.

scholarly journals Efficient robotic path planning algorithm based on artificial potential field

2021 ◽  
Vol 11 (6) ◽  
pp. 4840
Author(s):  
Elia Nadira Sabudin ◽  
Rosli Omar ◽  
Sanjoy Kumar Debnath ◽  
Muhammad Suhaimi Sulong
Keyword(s):  
Computational Complexity ◽  
Path Planning ◽  
Potential Field ◽  
Path Length ◽  
Computation Time ◽  
Cell Decomposition ◽  
Target Point ◽  
Planning Methods ◽  
Planning Algorithm ◽  
Path Planning Algorithm

<span lang="EN-US">Path planning is crucial for a robot to be able to reach a target point safely to accomplish a given mission. In path planning, three essential criteria have to be considered namely path length, computational complexity and completeness. Among established path planning methods are voronoi diagram (VD), cell decomposition (CD), probability roadmap (PRM), visibility graph (VG) and potential field (PF). The above-mentioned methods could not fulfill all three criteria simultaneously which limits their application in optimal and real-time path planning. This paper proposes a path PF-based planning algorithm called dynamic artificial PF (DAPF). The proposed algorithm is capable of eliminating the local minima that frequently occurs in the conventional PF while fulfilling the criterion of path planning. DAPF also integrates path pruning to shorten the planned path. In order to evaluate its performance, DAPF has been simulated and compared with VG in terms of path length and computational complexity. It is found that DAPF is consistent in generating paths with low computation time in obstacle-rich environments compared to VG. The paths produced also are nearly optimal with respect to VG.</span>

Multi-scale theta* algorithm for the path planning of unmanned surface vehicle

2021 ◽  
pp. 147509022110396
Author(s):  
Xu Han ◽  
Xianku Zhang
Keyword(s):  
Path Planning ◽  
Planning Process ◽  
Optimal Path ◽  
Long Distance ◽  
Unmanned Surface Vehicle ◽  
Safe Navigation ◽  
Multi Scale ◽  
Fold Reduction ◽  
Planning Algorithm ◽  
Path Planning Algorithm

Theta* algorithm is a searching-based path planning algorithm that gives an optimal path with more flexibility on route angle than A* method. The dynamics of USV is characterized by large inertia, so that larger turning angle is preferred. In view of the shortcomings of traditional Theta* algorithm, such as being hard to balance overall situation and details in long-distance planning, and lacking of waypoint replacement scheme when a waypoint is unreachable, it is inappropriate to make long-distance planning through Theta* algorithm directly. In order to ensure safe navigation for unmanned surface vehicle (USV), this paper proposes a multi-scale Theta* algorithm to solve these defects. Simulation result manifests the proposed scheme can provide a path clear of obstacles with several fold reduction in time consumption. The proposed planning method simplifies path planning process and contributes to the development of marine transportation.

Investigating Reduced Path Planning Strategy for Differential Wheeled Mobile Robot

Robotica ◽  
2019 ◽  
Vol 38 (2) ◽  
pp. 235-255 ◽  
Author(s):  
Raouf Fareh ◽  
Mohammed Baziyad ◽  
Mohammad H. Rahman ◽  
Tamer Rabie ◽  
Maamar Bettayeb
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Control Strategy ◽  
Wheeled Mobile Robot ◽  
Computational Time ◽  
Planning Strategy ◽  
Starting Point ◽  
Planning Algorithm ◽  
Path Efficiency ◽  
Path Planning Algorithm

SummaryThis paper presents a vision-based path planning strategy that aims to reduce the computational time required by a robot to find a feasible path from a starting point to the goal point. The proposed algorithm presents a novel strategy that can be implemented on any well-known path planning algorithm such as A*, D* and probabilistic roadmap (PRM), to improve the swiftness of these algorithms. This path planning algorithm is suitable for real-time scenarios since it reduces the computational time compared to the basis and traditional algorithms. To test the proposed path planning strategy, a tracking control strategy is implemented on a mobile platform. This control strategy consists of three major stages. The first stage deals with gathering information about the surrounding environment using vision techniques. In the second stage, a free-obstacle path is generated using the proposed reduced scheme. In the final stage, a Lyapunov kinematic tracking controller and two Artificial Neural Network (ANN) based-controllers are implemented to track the proposed path by adjusting the rotational and linear velocity of the robot. The proposed path planning strategy is tested on a Pioneer P3-DX differential wheeled mobile robot and an Xtion PRO depth camera. Experimental results prove the efficiency of the proposed path planning scheme, which was able to reduce the computational time by a large percentage which reached up to 88% of the time needed by the basis and traditional scheme, without significant adverse effect on the workability of the basis algorithm. Moreover, the proposed path planning algorithm has improved the path efficiency, in terms of the path length and trackability, challenging the traditional trade-off between swiftness and path efficiency.

scholarly journals Minimum Energy Demand Locomotion on Space Station

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Wing Kwong Chung ◽  
Yangsheng Xu
Keyword(s):  
Path Planning ◽  
Energy Demand ◽  
Planning Process ◽  
Minimum Energy ◽  
Space Station ◽  
Space Manipulators ◽  
Planning Algorithm ◽  
Precious Resource ◽  
Path Planning Algorithm ◽  
Moving Path

The energy of a space station is a precious resource, and the minimization of energy consumption of a space manipulator is crucial to maintain its normal functionalities. This paper first presents novel gaits for space manipulators by equipping a new gripping mechanism. With the use of wheels locomotion, lower energy demand gaits can be achieved. With the use of the proposed gaits, we further develop a global path planning algorithm for space manipulators which can plan a moving path on a space station with a minimum total energy demand. Different from existing approaches, we emphasize both the use of the proposed low energy demand gaits and the gaits composition during the path planning process. To evaluate the performance of the proposed gaits and path planning algorithm, numerous simulations are performed. Results show that the energy demand of both the proposed gaits and the resultant moving path is also minimum.

scholarly journals Path Planning for Autonomous Mobile Robots: A Review

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7898
Author(s):  
José Ricardo Sánchez-Ibáñez ◽  
Carlos J. Pérez-del-Pulgar ◽  
Alfonso García-Cerezo
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Ground Vehicles ◽  
Comprehensive Overview ◽  
Starting Point ◽  
Planning Algorithm ◽  
Human Operators ◽  
Global Classification ◽  
Path Planning Algorithm

Providing mobile robots with autonomous capabilities is advantageous. It allows one to dispense with the intervention of human operators, which may prove beneficial in economic and safety terms. Autonomy requires, in most cases, the use of path planners that enable the robot to deliberate about how to move from its location at one moment to another. Looking for the most appropriate path planning algorithm according to the requirements imposed by users can be challenging, given the overwhelming number of approaches that exist in the literature. Moreover, the past review works analyzed here cover only some of these approaches, missing important ones. For this reason, our paper aims to serve as a starting point for a clear and comprehensive overview of the research to date. It introduces a global classification of path planning algorithms, with a focus on those approaches used along with autonomous ground vehicles, but is also extendable to other robots moving on surfaces, such as autonomous boats. Moreover, the models used to represent the environment, together with the robot mobility and dynamics, are also addressed from the perspective of path planning. Each of the path planning categories presented in the classification is disclosed and analyzed, and a discussion about their applicability is added at the end.

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