Path Planning Based on Fuzzy Decision Trees and Potential Field

2016 ◽  
Vol 6 (1) ◽  
pp. 212 ◽  
Author(s):  
Iswanto Iswanto ◽  
Oyas Wahyunggoro ◽  
Adha Imam Cahyadi
Keyword(s):  
Path Planning ◽  
Shortest Path ◽  
Potential Field ◽  
Planning Problem ◽  
Local Minima ◽  
Fuzzy Decision ◽  
Fuzzy Logic Algorithm ◽  
Planning Decision ◽  
Fuzzy Decision Trees ◽  
Available Information

The fuzzy logic algorithm is an artificial intelligence algorithm that uses mathematical logic to solve to by the data value inputs which are not precise in order to reach an accurate conclusion. In this work, Fuzzy decision tree (FDT) has been designed to solve the path planning problem by considering all available information and make the most appropriate decision given by the inputs. The FDT is often used to make a path planning decision in graph theory. It has been applied in the previous researches in the field of robotics, but it still shows drawbacks in that the robot will stop at the local minima and is not able to find the shortest path. Hence, this paper combines the FDT algorithm with the potential field algorithm. The potential field algorithm provides weight to the FDT algorithm which enables the robot to successfully avoid the local minima and find the shortest path.

scholarly journals Path Planning Based on Fuzzy Decision Trees and Potential Field

2016 ◽  
Vol 6 (1) ◽  
pp. 212
Author(s):  
Iswanto Iswanto ◽  
Oyas Wahyunggoro ◽  
Adha Imam Cahyadi
Keyword(s):  
Path Planning ◽  
Shortest Path ◽  
Potential Field ◽  
Planning Problem ◽  
Local Minima ◽  
Fuzzy Decision ◽  
Fuzzy Logic Algorithm ◽  
Planning Decision ◽  
Fuzzy Decision Trees ◽  
Available Information

The fuzzy logic algorithm is an artificial intelligence algorithm that uses mathematical logic to solve to by the data value inputs which are not precise in order to reach an accurate conclusion. In this work, Fuzzy decision tree (FDT) has been designed to solve the path planning problem by considering all available information and make the most appropriate decision given by the inputs. The FDT is often used to make a path planning decision in graph theory. It has been applied in the previous researches in the field of robotics, but it still shows drawbacks in that the robot will stop at the local minima and is not able to find the shortest path. Hence, this paper combines the FDT algorithm with the potential field algorithm. The potential field algorithm provides weight to the FDT algorithm which enables the robot to successfully avoid the local minima and find the shortest path.

Mechanical arm obstacle avoidance path planning based on improved artificial potential field method

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tianying Xu ◽  
Haibo Zhou ◽  
Shuaixia Tan ◽  
Zhiqiang Li ◽  
Xia Ju ◽  
...  
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Planning Process ◽  
Field Method ◽  
Artificial Potential Field ◽  
Local Minima ◽  
Content Type ◽  
Potential Field Method ◽  
Artificial Potential Field Method

Purpose This paper aims to resolve issues of the traditional artificial potential field method, such as falling into local minima, low success rate and lack of ability to sense the obstacle shapes in the planning process. Design/methodology/approach In this paper, an improved artificial potential field method is proposed, where the object can leave the local minima point, where the algorithm falls into, while it avoids the obstacle, following a shorter feasible path along the repulsive equipotential surface, which is locally optimized. The whole obstacle avoidance process is based on the improved artificial potential field method, applied during the mechanical arm path planning action, along the motion from the starting point to the target point. Findings Simulation results show that the algorithm in this paper can effectively perceive the obstacle shape in all the selected cases and can effectively shorten the distance of the planned path by 13%–41% with significantly higher planning efficiency compared with the improved artificial potential field method based on rapidly-exploring random tree. The experimental results show that the improved artificial potential field method can effectively plan a smooth collision-free path for the object, based on an algorithm with good environmental adaptability. Originality/value An improved artificial potential field method is proposed for optimized obstacle avoidance path planning of a mechanical arm in three-dimensional space. This new approach aims to resolve issues of the traditional artificial potential field method, such as falling into local minima, low success rate and lack of ability to sense the obstacle shapes in the planning process.

Path Planning of Mobile Robot Based on Improved Artificial Potential Field Method

2014 ◽  
Vol 644-650 ◽  
pp. 154-157 ◽  
Author(s):  
Su Ying Zhang ◽  
Yan Kai Shen ◽  
Wen Shuai Cui
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Potential Field ◽  
High Efficiency ◽  
Field Method ◽  
Artificial Potential Field ◽  
Local Minima ◽  
Potential Field Method ◽  
Smooth Path ◽  
Artificial Potential Field Method

The artificial potential field method has been extensively used in mobile robot path planning for its characteristics of simpleness, high efficiency, and smooth path. In this paper, to solve the problem of local minima in traditional artificial potential field method, A modified form of repulsion function is proposed. A detour force is added to the repulsion function, the problem of local minima can be solved effectively. In the end, with the help of Matlab software simulating, the result shows that this method is simple and effective.

scholarly journals Socially Compliant Path Planning for Robotic Autonomous Luggage Trolley Collection at Airports

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2759 ◽  
Author(s):  
Jiankun Wang ◽  
Max Q.-H. Meng
Keyword(s):  
Path Planning ◽  
Potential Field ◽  
Dimensional Space ◽  
Optimal Path ◽  
High Dimensional ◽  
Force Model ◽  
Planning Problem ◽  
Self Organizing Map ◽  
Social Force ◽  
The Social

This paper describes a socially compliant path planning scheme for robotic autonomous luggage trolley collection at airports. The robot is required to efficiently collect all assigned luggage trolleys in a designated area, while avoiding obstacles and not offending the pedestrians. This path planning problem is formulated in this paper as a Traveling Salesman Problem (TSP). Different from the conventional solutions to the TSP, in which the Euclidean distance between two sites is used as the metric, a high-dimensional metric including the factor of pedestrians’ feelings is applied in this work. To obtain the new metric, a novel potential function is firstly proposed to model the relationship between the robot, luggage trolleys, obstacles, and pedestrians. The Social Force Model (SFM) is utilized so that the pedestrians can bring extra influence on the potential field, different from ordinary obstacles. Directed by the attractive and repulsive force generated from the potential field, a number of paths connecting the robot and the luggage trolley, or two luggage trolleys, can be obtained. The length of the generated path is considered as the new metric. The Self-Organizing Map (SOM) satisfies the job of finding a final path to connect all luggage trolleys and the robot located in the potential field, as it can find the intrinsic connection in the high dimensional space. Therefore, while incorporating the new metric, the SOM is used to find the optimal path in which the robot can collect the assigned luggage trolleys in sequence. As a demonstration, the proposed path planning method is implemented in simulation experiments, showing an increase of efficiency and efficacy.

scholarly journals Artificial Potential Field with Discrete Map Transformation for Feasible Indoor Path Planning

2020 ◽  
Vol 10 (24) ◽  
pp. 8987
Author(s):  
Muhammad Zulfaqar Azmi ◽  
Toshio Ito
Keyword(s):  
Path Planning ◽  
Potential Field ◽  
Indoor Navigation ◽  
Artificial Potential Field ◽  
Planning Problem ◽  
Personal Mobility ◽  
Personal Mobility Vehicle ◽  
Infinite Loop ◽  
Path Planning Problem ◽  
Loop Problem

This work considers the path planning problem of personal mobility vehicle (PMV) for indoor navigation using the Artificial Potential Field (APF) method. The APF method sometimes suffers from an infinite loop problem during the planning phase when the goal is blocked by obstacles with certain characteristics. To address the issue, this study deploys the map augmentation method for replanning. When infinite loop situations occur, the map is transformed and the search for drivable path is initiated. The method successfully generates a feasible trajectory when the map is rotated at a certain angle. The scenario of successful planning is shown in the result.

scholarly journals Improved Artificial Potential Field Method Applied for AUV Path Planning

2020 ◽  
Vol 2020 ◽  
pp. 1-21 ◽  
Author(s):  
Xiaojing Fan ◽  
Yinjing Guo ◽  
Hui Liu ◽  
Bowen Wei ◽  
Wenhong Lyu
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Potential Field ◽  
Moving Objects ◽  
Dynamic Environment ◽  
Planning Method ◽  
Artificial Potential Field ◽  
Local Minima ◽  
Field Function ◽  
Time Path

With the topics related to the intelligent AUV, control and navigation have become one of the key researching fields. This paper presents a concise and reliable path planning method for AUV based on the improved APF method. AUV can make the decision on obstacle avoidance in terms of the state of itself and the motion of obstacles. The artificial potential field (APF) method has been widely applied in static real-time path planning. In this study, we present the improved APF method to solve some inherent shortcomings, such as the local minima and the inaccessibility of the target. A distance correction factor is added to the repulsive potential field function to solve the GNRON problem. The regular hexagon-guided method is proposed to improve the local minima problem. Meanwhile, the relative velocity method about the moving objects detection and avoidance is proposed for the dynamic environment. This method considers not only the spatial location but also the magnitude and direction of the velocity of the moving objects, which can avoid dynamic obstacles in time. So the proposed path planning method is suitable for both static and dynamic environments. The virtual environment has been built, and the emulation has been in progress in MATLAB. Simulation results show that the proposed method has promising feasibility and efficiency in the AUV real-time path planning. We demonstrate the performance of the proposed method in the real environment. Experimental results show that the proposed method is capable of avoiding the obstacles efficiently and finding an optimized path.

A Novel Approach for Path Planning Based on Reactive Behavior-Artificial Potential Filed

2014 ◽  
Vol 529 ◽  
pp. 646-649 ◽  
Author(s):  
Long Xiang Yang ◽  
Zai Xin Liu ◽  
Hao Tang
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Real Time ◽  
Potential Field ◽  
Artificial Potential Field ◽  
Optimal Selection ◽  
Local Minima ◽  
Novel Approach ◽  
Reactive Behavior ◽  
Real Time Application

The main objective of this paper is to focus on the local minima and the GNRON issues encountered in path planning by the artificial potential field (APF) method, and propose a novel approach to plan a trajectory adaptive for the environment that the obstacles are randomly distributed. By introducing the ideas of reactive behaviors (RB), the RB-APF method is presented, which combines the efficiency of the APF with the simplicity of the RB, so it can be suitable for real-time application in mobile robots. In this algorithm (RB-APF), the switch conditions and optimal selection equations are reasonably designed with the consideration of the different circumstances of the robot located in. Simulations are presented and the results further demonstrate that the proposed approach is applicable for the environment that obstacles are randomly distributed.

scholarly journals A New Online Random Particles Optimization Algorithm for Mobile Robot Path Planning in Dynamic Environments

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Behrang Mohajer ◽  
Kourosh Kiani ◽  
Ehsan Samiei ◽  
Mostafa Sharifi
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimization Algorithm ◽  
Potential Field ◽  
Optimal Path ◽  
Target Position ◽  
Path Selection ◽  
Artificial Potential Field ◽  
Planning Problem ◽  
Robot Path

A new algorithm named random particle optimization algorithm (RPOA) for local path planning problem of mobile robots in dynamic and unknown environments is proposed. The new algorithm inspired from bacterial foraging technique is based on particles which are randomly distributed around a robot. These particles search the optimal path toward the target position while avoiding the moving obstacles by getting help from the robot’s sensors. The criterion of optimal path selection relies on the particles distance to target and Gaussian cost function assign to detected obstacles. Then, a high level decision making strategy will decide to select best mobile robot path among the proceeded particles, and finally a low level decision control provides a control signal for control of considered holonomic mobile robot. This process is implemented without requirement to tuning algorithm or complex calculation, and furthermore, it is independent from gradient base methods such as heuristic (artificial potential field) methods. Therefore, in this paper, the problem of local mobile path planning is free from getting stuck in local minima and is easy computed. To evaluate the proposed algorithm, some simulations in three various scenarios are performed and results are compared by the artificial potential field.

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