A Novel of Repulsive Function on Artificial Potential Field for Robot Path Planning (original) (raw)

Improvement Of Potential Field Algorithm for Robot Path Planning

2021

In this project, we improved and implemented the Artificial Potential Field Algorithms (APF) for path planning, then the robot uses the path planning to navigate from the starting position to the goal position with avoiding the obstacles collision. The path planning generated by the APF is an optimal path, shortest distance to the goal. We have investigated three scenarios to create the path planning. Forst scenario, generating path planning with one obstacle. The second scenario, generating path planning with two obstacles. The third scenario, generating the path planning with two obstacles but with different parameters of the spread of attraction, the spread of repulsive, the strength of attraction, and the strength of repulsive , then, the optimal path planning among those parameters founded in scenario 3. We tested two local minima and simulated with APF algorithm then we compare the results for the better path planning. An improvement APF has been tested and simulated for the local minima problem

Improvement of Potential Field Algorithm for Path Planning

2021

In this project, we improved and implemented the Artificial Potential Field Algorithms (APF) for path planning, then the robot uses the path planning to navigate from the starting position to the goal position with avoiding the obstacles collision. The path planning generated by the APF is an optimal path, shortest distance to the goal. We have investigated three scenarios to create the path planning. Forst scenario, generating path planning with one obstacle. The second scenario, generating path planning with two obstacles. The third scenario, generating the path planning with two obstacles but with different parameters of the spread of attraction, the spread of repulsive, the strength of attraction, and the strength of repulsive, then, the optimal path planning among those parameters founded in scenario 3. We tested two local minima and simulated with APF algorithm then we compare the results for the better path planning. An improvement APF has been tested and simulated for the local minima problem.

Efficient robotic path planning algorithm based on artificial potential field

International Journal of Electrical and Computer Engineering (IJECE), 2021

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.

Modified artificial potential field method for online path planning applications

2017 IEEE Intelligent Vehicles Symposium (IV), 2017

This paper presents a modified potential field method for robot navigation. The approach overcomes the wellknown artificial potential field (APF) method issue, which is due to local minima that induce the standard APF method to trap in. Thus, the standard APF method is no longer useful in such case. The advantage of the new proposed method, as opposed to those that resort to the global optimization methods, is the low computing time that lines up with the standard A-Star (A*) method. The strategy consists of looking for a practical path in the potential field-according to the potential gradient descent algorithm (PGDA)-and adding a repulsive potential to the current state, in case of blocking configuration, a local minimum. When the PGDA reaches the global minimum, a new potential field will be constructed with only one minimum that matches the final destination of the robot, the global minimum. Finally, to determine the achievable trajectory, a second iteration is performed by the PGDA.

ARTIFICIAL POTENTIAL FIELD APPROACH TO ESCAPE LOCAL MINIMUM PROBLEM USING REGRESSION SEARCH METHOD

Artificial Potential Field (APF) method is one of the simplest and efficient ways for path planning. Although these methods have good performance because of rapid search speed and better search quality as well as safety and smoothness, it sometimes falls in local minima problem, which can trap the robot before reaching its destination. When the robot moves in unknown environment, it cannot predict local minima problem before falling it. The avoidance of this issue has been popular research topic in the field of artificial potential field based path planning. In this work, we propose an approach through adding secondary goals to escape local minima. These secondary goals are located on the two sides of U-shaped obstacles so that the goals are used as an intermediate goal to escape local minima, and create an APF path. We also use the regression search methods for creating optimal path for robot so that it can have shortest distance, and save energy and time. Consequently, it makes an effective motion planner to improve the quality of the trajectory of mobile robot.

New Potential Functions for Mobile Robot Path Planning

This paper first describes the problem of goals nonreachable with obstacles nearby when using potential field methods for mobile robot path planning. Then, new repulsive potential functions are presented by taking the relative distance between the robot and the goal into consideration, which ensures that the goal position is the global minimum of the total potential.

Path Planning of Mobile Robot by using Modified Optimized Potential Field Method

International Journal of Computer Applications, 2015

This paper deals with the navigation of a mobile robot in an unknown environment by using artificial potential field (APF) method. The aim is to develop a method for path planning of mobile robot from start point to the goal point while avoiding obstacles on robot's path. Artificial potential field method will be modified and optimized by using particle swarm optimization (PSO) algorithm to solve the drawbacks such as local minima and improve the quality of the trajectory of mobile robot.

Path planning of humanoids based on artificial potential field method in unknown environments

Expert Systems, 2018

In this paper, an artificial potential field based navigational controller has been developed for motion planning of humanoid robots. Here, NAO robots are used as the humanoid platform using the underlying principles of potential field based method. The movement of the robot is considered to be under a negative gradient scheme by the combined effect of attractive and repulsive forces generated due to target and obstacles, respectively. The working of the controller is tested in a V-REP simulation platform, and the simulation results are validated through a real-time experimental setup developed under laboratory conditions. Here, the navigation of both single and multiple humanoids has been attempted. For avoiding intercollision among multiple humanoids during their navigation in a common platform, a Petri-Net control scheme has been proposed. The results obtained from both the simulation and experimental platforms are compared against each other with a good agreement between them having minimal percentage of deviations. Finally, the proposed controller is also evaluated against another existing navigational model, and a significant performance improvement has been observed. KEYWORDS artificial potential field, humanoid NAO, path planning, V-REP 1 | INTRODUCTION With increasing use in demanding sectors such as industrial automation and manufacturing, humanoids have become the centre of attraction for many researchers dealing with robotic fields. The need of building an autonomous behaviour for path planning and navigation into a robot whether it is a mobile robot or a humanoid is of utmost importance. Humanoid navigation is different from mobile robot navigation in joint movements (Kumar, Pandey, Sahu, Chhotray, & Parhi, 2017; Kumar, Kumar, & Parhi, 2018). Therefore, it demands special attention towards careful selection of navigational parameters. In order to achieve this objective, researchers have made use of different classical and reactive techniques for navigational purposes (Kumar, Sahu, Parhi, Pandey et al., 2018). In real world, the robot will come across static as well as dynamic surrounding, which means the obstacles may be stationary or moving or both. The target seeking and obstacle avoidance behaviour should be built in such a way that robot follows the shortest possible path directed towards the target avoiding all hindrances. The path planning approaches can be classified into local and global path planning (Kunchev, Jain, Ivancevic, & Finn, 2006; Mahajan & Marbate, 2013). In local path planning, the surrounding environment is unknown to the robot whereas, in global path planning, the robot is already aware of its surroundings (Kumar, Sahu, & Parhi 2018). The environment can be static or dynamic in nature in both the above-mentioned cases. Similarly, the algorithms used for navigation can be classified as classical approaches such as roadmap building, cell decomposition method, and artificial potential field (APF) method and reactive approaches, namely fuzzy logic, neural networks, neuro-fuzzy systems, and various bioinspired techniques. The application of potential field method is very common for mobile robot navigation (

Obstacle avoidance of mobile robots using modified artificial potential field algorithm

EURASIP Journal on Wireless Communications and Networking

In recent years, topics related to robotics have become one of the researching fields. In the meantime, intelligent mobile robots have great acceptance, but the control and navigation of these devices are very difficult, and the lack of dealing with fixed obstacles and avoiding them, due to safe and secure routing, is the basic requirement of these systems. In this paper, the modified artificial potential field (APF) method is proposed for that robot avoids collision with fixed obstacles and reaches the target in an optimal path; using this algorithm, the robot can run to the target in optimal environments without any problems by avoiding obstacles, and also using this algorithm, unlike the APF algorithm, the robot does not get stuck in the local minimum. We are looking for an appropriate cost function, with restrictions that we have, and the goal is to avoid obstacles, achieve the target, and do not stop the robot in local minimum. The previous method, APF algorithm, has advantages, such as the use of a simple math model, which is easy to understand and implement. However, this algorithm has many drawbacks; the major drawback of this problem is at the local minimum and the inaccessibility of the target when the obstacles are in the vicinity of the target. Therefore, in order to obtain a better result and to improve the shortcomings of the APF algorithm, this algorithm needs to be improved. Here, the obstacle avoidance planning algorithm is proposed based on the improvement of the artificial potential field algorithm to solve this local minimum problem. In the end, simulation results are evaluated using MATLAB software. The simulation results show that the proposed method is superior to the existing solution.

Robot Manipulator Path Planning Based on Intelligent Multi-resolution Potential Field

International Journal of u- and e-Service, Science and Technology, 2015

Robot path planning is an important part of the development of autonomous systems. Numerous strategies have been proposed in the literature regarding mobile robots but trajectory planning for manipulators is considerably more difficult since the entire structure can move and therefore produce collisions with surrounding obstacles. This paper presents an original solution and analytical comparison to path planning for manipulator arms. Path planning is executed in two parts: first, a global path is found to guide the end-effector in the environment using artificial potential fields and multi-resolution occupancy grids, then, a local path is determined for the entire robot structure by considering the kinematics of the robot as well as the repulsive forces of nearby obstacles in a fuzzy logic controller. Results are shown from a simulator that has been built for this purpose. The contribution of this research is to develop a robust solution for path planning with collision avoidance: one that can be used for various manipulator arms and environment configurations.