Local Information using Stereo Camera in Artificial Potential Field based Path Planning (original) (raw)
Related papers
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.
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.
Local Autonomous Robot Navigation Using Potential Fields
Motion Planning, 2008
The potential fields method for autonomous robot navigation consists essentially in the assignment of an attractive potential to the goal point and a repulsive potential to each of the obstacles in the environment. Several implementations of potential fields for autonomous robot navigation have been reported. The most simple implementation considers a known environment where fixed potentials can be assigned to the goal and the obstacles. When the obstacles are unknown the potential fields have to be adapted as the robot advances, and detects new obstacles. The implementation of the potential fields method with one attraction potential assigned to the goal point and fixed repulsion points assigned to the obstacles, has the important limitation that for some obstacle configurations it may not be possible to produce appropriate resultant forces to avoid the obstacles. Recently the use of several adjustable attraction points, and the progressive insertion of repulsion points as obstacles are detected online, have proved to be a viable method to avoid large obstacles using potential fields in environments with unknown obstacles. In this chapter we present the main characteristics of the different approaches to implement local robot navigation algorithms using potential fields for known and partially known environments. Different strategies to escape from local minima, that occur when the attraction and repulsion forces cancel each other, are also considered. www.intechopen.com Mobile Robots Motion Planning, New Challenges 2 approaches to recover from local minima of the potential field. During the chapter we have only considered potential fields defined in cartesian space, where attractive or repulsive potentials are a function of the position of the target or the obstacle. Recently, potential fields defined in a 2D trajectory space, using the path curvature and longitudinal robot velocity, have been reported (Shimoda et al., 2005). 1.1 Previous works on artificial potential fields for autonomous robot navigation Artificial potential fields for autonomous robot navigation were first proposed by Khatib (1990). The main idea is to generate attraction and repulsion forces within the working environment of the robot to guide it to the target. The target point has an attractive influence on the robot and each obstacle tends to push away the robot, in order to avoid collisions. Potential field methods provide an elegant solution to the path finding problem. Since the path is the result of the interaction of appropriate force fields, the path finding problem becomes a search for optimum field configurations instead of the direct construction (e.g. using rules) of an optimum path. Different approaches have been taken to calculate appropriate field configurations. Vadakkepat et al. (2000) report the development of a genetic algorithm (GA) for autonomous robot navigation based on artificial potential fields. Repulsion forces are assigned to obstacles in the environment and attraction forces are assigned to the target point. The GA adjusts the constants in the force functions. Multiobjective optimisation is performed on 3 functions which measure each: error to the target point, number of collisions along a candidate path, and total path length. This scheme requires a priory knowledge of the obstacle positions in order the evaluate the number of collisions through each candidate path. Kun Hsiang et al. (1999), report the development of an autonomous robot navigation scheme based on potential fields and the chamfer distance transform for global path planning in a known environment, and a local fuzzy logic controller to avoid trap situations. Simulation and experimental results on a real AGV are reported for a simple (4 obstacles) and known environment. McFetridge and Ibrahim (1998) report the development of a robot navigation scheme based on artificial potential fields and fuzzy rules. The main contribution of the work consists in the use of a variable for the evaluation of the importance of each obstacle in the path of the robot. Simulation results on a very simple environment (one obstacle) show that use of the importance variable produces smoother and shorter trajectories. Ge and Cui (2002) describe a motion planning scheme for mobile robots in dynamic environments, with moving obstacles and target point. They use potential field functions which have terms that measure the relative velocity between the robot and the target or obstacle. The main disadvantage of artificial potential field methods is its susceptibility to local minima (Borenstein and Koren, 1991), (Grefenstette and Schultz, 1994). Since the objective function for path evaluation is usually a multimodal function of a large number of variables. Additionally, in the majority of works on artificial potential fields for robot navigation, a single attraction point has been used. This approach can be unable to produce the resultant forces required to avoid a large or several, closely spaced, obstacles (Koren and Borenstein, 1991). An scheme based on a fixed target attraction point and several, moving, auxiliary attractions points was reported in Arámbula and Padilla (2004). Multiple auxiliary attractions points with adjustable position and force intensity enable navigation around large obstacles, as well as through closely spaced obstacles, at the cost of increased www.intechopen.com
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.
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 (
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.
Path-Planning Solution Based on a Potential Field Non Uniformly Distributed
Robot Control 1991, 1992
Using robots in an environment with obstacles, implies a previous planning of all their ~ovements in .order to avoid possible collisions. To solve this problem, called path-planning. different solutions have been used: Slides, Octrees, Potential Field or V-graph. The methods based on the potential field present advantages because of the simplicity of the algorithm formulation and its implementation. Verbeek proposes the use of the "constrained distance transformation" to solve the path planing problem for a mobile robot in a two dimensional space. This solution can be extended to three or more DOF with a high cost-in time and memory. In this paper, we propose a new method the "self-adjustable filter", based on the convolution filtering concepts and using the octrees concepts to minimize the amount of the memory and the time employed. The method has been applied for a three DOF static robot, using the Cspace concepts in order to consider the possible collision with all the robot's arms. The results spends less time and memory, to obtain a solution with the same precision. This work is included in the EUREKA project "Advanced Mobile Robots for public safety applications". Keywords Robots; collision avoidance, path planning, potential field INTRODUcnON When a robot operates in an environment with obstacles it is necessary to make previous decisions about which trajectory the robot must move along. This problem known by path planning or more exactly by collision avoidance, has been studied by several authors using different methodologies.
International Journal of Robotics and Control Systems
Mobile robots need path-planning abilities to achieve a collision-free trajectory. Obstacles between the robot and the goal position must be passed without crashing into them. The Artificial Potential Field (APF) algorithm is a method for robot path planning that is usually used to control the robot for avoiding obstacles in front of the robot. The APF algorithm consists of an attractive potential field and a repulsive potential field. The attractive potential fields work based on the predetermined goals that are generated to attract the robot to achieve the goal position. Apart from it, the obstacle generates a repulsive potential field to push the robot away from the obstacle. The robot's localization in producing the robot's position is generated by the differential drive kinematic equations of the mobile robot based on encoder and gyroscope data. In addition, the mapping of the robot's work environment is embedded in the robot's memory. According to the experimen...
Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications
In this work, a mobile robot path-planning algorithm based on the evolutionary artificial potential field (EAPF) for non-static environments is presented. With the aim to accelerate the path planning computation, the EAPF algorithm is implemented employing novel parallel computing architectures. The EAPF algorithm is capable of deriving optimal potential field functions using evolutionary computation to generate accurate and efficient paths to drive a mobile robot from the start point to the goal point without colliding with obstacles in static and non-static environments. The algorithm allows parallel implementation to accelerate the computation to obtain better results in a reasonable runtime. Comparative performance analysis in terms of path length and computation time is provided. The experiments were specifically designed to show the effectiveness and the efficiency of the mobile robot path-planning algorithm based on the EAPF in a sequential implementation on CPU, a parallel implementation on CPU, and a parallel implementation on GPU.