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1 – 10 of over 19000Tianying Xu, Haibo Zhou, Shuaixia Tan, Zhiqiang Li, Xia Ju and Yichang Peng
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…
Abstract
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.
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The results of obstacle avoidance path planning for the manipulator using artificial potential field (APF) method contain a large number of path nodes, which reduce the efficiency…
Abstract
Purpose
The results of obstacle avoidance path planning for the manipulator using artificial potential field (APF) method contain a large number of path nodes, which reduce the efficiency of manipulators. This paper aims to propose a new intelligent obstacle avoidance path planning method for picking robot to improve the efficiency of manipulators.
Design/methodology/approach
To improve the efficiency of the robot, this paper proposes a new intelligent obstacle avoidance path planning method for picking robot. In this method, we present a snake-tongue algorithm based on slope-type potential field and combine the snake-tongue algorithm with genetic algorithm (GA) and reinforcement learning (RL) to reduce the path length and the number of path nodes in the path planning results.
Findings
Simulation experiments were conducted with tomato string picking manipulator. The results showed that the path length is reduced from 4.1 to 2.979 m, the number of nodes is reduced from 31 to 3 and the working time of the robot is reduced from 87.35 to 37.12 s, after APF method combined with GA and RL.
Originality/value
This paper proposes a new improved method of APF, and combines it with GA and RL. The experimental results show that the new intelligent obstacle avoidance path planning method proposed in this paper is beneficial to improve the efficiency of the robotic arm.
Graphical abstract
Figure 1 According to principles of bionics, we propose a new path search method, snake-tongue algorithm, based on a slope-type potential field. At the same time, we use genetic algorithm to strengthen the ability of the artificial potential field method for path searching, so that it can complete the path searching in a variety of complex obstacle distribution situations with shorter path searching results. Reinforcement learning is used to reduce the number of path nodes, which is good for improving the efficiency of robot work. The use of genetic algorithm and reinforcement learning lays the foundation for intelligent control.
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Tao Zhang, Yi Zhu and Jingyan Song
The purpose of this paper is to focus on the local minima issue encountered in motion planning by the artificial potential field (APF) method, investigate the currently existing…
Abstract
Purpose
The purpose of this paper is to focus on the local minima issue encountered in motion planning by the artificial potential field (APF) method, investigate the currently existing approaches and analyze four types of previous methods. Based on the conclusions of analysis, this paper presents an improved wall‐following approach for real‐time application in mobile robots.
Design/methodology/approach
In the proposed method, new switching conditions among various behaviors are reasonably designed in order to guarantee the reliability and the generality of the method. In addition, path memory is incorporated in this method to enhance the robot's cognition capability to the environment. Therefore, the new method greatly weakens the blindness of decision making of robot and it is very helpful to select appropriate behaviors facing to the changeable situation. Comparing with the previous methods which are normally considering specific obstacles, the effectiveness of this proposed method for the environment with convex polygon‐shaped obstacles has been theoretically proved. The simulation and experimental results further demonstrate that the proposed method is adaptable for the environment with convex polygon‐shaped obstacles or non‐convex polygon‐shaped obstacles. It has more widely generality and adaptiveness than other existed methods in complicated unknown environment.
Findings
The proposed method can effectively realize real time motion planning with high reliability and generality. The cognition capability of mobile robot to the environment can be improved in order to adapt to the changeable situation. The proposed method can be suitable to more complex unknown environment. It is more applicable for actual environment comparing with other traditional APF methods.
Originality/value
This paper has widely investigated the currently existed approaches and analyzes deeply on four types of traditional APF methods adopted for real time motion planning in unknown environment with simulation works. Based on the conclusions of analysis, this paper presents an improved wall‐following approach. The proposed method can realize real time motion planning considering more complex environment with high reliability and generality. The simulation and experimental results further demonstrate that the proposed method is adaptable for the environment with convex polygon‐shaped obstacles or non‐convex polygon‐shaped obstacles. It has more widely generality and adaptiveness than other existed methods in complicated unknown environment.
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Hong Liu, Jun Wu, Shaowei Fan, Minghe Jin and Chunguang Fan
This paper aims to present a pose correction method based on integrated virtual impedance control for avoiding collision and reducing impact.
Abstract
Purpose
This paper aims to present a pose correction method based on integrated virtual impedance control for avoiding collision and reducing impact.
Design/methodology/approach
The authors first constructed the artificial potential field (APF) considering the geometric characteristics of the end-effector. The characteristics of the proposed field were analyzed considering the position and orientation misalignment. Then, an integrated virtual impedance control was proposed by adding resultant virtual repulsive force into traditional impedance control. Finally, the authors modified a correction trajectory for avoiding collision and reducing impact with virtual force and contact force.
Findings
The APF the authors constructed can get rid of a local minimum. Comparing with linear correction, this method is able to avoid collision effectively. When the capturing target has intrinsic estimation error, the pose correction can ensure smooth transitions among different stages.
Practical implications
This method can be implemented on a manipulator with inner position control. It can be applied to an industrial robot with applications on robotic assembly for achieving a softer and smoother process. The method can also be expanded to the kind of claw-shaped end-effectors for capturing target.
Originality value
As the authors know, it is the first time that the characteristics of the end-effector are considered for avoiding collision in capturing application. The proposed integrated virtual impedance control can provide smooth transitions among different stages without switching different force/position controllers.
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Ping Zhang, Peigen Jin, Guanglong Du and Xin Liu
The purpose of this paper is to provide a novel methodology based on two-level protection for ensuring safety of the moving human who enters the robot’s workspace, which is…
Abstract
Purpose
The purpose of this paper is to provide a novel methodology based on two-level protection for ensuring safety of the moving human who enters the robot’s workspace, which is significant for dealing with the problem of human security in a human-robot coexisting environment.
Design/methodology/approach
In this system, anyone who enters the robot’s working space is detected by using the Kinect and their skeletons are calculated by the interval Kalman filter in real time. The first-level protection is mainly based on the prediction of the human motion, which used Gaussian mixture model and Gaussian Mixture Regression. However, even in cases where the prediction of human motion is incorrect, the system can still safeguard the human by enlarging the initial bounding volume of the human as the second-level early warning areas. Finally, an artificial potential field with some additional avoidance strategies is used to plan a path for a robot manipulator.
Findings
Experimental studies on the GOOGOL GRB3016 robot show that the robot manipulator can accomplish the predetermined tasks by circumventing the human, and the human does not feel dangerous.
Originality/value
This study presented a new framework for ensuring human security in a human-robot coexisting environment, and thus can improve the reliability of human-robot cooperation.
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The purpose of this paper is to propose a suitable motion planning for omni‐directional mobile robots (OMRs) by taking into account the motion characteristics.
Abstract
Purpose
The purpose of this paper is to propose a suitable motion planning for omni‐directional mobile robots (OMRs) by taking into account the motion characteristics.
Design/methodology/approach
Based on the kinematic and dynamic constraints, the maximum velocity, motion stability and energy consumption of the OMR moving in different directions are analysed, and the anisotropy of the OMR is presented. In order to obtain the optimal motion, the path that the robot can take in order to avoid the obstacle safely and reach the goal in a shorter path is deduced. According to the new concept of anisotropic function, the motion direction derived from traditional artificial potential field (tAPF) is regulated.
Findings
A combination of the anisotropic function and tAPF method produces high‐speed, highly stable and efficient motion when compared to the tAPF. Simulations and experiments have proven the validity and effectiveness of this method.
Research limitations/implications
The practical factors, such as the effect of wear on the omni‐directional wheels, are not considered. Typical problems of APF, e.g. local minima, are not addressed here. In our future research, we will deal with these issues.
Practical implications
The proposed motion planning is applicable for any kind of OMRs, both three‐ and four‐wheeled OMRs, which can fully exhibit the advantages of OMRs.
Originality/value
The new concept of an anisotropic function is proposed to indicate the quality of motion in different directions. Different motion effects can be obtained in the same direction with different weights denoted by the anisotropic function, i.e. different trade‐offs can be achieved by varying the weights.
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Peng Wu, Shaorong Xie, Hengli Liu, Ming Li, Hengyu Li, Yan Peng, Xiaomao Li and Jun Luo
Autonomous obstacle avoidance is important in unmanned surface vehicle (USV) navigation. Although the result of obstacle detection is often inaccurate because of the inherent…
Abstract
Purpose
Autonomous obstacle avoidance is important in unmanned surface vehicle (USV) navigation. Although the result of obstacle detection is often inaccurate because of the inherent errors of LIDAR, conventional methods typically emphasize on a single obstacle-avoidance algorithm and neglect the limitation of sensors and safety in a local region. Conventional methods also fail in seamlessly integrating local and global obstacle avoidance algorithms. This paper aims to present a cooperative manoeuvring approach including both local and global obstacle avoidance.
Design/methodology/approach
The global algorithm used in our USV is the Artificial Potential Field-Ant Colony Optimization (APF-ACO) obstacle-avoidance algorithm, which plans a relative optimal path on the specified electronic map before the cruise of USV. The local algorithm is a multi-layer obstacle-avoidance framework based on a single LIDAR to present an efficient solution to USV path planning in the case of sensor errors and collision risks. When obstacles are within a layer, the USV uses a corresponding obstacle-avoidance algorithm. Then the USV moves towards the global direction according to fuzzy rules in the fuzzy layer.
Findings
The presented method offers a solution for obstacle avoidance in a complex environment. The USV follows the global trajectory planed by the APF-ACO algorithm. While, the USV can bypass current obstacle in the local region based on the multi-layer method effectively. This fact was validated by simulations and field trials.
Originality/value
The method presented in this paper takes advantage of algorithm integration that remedies errors of obstacle detection. Simulation and experiments were also conducted for performance evaluation.
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To plan the urban traffic path using the ant colony algorithm, the composition and functional division of the mobile robot are analyzed. The TSP (Traveling Salesman Problem) is…
Abstract
To plan the urban traffic path using the ant colony algorithm, the composition and functional division of the mobile robot are analyzed. The TSP (Traveling Salesman Problem) is used to deeply understand the traditional ant colony algorithm. Then, based on this, the improvement scheme of the traditional ant colony algorithm is analyzed. The results showed that the artificial potential field method and the A* algorithm improved the performance of the ant colony algorithm. At the initial stage of the search path, the blindness and randomness of the ant colony algorithm due to insufficient pheromone concentration in each path were solved. The local optimal path is avoided with the development of algorithm iteration. Therefore, the improved ant colony algorithm is superior to the traditional ant colony algorithm.
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Zhenyu Wu, Guang Hu, Lin Feng, Jiping Wu and Shenglan Liu
This paper aims to investigate the collision avoidance problem for a mobile robot by constructing an artificial potential field (APF) based on geometrically modelling the…
Abstract
Purpose
This paper aims to investigate the collision avoidance problem for a mobile robot by constructing an artificial potential field (APF) based on geometrically modelling the obstacles with a new method named the obstacle envelope modelling (OEM).
Design/methodology/approach
The obstacles of arbitrary shapes are enveloped in OEM using the primitive, which is an ellipse in a two-dimensional plane or an ellipsoid in a three-dimensional space. As the surface details of obstacles are neglected elegantly in OEM, the workspace of a mobile robot is made simpler so as to increase the capability of APF in a clustered environment.
Findings
Further, a dipole is applied to the construction of APF produced by each obstacle, among which the positive pole pushes the robot away and the negative pole pulls the robot close.
Originality/value
As a whole, the dipole leads the robot to make a derivation around the obstacle smoothly, which greatly reduces the local minima and trajectory oscillations. Computer simulations are conducted to demonstrate the effectiveness of the proposed approach.
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Peng Wang, Chunxiao Song, Renquan Dong, Peng Zhang, Shuang Yu and Hao Zhang
Aiming at the problem that quadruped crawling robot is easy to collide and overturn when facing obstacles and bulges in the process of complex slope movement, this paper aims to…
Abstract
Purpose
Aiming at the problem that quadruped crawling robot is easy to collide and overturn when facing obstacles and bulges in the process of complex slope movement, this paper aims to propose an obstacle avoidance gait planning of quadruped crawling robot based on slope terrain recognition.
Design/methodology/approach
First, considering the problem of low uniformity of feature points in terrain recognition images under complex slopes, which leads to too long feature point extraction time, an improved ORB (Oriented FAST and Rotated BRIEF) feature point extraction method is proposed; second, when the robot avoids obstacles or climbs over bumps, aiming at the problem that the robustness of a single step cannot satisfy the above two motions at the same time, the crawling gait is planned according to the complex slope terrain, and a robot obstacle avoidance gait planning based on the artificial potential field method is proposed. Finally, the slope walking experiment is carried out in the Robot Operating System.
Findings
The proposed method provides a solution for the efficient walking of robot under slope. The experimental results show that the extraction time of the improved ORB extraction algorithm is 12.61% less than the original ORB extraction algorithm. The vibration amplitude of the robot’s centroid motion curve is significantly reduced, and the contact force is reduced by 7.76%. The time it takes for the foot contact force to stabilize has been shortened by 0.25 s. This fact is verified by simulation and test.
Originality/value
The method proposed in this paper uses the improved feature point recognition algorithm and obstacle avoidance gait planning to realize the efficient walking of quadruped crawling robot on the slope. The walking stability of quadruped crawling robot is tested by prototype.
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