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1 – 10 of over 4000Yi Wu, Xiaohui Jia, Tiejun Li, Chao Xu and Jinyue Liu
This paper aims to use redundant manipulators to solve the challenge of collision avoidance in construction operations such as welding and painting.
Abstract
Purpose
This paper aims to use redundant manipulators to solve the challenge of collision avoidance in construction operations such as welding and painting.
Design/methodology/approach
In this paper, a null-space-based task-priority adjustment approach is developed to avoid collisions. The method establishes the relative position of the obstacle and the robot arm by defining the “link space,” and then the priority of the collision avoidance task and the end-effector task is adjusted according to the relative position by introducing the null space task conversion factors.
Findings
Numerical simulations demonstrate that the proposed method can realize collision-free maneuvers for redundant manipulators and guarantee the tracking precision of the end-effector task. The experimental results show that the method can avoid dynamic obstacles in redundant manipulator welding tasks.
Originality/value
A new formula for task priority adjustment for collision avoidance of redundant manipulators is proposed, and the original task tracking accuracy is guaranteed under the premise of safety.
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Keywords
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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Keywords
Rupeng Yuan, Fuhai Zhang, Jiadi Qu, Guozhi Li and Yili Fu
This paper aims to provide a novel obstacle avoidance method based on multi-information inflation map.
Abstract
Purpose
This paper aims to provide a novel obstacle avoidance method based on multi-information inflation map.
Design/methodology/approach
In this paper, the multi-information inflation map is introduced, which considers different information, including a two-dimensional grid map and a variety of sensor information. The static layer of the map is pre-processed at first. Then sensor inputs are added in different semantic layers. The processed information in semantic layers is used to update the static layer. The obstacle avoidance algorithm based on the multi-information inflation map is able to generate different avoidance paths for different kinds of obstacles, and the motion planning based on multi-information inflation map can track the global path and drive the robot.
Findings
The proposed method was implemented on a self-made mobile robot. Four experiments are conducted to verify the advantages of the proposed method. The first experiment is to demonstrate the advantages of the multi-information inflation map over the layered cost map. The second and third experiments verify the effectiveness of the obstacle avoidance path generation and motion planning. The fourth experiment comprehensively verifies that the obstacle avoidance algorithm is able to deal with different kinds of obstacles.
Originality/value
The multi-information inflation map proposed in this paper has better performance than the layered cost maps. As the static layer is pre-processed, the computational efficiency is higher. Sensor information is added in semantic layers with different cost attenuation coefficients. All layers are reset before next update. Therefore, the previous state will not affect the current situation. The obstacle avoidance and motion planning algorithm based on the multi-information inflation map can generate different paths for different obstacles and drive a robot safely and control the velocity according to different conditions.
Details
Keywords
Shuhuan Wen, Xueheng Hu, Zhen Li, Hak Keung Lam, Fuchun Sun and Bin Fang
This paper aims to propose a novel active SLAM framework to realize avoid obstacles and finish the autonomous navigation in indoor environment.
Abstract
Purpose
This paper aims to propose a novel active SLAM framework to realize avoid obstacles and finish the autonomous navigation in indoor environment.
Design/methodology/approach
The improved fuzzy optimized Q-Learning (FOQL) algorithm is used to solve the avoidance obstacles problem of the robot in the environment. To reduce the motion deviation of the robot, fractional controller is designed. The localization of the robot is based on FastSLAM algorithm.
Findings
Simulation results of avoiding obstacles using traditional Q-learning algorithm, optimized Q-learning algorithm and FOQL algorithm are compared. The simulation results show that the improved FOQL algorithm has a faster learning speed than other two algorithms. To verify the simulation result, the FOQL algorithm is implemented on a NAO robot and the experimental results demonstrate that the improved fuzzy optimized Q-Learning obstacle avoidance algorithm is feasible and effective.
Originality/value
The improved fuzzy optimized Q-Learning (FOQL) algorithm is used to solve the avoidance obstacles problem of the robot in the environment. To reduce the motion deviation of the robot, fractional controller is designed. To verify the simulation result, the FOQL algorithm is implemented on a NAO robot and the experimental results demonstrate that the improved fuzzy optimized Q-Learning obstacle avoidance algorithm is feasible and effective.
Details
Keywords
Sa Xiao, Xuyang Chen, Yuankai Lu, Jinhua Ye and Haibin Wu
Imitation learning is a powerful tool for planning the trajectory of robotic end-effectors in Cartesian space. Present methods can adapt the trajectory to the obstacle; however…
Abstract
Purpose
Imitation learning is a powerful tool for planning the trajectory of robotic end-effectors in Cartesian space. Present methods can adapt the trajectory to the obstacle; however, the solutions may not always satisfy users, whereas it is hard for a nonexpert user to teach the robot to avoid obstacles in time as he/she wishes through demonstrations. This paper aims to address the above problem by proposing an approach that combines human supervision with the kernelized movement primitives (KMP) model.
Design/methodology/approach
This approach first extracts the reference database used to train KMP from demonstrations by using Gaussian mixture model and Gaussian mixture regression. Subsequently, KMP is used to modulate the trajectory of robotic end-effectors in real time based on feedback from its interaction with humans to avoid obstacles, which benefits from a novel reference database update strategy. The user can test different obstacle avoidance trajectories in the current task until a satisfactory solution is found.
Findings
Experiments performed with the KUKA cobot for obstacle avoidance show that this approach can adapt the trajectories of the robotic end-effector to the user’s wishes in real time, including trajectories that the robot has already passed and has not yet passed. Simulation comparisons also show that it exhibits better performance than KMP with the original reference database update strategy.
Originality/value
An interactive learning approach based on KMP is proposed and verified, which not only enables users to plan the trajectory of robotic end-effectors for obstacle avoidance more conveniently and efficiently but also provides an effective idea for accomplishing interactive learning tasks under constraints.
Details
Keywords
Lin Zhang, Yingjie Zhang, Manni Zeng and Yangfan Li
The purpose of this paper is to put forward a path planning method in complex environments containing dynamic obstacles, which improves the performance of the traditional A…
Abstract
Purpose
The purpose of this paper is to put forward a path planning method in complex environments containing dynamic obstacles, which improves the performance of the traditional A* algorithm, this method can plan the optimal path in a short running time.
Design/methodology/approach
To plan an optimal path in a complex environment with dynamic and static obstacles, a novel improved A* algorithm is proposed. First, obstacles are identified by GoogLeNet and classified into static obstacles and dynamic obstacles. Second, the ray tracing algorithm is used for static obstacle avoidance, and a dynamic obstacle avoidance waiting rule based on dilate principle is proposed. Third, the proposed improved A* algorithm includes adaptive step size adjustment, evaluation function improvement and path planning with quadratic B-spline smoothing. Finally, the proposed improved A* algorithm is simulated and validated in real-world environments, and it was compared with traditional A* and improved A* algorithms.
Findings
The experimental results show that the proposed improved A* algorithm is optimal and takes less execution time compared with traditional A* and improved A* algorithms in a complex dynamic environment.
Originality/value
This paper presents a waiting rule for dynamic obstacle avoidance based on dilate principle. In addition, the proposed improved A* algorithm includes adaptive step adjustment, evaluation function improvement and path smoothing operation with quadratic B-spline. The experimental results show that the proposed improved A* algorithm can get a shorter path length and less running time.
Details
Keywords
Liu Linxian, Zhang Wendong, Zhang Guojun, Guan Linggang, Xue Chenyang, Zhang Hui and Xue Nan
The purpose of this paper is to develop a novel MEMS vector hydrophone with the key features of smaller size, better consistency, higher sensitivity and directional reception, and…
Abstract
Purpose
The purpose of this paper is to develop a novel MEMS vector hydrophone with the key features of smaller size, better consistency, higher sensitivity and directional reception, and to develop a highly effective and economical obstacle avoidance sonar system. Currently, the typical vector hydrophones are resonant vector hydrophones based on the accelerometer, which greatly increases the volume and constrains the detection sensitivity. Also, because the system is composed of a number of devices, its size is difficult to be reduced.
Design/methodology/approach
A novel double T-shape MEMS vector hydrophone is proposed with a fish’s lateral line organs as prototypes. The structure size and layout location of the piezoresistors were determined by simulation analysis, and the double T-shape microstructure was fabricated integrally by MEMS manufacturing technology, after which, the acoustic package of the microstructure was completed and the prototype was produced. Finally, the packaged hydrophone was calibrated in a standing wave field in the first-class national-defense underwater acoustic calibration station of China. Also, the design and test of an obstacle avoidance sonar system based on the vector hydrophone were completed.
Findings
The calibration data show that the double T-shape vector hydrophone has a flat frequency response curve, exhibits a sensitivity of −180 dB (1 kHz, 0 dB reference 1 V/uPa) and shows a good directivity pattern in the form of an “8” shape. The test results of the obstacle avoidance sonar system further verify the feasibility of detecting underwater acoustic signals.
Research limitations/implications
The next work is to increase the sensitivity by optimizing the microstructure and to realize orientation by organizing array.
Practical implications
The hydrophone has the advantages of smaller size, lower cost and directional reception. It can be used to develop highly effective and economical obstacle avoidance sonar system, thus solving the problems of water transport efficiency and traffic safety. The hydrophone has broad application prospects and a huge market potential in the civilian fields.
Originality/value
The MEMS technology and innovative bionic microstructure enable the miniaturization and low cost of the hydrophone. The hydrophone is easy to form array and can narrow the array aperture greatly. So, the hydrophone can be widely used in civil sonar systems.
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Keywords
Chung‐Hao Chen, Chang Cheng, David Page, Andreas Koschan and Mongi Abidi
Aims to develop a robotic platform to autonomously track a moving object
Abstract
Purpose
Aims to develop a robotic platform to autonomously track a moving object
Design/methodology/approach
This robotic platform, based on a modular system known as SafeBot, uses two sensors: a visual CCD camera and a laser‐based range sensor. The rigidly mounted camera tracks an object in front of the platform and generates appropriate drive commands to keep the object in view, even if the object itself moves. The range sensor detects other objects as the platform moves to provide real‐time obstacle avoidance while continuously tracking the original object.
Findings
The current approach successfully tracks an object, particularly a human subject, and avoids reasonably sized obstacles, but on‐board processing limitations restrict the speed of the object to approximately 5 km/h.
Originality/value
The core technology – a moving object tracked by a mobile robot with real‐time obstacle avoidance – is an integrated system comprising object tracking on a mobile platform and real‐time obstacle avoidance with robotic control. This system is applicable to a variety of automated applications such as inventory management, industrial palette distribution, and intruder surveillance.
Details
Keywords
Dongmin Li, Yuanzhi Zhao, Shiming Zhu and Hengxuan Luan
This paper aims to propose a conceptual scale model of mobile drilling robot according to the actual drilling rig and working conditions to improve the safety and automation of…
Abstract
Purpose
This paper aims to propose a conceptual scale model of mobile drilling robot according to the actual drilling rig and working conditions to improve the safety and automation of drilling in tunnel construction and coal mining applications.
Design/methodology/approach
A couple of pinion and rack serves as the support mechanism driven by a motor with low rotation speed at high power, and these components are assembled in the center of the robot to tightly fasten the whole body together. The drilling rod and the sleeve are connected through a hole with screw thread so that the rod feeds and rotates simultaneously along with the sleeve. The robot model is automatically controlled by a single-chip microcomputer, and the anti-disturbance circuit is designed as well. A five-step rule obstacle avoidance method is proposed to ensure safe and reliable movement.
Findings
The results of simulation experiments on drilling operation do indicate that the mechanism and control method are feasible and effective.
Research limitations/implications
The robot is nearly complete but indeed remains only an experimental machine.
Originality/value
The design of the mechanism structure for the conceptual robot is novelty. The method of five-step rule obstacle avoidance can improve reliability of obstacle avoidance according to the experimental results, which can meet the requirements of complex working conditions underground coal mine.
Details
Keywords
Dynamic movement primitives (DMPs) is a general robotic skill learning from demonstration method, but it is usually used for single robotic manipulation. For cloud-based robotic…
Abstract
Purpose
Dynamic movement primitives (DMPs) is a general robotic skill learning from demonstration method, but it is usually used for single robotic manipulation. For cloud-based robotic skill learning, the authors consider trajectories/skills changed by the environment, rebuild the DMPs model and propose a new DMPs-based skill learning framework removing the influence of the changing environment.
Design/methodology/approach
The authors proposed methods for two obstacle avoidance scenes: point obstacle and non-point obstacle. For the case with point obstacles, an accelerating term is added to the original DMPs function. The unknown parameters in this term are estimated by interactive identification and fitting step of the forcing function. Then a pure skill despising the influence of obstacles is achieved. Using identified parameters, the skill can be applied to new tasks with obstacles. For the non-point obstacle case, a space matching method is proposed by building a matching function from the universal space without obstacle to the space condensed by obstacles. Then the original trajectory will change along with transformation of the space to get a general trajectory for the new environment.
Findings
The proposed two methods are certified by two experiments, one of which is taken based on Omni joystick to record operator’s manipulation motions. Results show that the learned skills allow robots to execute tasks such as autonomous assembling in a new environment.
Originality/value
This is a new innovation for DMPs-based cloud robotic skill learning from multi-scene tasks and generalizing new skills following the changes of the environment.
Details