A real-time dual-arm collision avoidance algorithm for assembly

2001 ◽  
Vol 18 (8) ◽  
pp. 477-486 ◽  
Author(s):  
Sukhan Lee ◽  
Hadi Moradi
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Dual Arm

scholarly journals Real-Time Kinematics-Based Self-Collision Avoidance Algorithm for Dual-Arm Robots

2020 ◽  
Vol 10 (17) ◽  
pp. 5893
Author(s):  
Maolin Lei ◽  
Ting Wang ◽  
Chen Yao ◽  
Huan Liu ◽  
Zhi Wang ◽  
...  
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Kinematic Model ◽  
Computational Cost ◽  
Time Algorithm ◽  
Upper Body ◽  
Sensitivity Index ◽  
Control Points ◽  
Collision Model ◽  
Dual Arm

Self-collisions of a dual-arm robot system can cause severe damage to the robot. To deal with this problem, this paper presents a real-time algorithm for preventing self-collisions in dual-arm systems. Our first contribution in this work is a novel collision model built using discrete spherical bounding volumes with different radii. In addition, we propose a sensitivity index to measure the distance between spheres with different radii in real time. Next, according to the minimal sensitivity index between different spheres, the repulsive velocity is produced at the centers of the spheres (control points), which the robot uses to generate new motion based on the robot kinematic model. The proposed algorithm offers the additional benefits of a decrease in the number of bounding spheres, and a simple collision model that can effectively decrease the computational cost of the process. To demonstrate the validity of the algorithm, we performed simulations and experiments by an upper-body humanoid robot. Although the repulsive velocity acted on the control points, the results indicate that the algorithm can effectively achieve self-collision avoidance by using a simple collision model.

Longitudinal and lateral collision avoidance control strategy for intelligent vehicles

2021 ◽  
pp. 095440702110240
Author(s):  
Ziyu Zhang ◽  
Chunyan Wang ◽  
Wanzhong Zhao ◽  
Jian Feng
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Decentralized Control ◽  
Control Strategy ◽  
Centralized Control ◽  
Lateral Control ◽  
Time Performance ◽  
Collision Avoidance Control ◽  
Avoidance Control ◽  
Control Accuracy

In order to solve the problems of longitudinal and lateral control coupling, low accuracy and poor real-time of existing control strategy in the process of active collision avoidance, a longitudinal and lateral collision avoidance control strategy of intelligent vehicle based on model predictive control is proposed in this paper. Firstly, the vehicle nonlinear coupling dynamics model is established. Secondly, considering the accuracy and real-time requirements of intelligent vehicle motion control in pedestrian crossing scene, and combining the advantages of centralized control and decentralized control, an integrated unidirectional decoupling compensation motion control strategy is proposed. The proposed strategy uses two pairs of unidirectional decoupling compensation controllers to realize the mutual integration and decoupling in both longitudinal and lateral directions. Compared with centralized control, it simplifies the design of controller, retains the advantages of centralized control, and improves the real-time performance of control. Compared with the decentralized control, it considers the influence of longitudinal and lateral control, retains the advantages of decentralized control, and improves the control accuracy. Finally, the proposed control strategy is simulated and analyzed in six working conditions, and compared with the existing control strategy. The results show that the proposed control strategy is obviously better than the existing control strategy in terms of control accuracy and real-time performance, and can effectively improve vehicle safety and stability.

scholarly journals COLREG-Compliant Optimal Path Planning for Real-Time Guidance and Control of Autonomous Ships

2021 ◽  
Vol 9 (4) ◽  
pp. 405
Author(s):  
Raphael Zaccone
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Collision Avoidance ◽  
Autonomous Navigation ◽  
Optimal Path ◽  
Open Water ◽  
Optimal Path Planning ◽  
Guidance And Control ◽  
Real Time Applications ◽  
And Control

While collisions and groundings still represent the most important source of accidents involving ships, autonomous vessels are a central topic in current research. When dealing with autonomous ships, collision avoidance and compliance with COLREG regulations are major vital points. However, most state-of-the-art literature focuses on offline path optimisation while neglecting many crucial aspects of dealing with real-time applications on vessels. In the framework of the proposed motion-planning, navigation and control architecture, this paper mainly focused on optimal path planning for marine vessels in the perspective of real-time applications. An RRT*-based optimal path-planning algorithm was proposed, and collision avoidance, compliance with COLREG regulations, path feasibility and optimality were discussed in detail. The proposed approach was then implemented and integrated with a guidance and control system. Tests on a high-fidelity simulation platform were carried out to assess the potential benefits brought to autonomous navigation. The tests featured real-time simulation, restricted and open-water navigation and dynamic scenarios with both moving and fixed obstacles.

scholarly journals A Probabilistic Model for Real-Time Semantic Prediction of Human Motion Intentions from RGBD-Data

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4141
Author(s):  
Wouter Houtman ◽  
Gosse Bijlenga ◽  
Elena Torta ◽  
René van de Molengraft
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Probabilistic Model ◽  
Real Life ◽  
Human Motion ◽  
Model Based ◽  
Multiple Hypotheses ◽  
Navigation Algorithms ◽  
The Right ◽  
Motion Behavior

For robots to execute their navigation tasks both fast and safely in the presence of humans, it is necessary to make predictions about the route those humans intend to follow. Within this work, a model-based method is proposed that relates human motion behavior perceived from RGBD input to the constraints imposed by the environment by considering typical human routing alternatives. Multiple hypotheses about routing options of a human towards local semantic goal locations are created and validated, including explicit collision avoidance routes. It is demonstrated, with real-time, real-life experiments, that a coarse discretization based on the semantics of the environment suffices to make a proper distinction between a person going, for example, to the left or the right on an intersection. As such, a scalable and explainable solution is presented, which is suitable for incorporation within navigation algorithms.

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