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

Lei, Maolin; Wang, Ting; Yao, Chen; Huan, Liu; Wang, Zhi; Deng, Yong

doi:10.3390/app10175893

Cited by 11 publications

(4 citation statements)

References 26 publications

(48 reference statements)

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“…In particular, for robots with several manipulators, there is a danger that manipulators will collide with each other. Such collisions are called self-collisions [7][8][9][10][11]. For robots with a copy-type control, the self-collisions control problem falls on the operator.…”

Section: Introductionmentioning

confidence: 99%

“…Vadim Kramar, Self-Collision Avoidance Control of Dual-Arm Multi-Link Robot Using Neural Network Approach and the search of the coordinates and their combinations at which these critical positions arise [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. In [7], the authors presented a solution of the problem of two manipulators motion control using a constraint-based programming approach, which was used to generate online motion plans.…”

Section: Introductionmentioning

confidence: 99%

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Self-Collision Avoidance Control of Dual-Arm Multi-Link Robot Using Neural Network Approach

Kramar

Kabanov

2022

Journal of Robotics and Control

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The problem of mutual collisions of manipulators of a dual-arm multi-link robot (so-called self-collisions) arises during the performance of a cooperative technological operation. Self-collisions can lead to non-fulfillment of the technological operation or even to the failure of the manipulators. In this regard, it is necessary to develop a method for online detection and avoidance of self-collisions of manipulators. The article presents a method for detecting and avoiding self-collisions of multi-link manipulators using an artificial neural network by the example of the dual-arm robot SAR-401. A comparative analysis is carried out and the architecture of an artificial neural network for self-collisions avoidance control of dual-arm robot manipulators is proposed. The novelty of the proposed approach lies in the fact that it is an alternative to the generally accepted methods of detecting self-collisions based on the numerical solution of inverse kinematics problems for manipulators in the form of nonlinear optimization problems. Experimental results performed based on MATLAB model, the simulator of the robot SAR-401 and on the real robot itself confirmed the applicability and effectiveness of the proposed approach. It is shown that the detection of possible self-collisions using the proposed method based on an artificial neural network is performed approximately 10 times faster than approaches based on the numerical solution of the inverse kinematics problem while maintaining the specified accuracy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Self-Collision Avoidance Control of Dual-Arm Multi-Link Robot Using Neural Network Approach

Kramar

Kabanov

2022

Journal of Robotics and Control

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“…Lei et al. [ 23 ] proposed a method for humanoid dual-arm robot that generates smooth repulsive velocity based on designing the links as discretized spheres. However, the method does not consider self-collision between the manipulator and mobile robot.…”

Section: Introductionmentioning

confidence: 99%

Reactive Self-Collision Avoidance for a Differentially Driven Mobile Manipulator

Jang

Kim

Park

2021

Sensors

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This paper introduces a reactive self-collision avoidance algorithm for differentially driven mobile manipulators. The proposed method mainly focuses on self-collision between a manipulator and the mobile robot. We introduce the concept of a distance buffer border (DBB), which is a 3D curved surface enclosing a buffer region of the mobile robot. The region has the thickness equal to buffer distance. When the distance between the manipulator and mobile robot is less than the buffer distance, which means the manipulator lies inside the buffer region of the mobile robot, the proposed strategy is to move the mobile robot away from the manipulator in order for the manipulator to be placed outside the border of the region, the DBB. The strategy is achieved by exerting force on the mobile robot. Therefore, the manipulator can avoid self-collision with the mobile robot without modifying the predefined motion of the manipulator in a world Cartesian coordinate frame. In particular, the direction of the force is determined by considering the non-holonomic constraint of the differentially driven mobile robot. Additionally, the reachability of the manipulator is considered to arrive at a configuration in which the manipulator can be more maneuverable. In this respect, the proposed algorithm has a distinct advantage over existing avoidance methods that do not consider the non-holonomic constraint of the mobile robot and push links away from each other without considering the workspace. To realize the desired force and resulting torque, an avoidance task is constructed by converting them into the accelerations of the mobile robot. The avoidance task is smoothly inserted with a top priority into the controller based on hierarchical quadratic programming. The proposed algorithm was implemented on a differentially driven mobile robot with a 7-DOFs robotic arm and its performance was demonstrated in various experimental scenarios.

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“…However, this method takes a long time and cannot be used for real-time collision detection. To reduce the required time for collision detection, the researchers proposed to use the relationship between spatial geometric objects as the principle of determination [37]. Additionally, the researcher reduced self-collision detection of components that never collide for optimizing self-collision detection technology [38].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Kinematic Modeling and Self-Collision Detection of a Mobile Manipulator Robot by Considering the Actual Physical Structure

Qiao

Luo

et al. 2021

Applied Sciences

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In this paper, an optimized kinematic modeling method to accurately describe the actual structure of a mobile manipulator robot with a manipulator similar to the universal robot (UR5) is developed, and an improved self-collision detection technology realized for improving the description accuracy of each component and reducing the time required for approximating the whole robot is introduced. As the primary foundation for trajectory tracking and automatic navigation, the kinematic modeling technology of the mobile manipulator has been the subject of much interest and research for many years. However, the kinematic model established by various methods is different from the actual physical model due to the fact that researchers have mainly focused on the relationship between driving joints and the end positions while ignoring the physical structure. To improve the accuracy of the kinematic model, we present a kinematic modeling method with the addition of key points and coordinate systems to some components that failed to model the physical structure based on the classical method. Moreover, self-collision detection is also a primary problem for successfully completing the specified task of the mobile manipulator. In traditional self-collision detection technology, the description of each approximation is determined by the spatial transformation of each corresponding component in the mobile manipulator robot. Unlike the traditional technology, each approximation in the paper is directly established by the physical structure used in the kinematic modeling method, which significantly reduces the complicated analysis and shortens the required time. The numerical simulations prove that the kinematic model with the addition of key point technology is similar to the actual structure of mobile manipulator robots, and the self-collision detection technology proposed in the article effectively improves the performance of self-collision detection. Additionally, the experimental results prove that the kinematic modeling method and self-collision detection technology outlined in this paper can optimize the inverse kinematics solution.

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

Cited by 11 publications

References 26 publications

Self-Collision Avoidance Control of Dual-Arm Multi-Link Robot Using Neural Network Approach

Self-Collision Avoidance Control of Dual-Arm Multi-Link Robot Using Neural Network Approach

Reactive Self-Collision Avoidance for a Differentially Driven Mobile Manipulator

Optimizing Kinematic Modeling and Self-Collision Detection of a Mobile Manipulator Robot by Considering the Actual Physical Structure

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