Reactive Self-Collision Avoidance for a Differentially Driven Mobile Manipulator

Jang, Keunwoo; Kim, Sanghyun; Park, Jaeheung

doi:10.3390/s21030890

Cited by 13 publications

(6 citation statements)

References 37 publications

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“…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%

“…According to the approach, a simplified 3D model of the robot is generated using a segmented point cloud, and a self-identification control is based on the over-segmentation approach and the forward kinematic model of the robot. In [24] a self-collision between a manipulator and a mobile robot was considered. Authors introduced the concept of a distance buffer border, which is a 3D curved surface enclosing a buffer region of the mobile robot.…”

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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“…al. [7] developed an algorithm about self-collision avoidance of a mobile manipulator, Sholbach et. al.…”

Section: Introductionmentioning

confidence: 99%

Topology Optimization of Mobile Transportation Robot

DEMİR

Sucuoğlu

Böğrekçi

et al. 2021

International Journal of 3D Printing Technologies and Digital Industry

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The main aim of this study was to decrease the weight of mobile transportation robot and to check the optimized shape using topology optimization and structural analysis. The analyses were applied using CAE (Computer Aided Engineering) Software with the methods of topology optimization and structural analysis. In the topology optimization process, the preserved areas were defined for connection and fixing areas. The aim was to decrease the weight of robot structure and lower the energy consumption of the robotic system. After topology optimization, the structural strength analysis was applied to the new optimized structure to check about the strength. The weight of the structure was decreased with the ratio of 20%. In addition to this, the structural strength of the robot was observed similar to the original body with the factor of safety value as three.

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“…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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Reactive Self-Collision Avoidance for a Differentially Driven Mobile Manipulator

Cited by 13 publications

References 37 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

Topology Optimization of Mobile Transportation Robot

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

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