Collision Detection for Robot Manipulators Using Unsupervised Anomaly Detection Algorithms

Park, Kyu Min; Park, Younghyo; Yoon, Sang Wook; Park, Frank C.

doi:10.1109/tmech.2021.3119057

Cited by 21 publications

(6 citation statements)

References 27 publications

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“…To address these issues, we introduce a threshold segmentation-based method, where D d segments line data into a set D of points, reducing computational complexity and minimizing false positives, as defined in Equation (1).…”

Section: Knn Query Methods For Line Data Based On Threshold Segmentationmentioning

confidence: 99%

“…Collision detection plays an important role in computer graphics and manipulator path planning [1][2][3][4]. Multi-degree-of-freedom manipulators are composed of several interconnected links that undergo positional changes during movement.…”

Section: Introductionmentioning

confidence: 99%

“…Q = [q 1 , q 2 , ..., q 6 ] T fk(Q) = T T={T 1 There are numerous methods for calculating the Euclidean distance between two simple convex geometries. Mohammad Safeea et al introduced an efficient approach to compute the minimum distance between capsules based on QR decomposition [13] and used it for the path planning of a robotic arm [14,15].…”

Section: Introductionmentioning

confidence: 99%

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Novel Probabilistic Collision Detection for Manipulator Motion Planning Using HNSW

Zhang,

Tao,

Jiang

et al. 2024

Machines

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Collision detection is very important for robot motion planning. The existing accurate collision detection algorithms regard the evaluation of each node as a discrete event, ignoring the correlation between nodes, resulting in low efficiency. In this paper, we propose a novel approach that transforms collision detection into a binary classification problem. In particular, the proposed method searches the k-nearest neighbor (KNN) of the new node and estimates its collision probability by the prior node. We perform the hierarchical navigable small world (HNSW) method to query the nearest neighbor data and store the detected nodes to build the database incrementally. In addition, this research develops a KNN query technique tailored for linear data, incorporating threshold segmentation to facilitate collision detection along continuous paths. Moreover, it refines the distance function of the collision classifier to enhance the precision of probability estimations. Simulation results demonstrate the effectiveness of the proposed method.

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Section: Knn Query Methods For Line Data Based On Threshold Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Novel Probabilistic Collision Detection for Manipulator Motion Planning Using HNSW

Zhang,

Tao,

Jiang

et al. 2024

Machines

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“…Since the special characteristics of robotic manipulators (e.g., strong coupling, nonlinear friction, and parameter perturbation), it is difficult to establish an accurate dynamic model of the robotic manipulator in practical engineering. 2,3 Moreover, external disturbances that may make control difficult and degrade performance always exist. 4 To this end, in some applications requiring high control performance, designing a controller that can make the robotic manipulator control system with such uncertainties have higher tracking precision, better transient performance, and strong robustness is still a challenging issue.…”

Section: Introductionmentioning

confidence: 99%

Adaptive super twisting observer‐based prescribed time integral sliding mode tracking control of uncertain robotic manipulators

Shen,

Song,

Fang

et al. 2024

Adaptive Control & Signal

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SummaryA novel integral sliding mode control (ISMC) strategy combined with an adaptive super twisting observer (ASTO) for an uncertain robotic manipulator tracking control system is presented in this article. The comprehensive uncertainties including both parameter perturbations and external disturbances are considered during the controller design. Firstly, a new nominal control law with prescribed time convergent property based on time varying scaling function is presented for the system without uncertainties. Then this nominal control law constitutes the prescribed time convergent sliding surface for ISMC. As the reaching phase is eliminated in ISMC, leading to the prescribed time stability of the whole control system without uncertainties. Secondly, take the system uncertainties (both the matched and unmatched uncertainties) into consideration, two ASTOs are designed for handling them. So, the lumped uncertainties of the robotic manipulator control system can be well estimated and compensated in finite time with the help of backstepping method. Besides, the finite time convergent adaptive switching gains of the ASTO make the system stable without knowing the bounds of the uncertainties exactly and suppress the chattering phenomenon of control input. Finally, the proposed control algorithm is validated by simulation and experiment on a robotic manipulator. Also, from a quantitative analysis, we testify the proposed control scheme outperforms the compared one in all of the discussed cases of simulation part.

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“…[ 12 ], collision was detected by comparing the deviation between the calculated torque of kinematics and the measured torque of actual joints. The authors of [ 13 ] studied the feasibility of collision detection by using the change in the joint motor current value before and after collision, without using an additional physical joint sensor. In paper [ 14 ], external acceleration sensors were used to monitor the vibration of the manipulator in real-time, detect the collision of the manipulator through the abnormal vibration frequency, and determine the position and direction of the collision.…”

Section: Introductionmentioning

confidence: 99%

A Self-Collision Detection Algorithm of a Dual-Manipulator System Based on GJK and Deep Learning

Zhi

Adili

et al. 2023

Sensors

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Self-collision detection is fundamental to the safe operation of multi-manipulator systems, especially when cooperating in highly dynamic working environments. Existing methods still face the problem that detection efficiency and accuracy cannot be achieved at the same time. In this paper, we introduce artificial intelligence technology into the control system. Based on the Gilbert-Johnson-Keerthi (GJK) algorithm, we generated a dataset and trained a deep neural network (DLNet) to improve the detection efficiency. By combining DLNet and the GJK algorithm, we propose a two-level self-collision detection algorithm (DLGJK algorithm) to solve real-time self-collision detection problems in a dual-manipulator system with fast-continuous and high-precision properties. First, the proposed algorithm uses DLNet to determine whether the current working state of the system has a risk of self-collision; since most of the working states in a system workspace do not have a self-collision risk, DLNet can effectively reduce the number of unnecessary detections and improve the detection efficiency. Then, for the working states with a risk of self-collision, we modeled precise colliders and applied the GJK algorithm for fine self-collision detection, which achieved detection accuracy. The experimental results showed that compared to that with the global use of the GJK algorithm for self-collision detection, the DLGJK algorithm can reduce the time expectation of a single detection in a system workspace by 97.7%. In the path planning of the manipulators, it could effectively reduce the number of unnecessary detections, improve the detection efficiency, and reduce system overhead. The proposed algorithm also has good scalability for a multi-manipulator system that can be split into dual-manipulator systems.

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Collision Detection for Robot Manipulators Using Unsupervised Anomaly Detection Algorithms

Cited by 21 publications

References 27 publications

Novel Probabilistic Collision Detection for Manipulator Motion Planning Using HNSW

Novel Probabilistic Collision Detection for Manipulator Motion Planning Using HNSW

Adaptive super twisting observer‐based prescribed time integral sliding mode tracking control of uncertain robotic manipulators

A Self-Collision Detection Algorithm of a Dual-Manipulator System Based on GJK and Deep Learning

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