Fig 1 - uploaded by Jindong Liu
Content may be subject to copyright.
The experiment setup of a robotic arm and a solenoid with interchangeable hardness tips (showing the white tip in enlarged image). The arm is divided into 6 links (L0-L5) for the purpose of location identification of collisions.
Source publication
This paper presents a novel blind collision detection and material characterisation scheme for a compliant robotic arm. By the incorporation of a simple MEMS ac-celerometer at each joint, the robot is able to detect collision, identify the material of an obstacle, and create a map of the environment. Detailed hardware design is provided, illustrati...
Similar publications
Rapid development in micro/nano fabrication has enabled the shrinking of MEMS devices and the ability to fabricate them in large arrays. However, process variations and device mismatch have also raised testability issues in the MEMS industry. MEMS resonators have been coupled to simplify the characterization of the fabrication process and device pe...
Citations
... Current collision detection methods can be divided into two categories, model-based methods and model-free methods. In model-free methods, there are also two subcategories, the first is based on additional external sensors attached to the manipulator for detecting collisions and calculating the magnitudes of impact forces, such as skin sensors [5][6][7][8], additional vision [9], and accelerometer sensors [10,11]. In [12], a collision detection method was developed specifically for high payload applications. ...
The collision detection and estimation of external forces for robot manipulators are essential to ensure compliance and safety in the interaction between the robot and the environment or humans. The focus of this paper was to design a hybrid approach for collision detection between robots and their environment, and further to estimate external forces acting on a robot manipulator without the need for additional sensors. The current collision detection methods using observers are still suffering from the problem of an unavoidable trade-off between the estimation sensitivity and the reduction of the peaking value at the initial time. To satisfy both robustness and avoid peaking phenomenon at the initial time, a composite observer was designed, consisting of both a momentum observer and an extended state observer. The first observer provides high-precision tracking, while the second one reduces the peak value at the start. Through their complementary roles, the composite observer achieves improved performance in terms of sensitivity and reducing the peaking value. Simulation results, conducted using a 2-degree-of-freedom (2-DOF) robot manipulator, attest to the efficacy of the proposed approach.
... The coefficient of restitution is an important property in characterising how a body reacts during impact. While this property has rarely been used for object recognition, related features have been extracted from acoustic and acceleration data by investigating signal magnitude in the frequency domain [9], [10] or applying unsupervised learning methods [11] or statistical tools [12], [13]. The consideration of acceleration peak has also been used as a similar impact related measure for object recognition, proving able to recognise five different materials [14]. ...
... It can be seen that by using only friction or the coefficient of restitution, the classifier cannot recognise all the objects, resulting in a recognition rate lower than 50%. These features only recognise a group of hard objects (classes 1-10) better than soft objects (classes [11][12][13][14][15][16][17][18][19][20] as shown in Figs. 6a,c. ...
Current robotic haptic object recognition relies on statistical measures derived from movement dependent interaction signals such as force, vibration or position. Mechanical properties that can be identified from these signals are intrinsic object properties that may yield a more robust object representation. Therefore, this paper proposes an object recognition framework using multiple representative mechanical properties: the coefficient of restitution, stiffness, viscosity and friction coefficient. These mechanical properties are identified in real-time using a dual Kalman filter, then used to classify objects. The proposed framework was tested with a robot identifying 20 objects through haptic exploration. The results demonstrate the technique's effectiveness and efficiency, and that all four mechanical properties are required for best recognition yielding a rate of 98.18 $\pm$ 0.424 %. Clustering with Gaussian mixture models further shows that using these mechanical properties results in superior recognition as compared to using statistical parameters of the interaction signals.
... Besides, compliance will promote reach and dexterity of aerial manipulators, particularly in missions with unknown objects in unstructured and cluttered environments (Ruggiero et al., 2018). For this purpose, researchers either suggested mechanical compliance (Wisanuvej et al., 2014;Suarez et al., 2015;Yuksel et al., 2015;Bartelds et al., 2016) or treated the issue at the control level (Antonelli et al., 2016;Suarez et al., 2018;Hamaza et al., 2019). For the former type of solution, researchers substituted rigid-link manipulators with partially compliant ones which are composed of both rigid bodies and flexure joints (Teo et al., 2014). ...
Traditional aerial manipulation systems were usually composed of rigid-link manipulators attached to an aerial platform, arising several rigidity-related issues such as difficulties of reach, compliant motion, adaptability to object’s shape and pose uncertainties, and safety of human-manipulator interactions, especially in unstructured and confined environments. To address these issues, partially compliant manipulators, composed of rigid links and compliant/flexible joints, were proposed; however, they still suffer from insufficient dexterity and maneuverability. In this article, a new set of compliant aerial manipulators is suggested. For this purpose, the concept of aerial continuum manipulation system (ACMS) is introduced, several conceptual configurations are proposed, and the functionalities of ACMSs for different applications are discussed. Then, the performances of proposed aerial manipulators are compared with conventional aerial manipulators by implementing available benchmarks in the literature. To enhance the comparison, new features with related benchmarks are presented and used for evaluation purposes. In this study, the advantages of ACMSs over their rigid-link counterparts are illustrated and the potential applications of ACMSs are suggested. The open problems such as those related to dynamic coupling and control of ACMSs are also highlighted.
The future smart factory necessitates the identification of robot collisions, including the materials involved, the specific robot component impacted, and whether a human is involved. This article introduces a method for classifying robot collisions based on motor current and robot link vibration. First, it analyzes vibration modal features that can reveal the collision’s location and the contacting material. Thereafter, it employs variational mode decomposition (VMD) to analyze the vibration characteristics of collisions, with optimization of the decomposition parameter. To address computational complexity, an equivalent filter bank is proposed for extracting collision features instead of VMD. These vibration features are then utilized to construct and train a neural network for collision detection and classification. Finally, the proposed method is verified on a robot platform, successfully distinguishing and detecting collisions involving four different materials: human hand, cotton hammer, rubber hammer, and steel hammer. The accuracy of detection is evaluated using support vector machine (SVM), back propagation (BP), radial basis function (RBF), and convolutional neural network (CNN) through comprehensive experiments. The primary contribution of this study lies in the accurate differentiation between human-involved and nonhuman-involved collisions, facilitating real-time robot collision monitoring amid varying loads and offering insights into the collision’s location. Experimental results demonstrate a 90% accuracy in human collision detection and a 95% accuracy in collision link detection. Experiment data and code for training and testing of neural networks are available at
https://github.com/ldepn/Robot-collision-VMD.git
.
Labor shortages in the United States are impacting a number of industries including the meat processing sector. Collaborative technologies that work alongside humans while increasing production abilities may support the industry by enhancing automation and improving job quality. However, existing automation technologies used in the meat industry have limited collaboration potential, low flexibility, and high cost. The objective of this work was to explore the use of a robot arm to collaboratively work alongside a human and complete tasks performed in a meat processing facility. Toward this objective, we demonstrated proof-of-concept approaches to ensure human safety while exploring the capacity of the robot arm to perform example meat processing tasks. In support of human safety, we developed a knife instrumentation system to detect when the cutting implement comes into contact with meat within the collaborative space. To demonstrate the capability of the system to flexibly conduct a variety of basic meat processing tasks, we developed vision and control protocols to execute slicing, trimming, and cubing of pork loins. We also collected a subjective evaluation of the actions from experts within the U.S. meat processing industry. On average the experts rated the robot’s performance as adequate. Moreover, the experts generally preferred the cuts performed in collaboration with a human worker to cuts completed autonomously, highlighting the benefits of robotic technologies that assist human workers rather than replace them. Video demonstrations of our proposed framework can be found here: https://youtu.be/56mdHjjYMVc.
Existing collision detection methods can be loosely divided into two categories depending on whether the dynamics model of the robot is used (model-based methods) or not (model-free methods).
Current robotic haptic object recognition relies on statistical measures derived from movement dependent interaction signals such as force, vibration or position. Mechanical properties, which can be estimated from these signals, are intrinsic object properties that may yield a more robust object representation. Therefore, this paper proposes an object recognition framework using multiple representative mechanical properties: stiffness, viscosity and friction coefficient as well as the coefficient of restitution, which has been rarely used to recognise objects. These properties are estimated in real-time using a dual Kalman filter (without tangential force measurements) and then are used for object classification and clustering. The proposed framework was tested on a robot identifying 20 objects through haptic exploration. The results demonstrate the technique's effectiveness and efficiency, and that all four mechanical properties are required for the best recognition yielding a rate of 98.18
$\pm$
0.424 %. For object clustering, the use of these mechanical properties also results in superior performance when compared to methods based on statistical parameters.
