A Learning-Based Object Tracking Strategy Using Visual Sensors and Intelligent Robot Arm

• Published in: IEEE transactions on automation science and engineering Vol. 20; no. 4; pp. 1 - 14
• Main Authors: Xu, Sheng, Chen, Kai, Ou, Yongsheng, Wang, Zhiyang, Yang, Chenguang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cameras; Controllers; End effectors; Engineering profession; Industrial areas; learning from demonstration (LFD); Machine learning; object estimation; Object tracking; Optical tracking; Parameters; Position measurement; Probabilistic models; Robot arms; Robot dynamics; Robot tracking control; Robots; Stability analysis; Systems stability; Task analysis; Tracking; Tracking control; Tracking problem; Tracking systems; Trajectory; Velocity errors; Visualization
• ISSN: 1545-5955; 1558-3783
• DOI: 10.1109/TASE.2022.3213730

This paper focuses on addressing the visual tracking problem using learning-based methods for object tracking tasks. This problem contains a major difficulty, i.e., how to acquire a satisfactory generalization ability of the developed system? In this paper, firstly, the object state tracking system, including a camera-in-hand, a 3D camera and a Rethink Baxter robot, is introduced. The problem formulation is also presented. Secondly, we propose a Kalman-based estimation strategy to acquire the object's state. In addition, a learning-based tracking controller is developed using the Gaussian mixture models (GMM) method to steer the robot end-effector to track the mobile object. Thirdly, to guarantee system stability (i.e., the position and velocity errors between the object and end-effector will always converge to zeros), the controller parameter constraints are derived, which is a theoretical contribution of this paper. The controller parameter adjustment is avoided by the proposed training process. Thus, the proposed method becomes easy to implement, which is a practical contribution. Finally, the effectiveness of the proposed method is demonstrated by simulation and experimental examples, and the proposed method has satisfactory generalization ability. Note to Practitioners -This paper studies object tracking problems for different practical applications, such as industrial cutting, grasping and dynamic monitoring. Different trajectory tracking methods have been widely applied in the industrial area. However, users always complain that when the object or trajectory is changed, the tracking controller more or less needs to be re-adjusted. This re-adjust process always requires professional knowledge and programming experience, and thus a factory must employ some professional engineers. In addition, since the objects may be diverse in shape, color and size, to acquire the accurate object position and velocity, an appropriate solution is necessary. Motivated by the above introductions, this paper aims to develop a learning-based controller to track different complex trajectories without frequent and specific parameter adjustment processes. Firstly, a visual measurement system is developed to quickly find and estimate the position and velocity of an object. Secondly, with the object's information, the learning from demonstration method (GMM method) is applied for the control policy design. Thirdly, the detailed system stability analysis is presented, and the corresponding controller parameter constraints are derived and considered in the proposed control policy. Subsequently, with the demonstration data, the packaged learning algorithm will automatically compute the controller parameters, and users can change the controller performance only by providing the desired demonstrations. In summary, this paper proposes a systematic object tracking solution, and it may bring a new idea to develop a practical object tracking system, using both the learning-based methods to improve the ability of generalization for tracking different objects.