The Robotic Visual Information Processing System Based on Wavelet Transformation and Photoelectric Hybrid

Non

Visual Servoing using the Sum of Conditional Variance

International audienceIn this paper we propose a new way to achieve direct visual servoing. The novelty is the use of the sum of conditional variance to realize the optimization process of a positioning task. This measure, which has previously been used successfully in the case of visual tracking, has been shown to be invariant to non-linear illumination variations and inexpensive to compute. Compared to other direct approaches of visual servoing, it is a good compromise between techniques using the illumination of pixels which are computationally inexpensive but non robust to illumination variations and other approaches using the mutual information which are more complicated to compute but offer more robustness towards the variations of the scene. This method results in a direct visual servoing task easy and fast to compute and robust towards non-linear illumination variations. This paper describes a visual servoing task based on the sum of conditional variance performed using a Levenberg-Marquardt optimization process. The results are then demonstrated through experimental validations and compared to both photometric-based and entropy-based techniques

An FPGA-based controller for collaborative robotics

The use of robots is becoming more common in society. Industrial robots are being developed to work with people, and lower-force collaborative robots are being developed to help people in their everyday lives. These may need fast and sophisticated motion control and behavioral algorithms, but are expected to be more compact and lower cost. This paper proposes a processor plus FPGA solution for the control systems for such robots, where the FPGA performs all real-time tasks, freeing the processor to run lower-frequency high level control and interface to other devices such as camera systems. A demonstrator robot is designed, combining multi-axis motion control with 3D robot vision

3D Spectral Domain Registration-Based Visual Servoing

This paper presents a spectral domain registration-based visual servoing scheme that works on 3D point clouds. Specifically, we propose a 3D model/point cloud alignment method, which works by finding a global transformation between reference and target point clouds using spectral analysis. A 3D Fast Fourier Transform (FFT) in R3 is used for the translation estimation, and the real spherical harmonics in SO(3) are used for the rotations estimation. Such an approach allows us to derive a decoupled 6 degrees of freedom (DoF) controller, where we use gradient ascent optimisation to minimise translation and rotational costs. We then show how this methodology can be used to regulate a robot arm to perform a positioning task. In contrast to the existing state-of-the-art depth-based visual servoing methods that either require dense depth maps or dense point clouds, our method works well with partial point clouds and can effectively handle larger transformations between the reference and the target positions. Furthermore, the use of spectral data (instead of spatial data) for transformation estimation makes our method robust to sensor-induced noise and partial occlusions. We validate our approach by performing experiments using point clouds acquired by a robot-mounted depth camera. Obtained results demonstrate the effectiveness of our visual servoing approach.Comment: Accepted to 2023 IEEE International Conference on Robotics and Automation (ICRA'23

Robot visual servoing with iterative learning control.

YesThis paper presents an iterative learning scheme for vision guided robot trajectory tracking. At first, a stability criterion for designing iterative learning controller is proposed. It can be used for a system with initial resetting error. By using the criterion, one can convert the design problem into finding a positive definite discrete matrix kernel and a more general form of learning control can be obtained. Then, a three-dimensional (3-D) trajectory tracking system with a single static camera to realize robot movement imitation is presented based on this criterion

Visual servoing with hand-eye manipulator-optimal control approach

This paper proposes a control theoretic formulation and a controller design method for the feature-based visual servoing with redundant features. The linear time-invariant (LTI) formulation copes with the redundant features and provides a simple framework for controller design. The proposed linear quadratic (LQ) method can deal with the redundant features, which is important because the previous LQ methods are not applicable to redundant systems. Moreover, this LQ method gives flexibility for performance improvement instead of the very limited design parameters provided by the generalized inverse and task function controllers. Validity of the LTI model and effectiveness and flexibility of the LQ optimal controller are evaluated by real-time experiments on a PUMA 560 manipulator</p