Search for: All records

This work presents a motion planning framework for robotic manipulators that computes collision-free paths directly in image space. The generated paths can then be tracked using vision-based control, eliminating the need for an explicit robot model or proprioceptive sensing. At the core of our approach is the construction of a roadmap entirely in image space. To achieve this, we explicitly define sampling, nearest-neighbor selection, and collision checking based on visual features rather than geometric models. We first collect a set of image space samples by moving the robot within its workspace, capturing keypoints along its body at different configurations. These samples serve as nodes in the roadmap, which we construct using either learned or predefined distance metrics. At runtime, the roadmap generates collision-free paths directly in image space, removing the need for a robot model or joint encoders. We validate our approach through an experimental study in which a robotic arm follows planned paths using an adaptive vision-based control scheme to avoid obstacles. The results show that paths generated with the learned-distance roadmap achieved 100% success in control convergence, whereas the predefined image space distance roadmap enabled faster transient responses but had a lower success rate in convergence. 

This paper presents a novel strategy to train keypoint detection models for robotics applications. Our goal is to develop methods that can robustly detect and track natural features on robotic manipulators. Such features can be used for vision-based control and pose estimation purposes, when placing artificial markers (e.g. ArUco) on the robot’s body is not possible or practical in runtime. Prior methods require accurate camera calibration and robot kinematic models in order to label training images for the keypoint locations. In this paper, we remove these dependencies by utilizing inpainting methods: In the training phase, we attach ArUco markers along the robot’s body and then label the keypoint locations as the center of those markers. We, then, use an inpainting method to reconstruct the parts of the robot occluded by the ArUco markers. As such, the markers are artificially removed from the training images, and labeled data is obtained to train markerless keypoint detection algorithms without the need for camera calibration or robot models. Using this approach, we trained a model for realtime keypoint detection and used the inferred keypoints as control features for an adaptive visual servoing scheme. We obtained successful control results with this fully model-free control strategy, utilizing natural robot features in the runtime and not requiring camera calibration or robot models in any stage of this process. 

This paper presents a visual servoing method for controlling a robot in the configuration space by purely using its natural features. We first created a data collection pipeline that uses camera intrinsics, extrinsics, and forward kinematics to generate 2D projections of a robot's joint locations (keypoints) in image space. Using this pipeline, we are able to collect large sets of real-robot data, which we use to train realtime keypoint detectors. The inferred keypoints from the trained model are used as control features in an adaptive visual servoing scheme that estimates, in runtime, the Jacobian relating the changes of the keypoints and joint velocities. We compared the 2D configuration control performance of this method to the skeleton-based visual servoing method (the only other algorithm for purely vision-based configuration space visual servoing), and demonstrated that the keypoints provide more robust and less noisy features, which result in better transient response. We also demonstrate the first vision-based 3D configuration space control results in the literature, and discuss its limitations. Our data collection pipeline is available at https://github.com/JaniC-WPI/KPDataGenerator.git which can be utilized to collect image datasets and train realtime keypoint detectors for various robots and environments. 

This paper presents a novel visual servoing method that controls a robotic manipulator in the configuration space as opposed to the classical vision-based control methods solely focusing on the end effector pose. We first extract the robot's shape from depth images using a skeletonization algorithm and represent it using parametric curves. We then adopt an adaptive visual servoing scheme that estimates the Jacobian online relating the changes of the curve parameters and the joint velocities. The proposed scheme does not only enable controlling a manipulator in the configuration space, but also demonstrates a better transient response while converging to the goal configuration compared to the classical adaptive visual servoing methods. We present simulations and real robot experiments that demonstrate the capabilities of the proposed method and analyze its performance, robustness, and repeatability compared to the classical algorithms.