Adaptive Constrained Kinematic Control using Partial or Complete Task-Space Measurements

Recent advancements in constrained kinematic control make it an attractive strategy for controlling robots with arbitrary geometry in challenging tasks. Most current works assume that the robot kinematic model is precise enough for the task at hand. However, with increasing demands and safety requirements in robotic applications, there is a need for a controller that compensates online for kinematic inaccuracies. We propose an adaptive constrained kinematic control strategy based on quadratic programming, which uses partial or complete task-space measurements to compensate online for calibration errors. Our method is validated in experiments that show increased accuracy and safety compared to a state-of-the-art kinematic control strategy.Comment: Accepted on T-RO 2022, 16 Pages. Corrected a few typos and adjusted figure placemen

Dynamic Modeling of Branched Robots using Modular Composition

This letter proposes a systematic modular procedure for the dynamic modeling of branched robots comprising several subsystems, each of which being composed of multiple rigid bodies. Furthermore, the proposed strategy is applicable even if some subsystems are regarded as black boxes, requiring only the twists and wrenches at the connection points between different subsystems. To help in the model composition, we also propose a graph representation that encodes the propagation of twists and wrenches between the subsystems. Numerical results show that the proposed formalism is as accurate as a state-of-the-art library for robotic dynamic modeling.Comment: 7 pages, 5 figures, 2 tables. Under Review for the IEEE Robotics and Automation Letters (RA-L

Control Strategies for the Task Definition of Constrained Bimanual Manipulation

This work describes two use-cases to present strategies for the definition of constrained bimanual manipulation tasks. We use the cooperative dual task-space (CDTS) framework, vector field inequalities (VFIs) and geometric primitives such as Plücker lines and planes to implement collision avoidance with the environment and preserve the integrity of the task. Simulations of the use-cases were carried out and showed that all implemented constraints were upheld through the execution of the bimanual manipulation task

A Nonlinear Estimator for Dead Reckoning of Aquatic Surface Vehicles Using an IMU and a Doppler Velocity Log

Aquatic robots require an accurate and reliable localization system to navigate autonomously and perform practical missions. Kalman filters (KFs) and their variants are typically used in aquatic robots to combine sensor data. The two critical drawbacks of KFs are the requirement for skilled tuning of several filter parameters and the fact that changes to how the Inertial Measurement Unit (IMU) is oriented necessitate modifying the filter. To overcome those problems, this paper presents a novel method of fusing sensor data from a Doppler Velocity Log (DVL) and IMU using an adaptive nonlinear estimator to provide dead reckoning localization for a small autonomous surface vehicle. The proposed method has only one insensitive tuning parameter and is agnostic to the configuration of the IMU. The system was validated using a small ASV in a 2.4$\times$3.6$\times$2.4 m water tank, with a motion capture system as ground truth, and was evaluated against a state-of-the-art method based on KFs. Experiments showed that the average drift error of the nonlinear filter was 0.16 m (s.d. 0.06 m) compared to 0.15 m (s.d. 0.05 m) for the state of the art, meaning that the benefits in terms of tuning and flexible configuration do not come at the expense of performance