-- This work presents an RGB-D imaging-based approach to marker-free hand-eye calibration using a novel implementation of the iterative closest point (ICP) algorithm with a robust point-to-plane (PTP) objective formulated on a Lie algebra. Its applicability is demonstrated through comprehensive experiments using three well known serial manipulators and two RGB-D cameras. With only three randomly chosen robot configurations, our approach achieves approximately 90% successful calibrations, demonstrating 2 3 higher convergence rates to the global optimum compared to both marker-based and marker-free baselines. We also report 2 orders of magnitude faster convergence time ( 0 . Our method exhibits significantly improved accuracy ( 5 mm in task space) over classical approaches ( 7 mm in task space) whilst being marker-free. The benchmarking dataset and code are open sourced under Apache 2.0 License, and a ROS 2 integration with robot abstraction is provided to facilitate deployment Hand-eye calibration - determining a camera's pose relative to a serial manipulator and thus relating joint space to a Cartesian reference space - is a core component of robotics and computer vision, enabling autonomous operation and structured interaction with the environment.

The Baxter robot is a standard research platform used widely in research tasks, supported with an SDK provided by the developers, Rethink Robotics. Despite the ubiquitous use of the robot, the official software support is sub-standard. Especially, the native IK service has a low success rate and is often inconsistent. This unreliable behavior makes Baxter difficult to use for experiments and the research community is in need of a more reliable software support to control the robot. We present our work towards creating a Python based software library supporting the kinematics and dynamics of the Baxter robot. Our toolbox contains implementation of pose and velocity kinematics with support for Jacobian operations for redundancy resolution. We present the implementation and performance of our library, along with a comparison with PyKDL. Keywords- Baxter Research Robot, Manipulator Kinematics, Iterative IK, Dynamical Model, Redundant Manipulator

Abstract-- Robot art presents an opportunity to both showcase and advance state-of-the-art robotics through the challenging task of creating art. Creating large-scale artworks in particular engages the public in a way that small-scale works cannot, and the distinct qualities of brush strokes contribute to an organic and human-like quality. Combining the large scale of murals with the strokes of the brush medium presents an especially impactful result, but also introduces unique challenges in maintaining precise, dextrous motion control of the brush across such a large workspace. In this work, we present the first robot to our knowledge that can paint architectural-scale murals with a brush. We create a hybrid robot consisting of a cable-driven parallel robot and 4 degree of freedom (DoF) serial manipulator to paint a 27m by 3.7m mural on windows spanning 2-stories of a building. We discuss our approach to achieving both the scale and accuracy required for brush-painting a mural through a combination of novel mechanical design elements, coordinated planning and control, and on-site calibration algorithms with experimental validations.

Email: bruno.adorno@manchester.ac.uk ABSTRACT This paper presents two approaches to obtain the dynamical equations of mobile manipulators using dual quaternion algebra. The first one is based on a general recursive Newton-Euler formulation and uses twists and wrenches, which are propagated through high-level algebraic operations and works for any type of joints and arbitrary parameterizations. The second approach is based on Gauss's Principle of Least Constraint (GPLC) and includes arbitrary equality constraints. In addition to showing the connections of GPLC with Gibbs-Appell and Kane's equations, we use it to model a nonholonomic mobile manipulator. Our current formulations are more general than their counterparts in the state of the art, although GPLC is more computationally expensive, and simulation results show that they are as accurate as the classic recursive Newton-Euler algorithm. Keywords: Mobile Manipulator Dynamics, Dual Quaternion Algebra, Newton-Euler Model, Gauss's Principle of Least Constraint, Euler-Lagrange Equations, Gibbs-Appell Equations, Kane's Equations. 1 INTRODUCTION In the last thirty years, there have been an expressive amount of papers dealing with different representations for robot modeling. Notorious examples can be found in the works of Feather-stone [1-3], McCarthy [4-6], Selig [7,8], and Bayro-Corrochano [9], among many others. One of the reasons for such investigations is that the complexity of a robotic system goes far beyond the complexity of the mechanism itself. A typical robotic system involves motion/force/impedance control, path planning, task planning, and many more higher-level layers. Therefore, representations that are very useful for robot modeling, such as homogeneous transformation matrices, not necessarily are easy to use when performing pose control or impedance control, for example [10].