4,368 research outputs found
Encoderless Gimbal Calibration of Dynamic Multi-Camera Clusters
Dynamic Camera Clusters (DCCs) are multi-camera systems where one or more
cameras are mounted on actuated mechanisms such as a gimbal. Existing methods
for DCC calibration rely on joint angle measurements to resolve the
time-varying transformation between the dynamic and static camera. This
information is usually provided by motor encoders, however, joint angle
measurements are not always readily available on off-the-shelf mechanisms. In
this paper, we present an encoderless approach for DCC calibration which
simultaneously estimates the kinematic parameters of the transformation chain
as well as the unknown joint angles. We also demonstrate the integration of an
encoderless gimbal mechanism with a state-of-the art VIO algorithm, and show
the extensions required in order to perform simultaneous online estimation of
the joint angles and vehicle localization state. The proposed calibration
approach is validated both in simulation and on a physical DCC composed of a
2-DOF gimbal mounted on a UAV. Finally, we show the experimental results of the
calibrated mechanism integrated into the OKVIS VIO package, and demonstrate
successful online joint angle estimation while maintaining localization
accuracy that is comparable to a standard static multi-camera configuration.Comment: ICRA 201
RUR53: an Unmanned Ground Vehicle for Navigation, Recognition and Manipulation
This paper proposes RUR53: an Unmanned Ground Vehicle able to autonomously
navigate through, identify, and reach areas of interest; and there recognize,
localize, and manipulate work tools to perform complex manipulation tasks. The
proposed contribution includes a modular software architecture where each
module solves specific sub-tasks and that can be easily enlarged to satisfy new
requirements. Included indoor and outdoor tests demonstrate the capability of
the proposed system to autonomously detect a target object (a panel) and
precisely dock in front of it while avoiding obstacles. They show it can
autonomously recognize and manipulate target work tools (i.e., wrenches and
valve stems) to accomplish complex tasks (i.e., use a wrench to rotate a valve
stem). A specific case study is described where the proposed modular
architecture lets easy switch to a semi-teleoperated mode. The paper
exhaustively describes description of both the hardware and software setup of
RUR53, its performance when tests at the 2017 Mohamed Bin Zayed International
Robotics Challenge, and the lessons we learned when participating at this
competition, where we ranked third in the Gran Challenge in collaboration with
the Czech Technical University in Prague, the University of Pennsylvania, and
the University of Lincoln (UK).Comment: This article has been accepted for publication in Advanced Robotics,
published by Taylor & Franci
Evaluation of automated decisionmaking methodologies and development of an integrated robotic system simulation
A generic computer simulation for manipulator systems (ROBSIM) was implemented and the specific technologies necessary to increase the role of automation in various missions were developed. The specific items developed are: (1) capability for definition of a manipulator system consisting of multiple arms, load objects, and an environment; (2) capability for kinematic analysis, requirements analysis, and response simulation of manipulator motion; (3) postprocessing options such as graphic replay of simulated motion and manipulator parameter plotting; (4) investigation and simulation of various control methods including manual force/torque and active compliances control; (5) evaluation and implementation of three obstacle avoidance methods; (6) video simulation and edge detection; and (7) software simulation validation
Motion Planning of Uncertain Ordinary Differential Equation Systems
This work presents a novel motion planning framework, rooted in nonlinear programming theory, that treats uncertain fully and under-actuated dynamical systems described by ordinary differential equations. Uncertainty in multibody dynamical systems comes from various sources, such as: system parameters, initial conditions, sensor and actuator noise, and external forcing. Treatment of uncertainty in design is of paramount practical importance because all real-life systems are affected by it, and poor robustness and suboptimal performance result if it’s not accounted for in a given design. In this work uncertainties are modeled using Generalized Polynomial Chaos and are solved quantitatively using a least-square collocation method. The computational efficiency of this approach enables the inclusion of uncertainty statistics in the nonlinear programming optimization process. As such, the proposed framework allows the user to pose, and answer, new design questions related to uncertain dynamical systems.
Specifically, the new framework is explained in the context of forward, inverse, and hybrid dynamics formulations. The forward dynamics formulation, applicable to both fully and under-actuated systems, prescribes deterministic actuator inputs which yield uncertain state trajectories. The inverse dynamics formulation is the dual to the forward dynamic, and is only applicable to fully-actuated systems; deterministic state trajectories are prescribed and yield uncertain actuator inputs. The inverse dynamics formulation is more computationally efficient as it requires only algebraic evaluations and completely avoids numerical integration. Finally, the hybrid dynamics formulation is applicable to under-actuated systems where it leverages the benefits of inverse dynamics for actuated joints and forward dynamics for unactuated joints; it prescribes actuated state and unactuated input trajectories which yield uncertain unactuated states and actuated inputs.
The benefits of the ability to quantify uncertainty when planning the motion of multibody dynamic systems are illustrated through several case-studies. The resulting designs determine optimal motion plans—subject to deterministic and statistical constraints—for all possible systems within the probability space
Approximation of the inverse kinematics of a robotic manipulator using a neural network
A fundamental property of a robotic manipulator system is that it is capable of accurately
following complex position trajectories in three-dimensional space. An essential component
of the robotic control system is the solution of the inverse kinematics problem which allows
determination of the joint angle trajectories from the desired trajectory in the Cartesian
space. There are several traditional methods based on the known geometry of robotic
manipulators to solve the inverse kinematics problem. These methods can become
impractical in a robot-vision control system where the environmental parameters can alter.
Artificial neural networks with their inherent learning ability can approximate the inverse
kinematics function and do not require any knowledge of the manipulator geometry.
This thesis concentrates on developing a practical solution using a radial basis function
network to approximate the inverse kinematics of a robot manipulator. This approach is
distinct from existing approaches as the centres of the hidden-layer units are regularly
distributed in the workspace, constrained training data is used and the training phase is
performed using either the strict interpolation or the least mean square algorithms. An
online retraining approach is also proposed to modify the network function approximation
to cope with the situation where the initial training and application environments are
different. Simulation results for two and three-link manipulators verify the approach.
A novel real-time visual measurement system, based on a video camera and image
processing software, has been developed to measure the position of the robotic manipulator
in the three-dimensional workspace. Practical experiments have been performed with a
Mitsubishi PA10-6CE manipulator and this visual measurement system. The performance
of the radial basis function network is analysed for the manipulator operating in two and
three-dimensional space and the practical results are compared to the simulation results.
Advantages and disadvantages of the proposed approach are discussed
Robotic manipulators for single access surgery
This thesis explores the development of cooperative robotic manipulators for enhancing surgical precision and patient outcomes in single-access surgery and, specifically, Transanal Endoscopic Microsurgery (TEM). During these procedures, surgeons manipulate a heavy set of instruments via a mechanical clamp inserted in the patient’s body through a surgical port, resulting in imprecise movements, increased patient risks, and increased operating time. Therefore, an articulated robotic manipulator with passive joints is initially introduced, featuring built-in position and force sensors in each joint and electronic joint brakes for instant lock/release capability.
The articulated manipulator concept is further improved with motorised joints, evolving into an active tool holder. The joints allow the incorporation of advanced robotic capabilities such as ultra-lightweight gravity compensation and hands-on kinematic reconfiguration, which can optimise the placement of the tool holder in the operating theatre.
Due to the enhanced sensing capabilities, the application of the active robotic manipulator was further explored in conjunction with advanced image guidance approaches such as endomicroscopy. Recent advances in probe-based optical imaging such as confocal endomicroscopy is making inroads in clinical uses. However, the challenging manipulation of imaging probes hinders their practical adoption. Therefore, a combination of the fully cooperative robotic manipulator with a high-speed scanning endomicroscopy instrument is presented, simplifying the incorporation of optical biopsy techniques in routine surgical workflows.
Finally, another embodiment of a cooperative robotic manipulator is presented as an input interface to control a highly-articulated robotic instrument for TEM. This master-slave interface alleviates the drawbacks of traditional master-slave devices, e.g., using clutching mechanics to compensate for the mismatch between slave and master workspaces, and the lack of intuitive manipulation feedback, e.g. joint limits, to the user. To address those drawbacks a joint-space robotic manipulator is proposed emulating the kinematic structure of the flexible robotic instrument under control.Open Acces
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