26 research outputs found
A Sampling-Based Tree Planner for Robot Navigation Among Movable Obstacles
This thesis proposes a planner that solves Navigation Among Movable Obstacles problems giving robots the ability to reason about the environment and choose when manipulating obstacles. The planner combines the A*-Search and the exploration strategy of the Kinodynamic Motion Planning by Interior-Exterior Cell Exploration algorithm. It is locally optimal and independent from the size of the map and from the number, shape, and position of obstacles. It assumes full world knowledgeope
Conditional Task and Motion Planning through an Effort-based Approach
This paper proposes a preliminary work on a Conditional Task and Motion
Planning algorithm able to find a plan that minimizes robot efforts while
solving assigned tasks. Unlike most of the existing approaches that replan a
path only when it becomes unfeasible (e.g., no collision-free paths exist), the
proposed algorithm takes into consideration a replanning procedure whenever an
effort-saving is possible. The effort is here considered as the execution time,
but it is extensible to the robot energy consumption. The computed plan is both
conditional and dynamically adaptable to the unexpected environmental changes.
Based on the theoretical analysis of the algorithm, authors expect their
proposal to be complete and scalable. In progress experiments aim to prove this
investigation
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
From Human Perception and Action Recognition to Causal Understanding of Human-Robot Interaction in Industrial Environments
Human-robot collaboration is migrating from lightweight robots in laboratory environments to industrial applications, where heavy tasks and powerful robots are more common. In this scenario, a reliable perception of the humans involved in the process and related intentions and behaviors is fundamental. This paper presents two projects investigating the use of robots in relevant industrial scenarios, providing an overview of how industrial human-robot collaborative tasks can be successfully addressed
Precise Robotic Manipulation of Bulky Components
Increasing the flexibility of robots needs systems more capable in perceiving and interacting with the environment. A challenge is still to easy design the robotic system around the application, especially when the objects to be manipulated are bulky, and the relative positions between the robot and the objects to be manipulated are uncertain and high precision is required to successfully complete a task. In this paper a possible guideline to design a system capable to localize itself, identify a target, bulky, object and manipulate it, is presented. A method for tuning the impedance control parameters is shown, to keep interaction forces below dangerous values. The autonomous localization, grasping and assembly of a sidewall panel of an airplane is used as test. Experiments show that the success rate of completing a task increases, combining vision perception and force control, with respect to the single use of visual localization and position control
Using robotics to train students for Industry 4.0
This paper presents the master course on Autonomous Robotics that we offer at the School of Engineering of the University of Padova (Italy). Its novelty is the assignment of a lab project carefully designed to train students on autonomous and industrial robotics in the framework of Industry 4.0: the "Industry 4.0 Robotics Challenge". Students have to program both a manipulator and a mobile robot, together with a 3D vision system, in order to collaborate in the fulfillment of a pick-place-transport industrial task. We adopt a constructionist approach: project-based learning and team-based learning are applied to robotics and Industry 4.0. The project is organized as a challenge to motivate students to propose innovative ideas. A survey on students' satisfaction is reported at the end of the paper. We made the description of both the hardware and software setup, together with tutorials and wikis, publicly available to let other robotics instructors replicate our proposal and make it a point of reference for teaching robotics in the frame of Industry 4.0
Evaluation of the Force-Current Relationship in a 3-Finger Underactuated Gripper
This paper provides a detailed analytic evaluation of the force-current relationship for a real underactuated gripper, whose geometry presents some differences with respect to the cases usually considered in the literature. Differently from other approaches, the model proposed can work in two ways: calculating the current needed to exert a given force, and calculating the force applied by the gripper when a known current is impressed to the motor. Calculating the current as a function of the forces is not a trivial task; however, this is possible in the proposed solution thanks to the use of a single input parameter describing the set of forces applied by the gripper. The proposed approach was tested in real experiments, demonstrating that the proposed model is capable of providing a very good estimation in several working conditions