3,070 research outputs found
Safety-related Tasks within the Set-Based Task-Priority Inverse Kinematics Framework
In this paper we present a framework that allows the motion control of a
robotic arm automatically handling different kinds of safety-related tasks. The
developed controller is based on a Task-Priority Inverse Kinematics algorithm
that allows the manipulator's motion while respecting constraints defined
either in the joint or in the operational space in the form of equality-based
or set-based tasks. This gives the possibility to define, among the others,
tasks as joint-limits, obstacle avoidance or limiting the workspace in the
operational space. Additionally, an algorithm for the real-time computation of
the minimum distance between the manipulator and other objects in the
environment using depth measurements has been implemented, effectively allowing
obstacle avoidance tasks. Experiments with a Jaco manipulator, operating in
an environment where an RGB-D sensor is used for the obstacles detection, show
the effectiveness of the developed system
Fast Manipulability Maximization Using Continuous-Time Trajectory Optimization
A significant challenge in manipulation motion planning is to ensure agility
in the face of unpredictable changes during task execution. This requires the
identification and possible modification of suitable joint-space trajectories,
since the joint velocities required to achieve a specific endeffector motion
vary with manipulator configuration. For a given manipulator configuration, the
joint space-to-task space velocity mapping is characterized by a quantity known
as the manipulability index. In contrast to previous control-based approaches,
we examine the maximization of manipulability during planning as a way of
achieving adaptable and safe joint space-to-task space motion mappings in
various scenarios. By representing the manipulator trajectory as a
continuous-time Gaussian process (GP), we are able to leverage recent advances
in trajectory optimization to maximize the manipulability index during
trajectory generation. Moreover, the sparsity of our chosen representation
reduces the typically large computational cost associated with maximizing
manipulability when additional constraints exist. Results from simulation
studies and experiments with a real manipulator demonstrate increases in
manipulability, while maintaining smooth trajectories with more dexterous (and
therefore more agile) arm configurations.Comment: In Proceedings of the IEEE International Conference on Intelligent
Robots and Systems (IROS'19), Macau, China, Nov. 4-8, 201
Overcoming barriers and increasing independence: service robots for elderly and disabled people
This paper discusses the potential for service robots to overcome barriers and increase independence of
elderly and disabled people. It includes a brief overview of the existing uses of service robots by disabled and elderly
people and advances in technology which will make new uses possible and provides suggestions for some of these new
applications. The paper also considers the design and other conditions to be met for user acceptance. It also discusses
the complementarity of assistive service robots and personal assistance and considers the types of applications and
users for which service robots are and are not suitable
Human-Robot Perception in Industrial Environments: A Survey
Perception capability assumes significant importance for human–robot interaction. The
forthcoming industrial environments will require a high level of automation to be flexible and
adaptive enough to comply with the increasingly faster and low-cost market demands. Autonomous
and collaborative robots able to adapt to varying and dynamic conditions of the environment,
including the presence of human beings, will have an ever-greater role in this context. However, if
the robot is not aware of the human position and intention, a shared workspace between robots and
humans may decrease productivity and lead to human safety issues. This paper presents a survey on
sensory equipment useful for human detection and action recognition in industrial environments.
An overview of different sensors and perception techniques is presented. Various types of robotic
systems commonly used in industry, such as fixed-base manipulators, collaborative robots, mobile
robots and mobile manipulators, are considered, analyzing the most useful sensors and methods to
perceive and react to the presence of human operators in industrial cooperative and collaborative
applications. The paper also introduces two proofs of concept, developed by the authors for future
collaborative robotic applications that benefit from enhanced capabilities of human perception and
interaction. The first one concerns fixed-base collaborative robots, and proposes a solution for human
safety in tasks requiring human collision avoidance or moving obstacles detection. The second
one proposes a collaborative behavior implementable upon autonomous mobile robots, pursuing
assigned tasks within an industrial space shared with human operators
NASA Center for Intelligent Robotic Systems for Space Exploration
NASA's program for the civilian exploration of space is a challenge to scientists and engineers to help maintain and further develop the United States' position of leadership in a focused sphere of space activity. Such an ambitious plan requires the contribution and further development of many scientific and technological fields. One research area essential for the success of these space exploration programs is Intelligent Robotic Systems. These systems represent a class of autonomous and semi-autonomous machines that can perform human-like functions with or without human interaction. They are fundamental for activities too hazardous for humans or too distant or complex for remote telemanipulation. To meet this challenge, Rensselaer Polytechnic Institute (RPI) has established an Engineering Research Center for Intelligent Robotic Systems for Space Exploration (CIRSSE). The Center was created with a five year $5.5 million grant from NASA submitted by a team of the Robotics and Automation Laboratories. The Robotics and Automation Laboratories of RPI are the result of the merger of the Robotics and Automation Laboratory of the Department of Electrical, Computer, and Systems Engineering (ECSE) and the Research Laboratory for Kinematics and Robotic Mechanisms of the Department of Mechanical Engineering, Aeronautical Engineering, and Mechanics (ME,AE,&M), in 1987. This report is an examination of the activities that are centered at CIRSSE
Two-Stage Transfer Learning for Heterogeneous Robot Detection and 3D Joint Position Estimation in a 2D Camera Image using CNN
Collaborative robots are becoming more common on factory floors as well as
regular environments, however, their safety still is not a fully solved issue.
Collision detection does not always perform as expected and collision avoidance
is still an active research area. Collision avoidance works well for fixed
robot-camera setups, however, if they are shifted around, Eye-to-Hand
calibration becomes invalid making it difficult to accurately run many of the
existing collision avoidance algorithms. We approach the problem by presenting
a stand-alone system capable of detecting the robot and estimating its
position, including individual joints, by using a simple 2D colour image as an
input, where no Eye-to-Hand calibration is needed. As an extension of previous
work, a two-stage transfer learning approach is used to re-train a
multi-objective convolutional neural network (CNN) to allow it to be used with
heterogeneous robot arms. Our method is capable of detecting the robot in
real-time and new robot types can be added by having significantly smaller
training datasets compared to the requirements of a fully trained network. We
present data collection approach, the structure of the multi-objective CNN, the
two-stage transfer learning training and test results by using real robots from
Universal Robots, Kuka, and Franka Emika. Eventually, we analyse possible
application areas of our method together with the possible improvements.Comment: 6+n pages, ICRA 2019 submissio
Grounding action in visuo-haptic space using experience networks
Traditional approaches to the use of machine learning algorithms do not provide a method to learn multiple tasks in one-shot on an embodied robot. It is proposed that grounding actions within the sensory space leads to the development of action-state relationships which can be re-used despite a change in task. A novel approach called an Experience Network is developed and assessed on a real-world robot required to perform three separate tasks. After grounded representations were developed in the initial task, only minimal further learning was required to perform the second and third task
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