125 research outputs found
Collision Detection and Reaction: A Contribution to Safe Physical Human-Robot Interaction
In the framework of physical Human-Robot Interaction
(pHRI), methodologies and experimental tests are
presented for the problem of detecting and reacting to collisions
between a robot manipulator and a human being. Using a
lightweight robot that was especially designed for interactive
and cooperative tasks, we show how reactive control strategies
can significantly contribute to ensuring safety to the human
during physical interaction. Several collision tests were carried
out, illustrating the feasibility and effectiveness of the proposed
approach. While a subjective “safety” feeling is experienced by
users when being able to naturally stop the robot in autonomous
motion, a quantitative analysis of different reaction strategies
was lacking. In order to compare these strategies on an objective
basis, a mechanical verification platform has been built. The
proposed collision detection and reactions methods prove to
work very reliably and are effective in reducing contact forces
far below any level which is dangerous to humans. Evaluations
of impacts between robot and human arm or chest up to a
maximum robot velocity of 2.7 m/s are presented
Air Bumper: A Collision Detection and Reaction Framework for Autonomous MAV Navigation
Autonomous navigation in unknown environments with obstacles remains
challenging for micro aerial vehicles (MAVs) due to their limited onboard
computing and sensing resources. Although various collision avoidance methods
have been developed, it is still possible for drones to collide with unobserved
obstacles due to unpredictable disturbances, sensor limitations, and control
uncertainty. Instead of completely avoiding collisions, this article proposes
Air Bumper, a collision detection and reaction framework, for fully autonomous
flight in 3D environments to improve the safety of drones. Our framework only
utilizes the onboard inertial measurement unit (IMU) to detect and estimate
collisions. We further design a collision recovery control for rapid recovery
and collision-aware mapping to integrate collision information into general
LiDAR-based sensing and planning frameworks. Our simulation and experimental
results show that the quadrotor can rapidly detect, estimate, and recover from
collisions with obstacles in 3D space and continue the flight smoothly with the
help of the collision-aware map. Our Air Bumper will be released as open-source
software on GitHub
Faster Motion on Cartesian Paths Exploiting Robot Redundancy at the Acceleration Level
The problem of minimizing the transfer time along a given Cartesian path for redundant robots can be approached in two steps, by separating the generation of a joint path associated to the Cartesian path from the exact minimization of motion time under kinematic/dynamic bounds along the obtained parameterized joint path. In this framework, multiple suboptimal solutions can be found, depending on how redundancy is locally resolved in the joint space within the first step. We propose a solution method that works at the acceleration level, by using weighted pseudoinversion, optimizing an inertia-related criterion, and including null-space damping. Several numerical results obtained on different robot systems demonstrate consistently good behaviors and definitely faster motion times in comparison with related methods proposed in the literature. The motion time obtained with our method is reasonably close to the global time-optimal solution along same Cartesian path. Experimental results on a KUKA LWR IV are also reported, showing the tracking control performance on the executed motions
A New Computed Torque Control System with an Uncertain RBF Neural Network Controller for a 7-DOF Robot
A novel percutaneous puncture robot system is proposed in the paper. Increasing the surgical equipment precision to reduce the patient\u27s pain and the doctor\u27s operation difficulty to treat smaller tumors can increase the success rate of surgery. To attain this goal, an optimized Computed Torque Law (CTL) using a radial basis function (RBF) neural network controller (RCTL) is proposed to improve the direction and position accuracy. BRF neural network with an uncertain term (URBF) which is able to compensate the system error caused by the imprecision of the model is added in the RCTL system. At first, a 7-DOF robotic system is established. It consists of robotic arm and actuator control channels. Now, the RBF compensator is added to the CTL to adjust the robot arm to reduce the position and direction errors. The angle and velocity errors of the robot arm are compensated using the RBF controller. According to the Lyapunov theory, the accuracy of torque control system depends on path tracking errors, inertia of robot, dynamic parameters and disturbance of each joint. Compared to general CTL approaches, the precision of a 7-DOF robot could be improved by adjusting the RBF parameters
SOFA: A modular yet efficient simulation framework
International audienceSOFA is a new open source framework primarily targeted at medical simulation research and industry. It is based on a scene graph data structure extended to physical models and abstract algorithms. Additionally, multiple models of the same objects can easily be used to optimize different tasks such as force computation, collision handling, and rendering. This results in a highly flexible architecture able to model and animate a wide range of simulated objects. We explain the main concepts of SOFA and detail an example of application to a surgery procedure
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