1,720,998 research outputs found
Hierarchical Task-Priority Control for Human-Robot Co-manipulation
Full text linkThe extensive distribution of collaborative robots in indus- trial workplaces allows human operators to decrease the weight and the repetitiveness of their activities. In order to facilitate the role of the human worker during the interaction with these robots, innovative con- trol paradigms, enabling an intuitive human-robot collaborative manipu- lation, are needed. In this work, a dynamic and hierarchical task-priority control framework is proposed, leveraging a physical interaction with a redundant robot manipulator through a force sensor. The foremost objec- tive of this approach is to exploit the non-trivial null-space of the redun- dant robot to increase the performance of the co-manipulation and, con- sequently, its effectiveness. A comparison between the proposed method- ology and a standard admittance control scheme is carried out within an industrial use case study consisting of a human operator interacting with a KUKA LBR iiwa arm
Fault Detection and Isolation for a Standard Quadrotor Using a Deep Neural Network Trained on a Momentum-based Estimator
Full text linkRapid identification of motor failures holds significant importance for ensuring the safety of multi-rotor unmanned aerial vehicles. This study introduces a method for detecting and isolating motor faults in standard quadrotors, utilizing an external wrench estimator and a recurrent neural network with long short-term memory nodes. The proposed approach treats partial or complete motor failure as an external disturbance affecting the quadrotor. Consequently, the external wrench estimator trains the network to quickly discern whether the estimated wrench results from a motor fault, identifying the specific motor involved, or if it stems from unmodeled dynamics or external factors such as wind or contacts. The training and testing of this method were conducted in a simulation environment equipped with a physics engine
Motor Fault Detection and Isolation for Multi-Rotor UAVs Based on External Wrench Estimation and Recurrent Deep Neural Network
Full text linkNeural-Network for Position Estimation of a Cable-Suspended Payload Using Inertial Quadrotor Sensing
Full text linkThis paper considers a standard quadrotor drone with a cable-suspended payload and minimal sensor configuration. A neural network estimator is proposed to perform accurate real-time payload position estimation. A novel proprioceptive feedback measurement method is proposed, and a neural network has been trained with domain randomization. The network shows accurate zero-shot estimation, even with excitations never seen by the system before. This preliminary work has been tested in a simulated environment and aims to show that only onboard inertial sensing is enough to achieve the sought task. The presented work may open new applications for drone transportation in real environments subject to several perturbations
Passivity-based control of VToL UAVs with a momentum-based estimator of external wrench and unmodeled dynamics
No full textStabilization and control on a pipe-rack of a wheeled mobile manipulator with a snake-like arm
Full text linkAutonomous inspection and maintenance tasks with robots in oil and gas refineries require moving along pipelines and manipulation dexterity in cluttered environments. This paper investigates the problem of controlling a wheeled mobile manipulator endowed with a snake-like arm to inspect the structures while stabilizing the supporting pipe. A model predictive control approach stabilizes the wheeled robot on the pipe. When the wheel torques saturate, the stabilization task leverages the resulting propagating force on the wheeled robot given by the snake-like arm’s dynamics. The significant number of degrees of freedom given by the snake- like arm allows a prioritized redundancy resolution scheme with hybrid motion/force tracking to inspect the same and adjacent pipes while avoiding self-collisions and environmental impacts. Simulations in the realistic Gazebo environment validate the achieved preliminary results
A Mixed-Initiative Control System for an Aerial Service Vehicle Supported by Force Feedback
No full textOnline Feet Potential Fields for Quadruped Robots Navigation in Harsh Terrains
Full text linkQuadruped robots have garnered significant attention in recent years due to their ability to navigate through challenging terrains. Among the various environments, agriculture fields are particularly difficult for legged robots, given the variability of soil types and conditions. To address this issue, this study proposes a novel navigation strategy that utilizes ground reaction forces to calculate online artificial potential fields, which are then applied to the robot’s feet to avoid low-traversability regions. The strategy also incorporates the net vector of the attractive potential field towards the goal and the repulsive field to avoid slippery regions, which dynamically adjusts the quadruped’s gait. A realistic simulation environment validates the proposed navigation framework with case studies on randomly generated terrains. A comprehensive comparison with baseline navigation methods is conducted to assess the effectiveness of the proposed approach
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