50,811 research outputs found
Multi-contact Planning on Humans for Physical Assistance by Humanoid
International audienceFor robots to interact with humans in close proximity safely and efficiently, a specialized method to compute whole-body robot posture and plan contact locations is required. In our work, a humanoid robot is used as a caregiver that is performing a physical assistance task. We propose a method for formulating and initializing a non-linear optimization posture generation problem from an intuitive description of the assistance task and the result of a human point cloud processing. The proposed method allows to plan whole-body posture and contact locations on a task-specific surface of a human body, under robot equilibrium, friction cone, torque/joint limits, collision avoidance, and assistance task inherent constraints. The proposed framework can uniformly handle any arbitrary surface generated from point clouds, for autonomously planing the contact locations and interaction forces on potentially moving, movable, and deformable surfaces, which occur in direct physical human-robot interaction. We conclude the paper with examples of posture generation for physical human-robot interaction scenarios
Collaborative Bimanual Manipulation Using Optimal Motion Adaptation and Interaction Control Retargetting Human Commands to Feasible Robot Control References
This article presents a robust and reliable human–robot collaboration (HRC) framework for bimanual manipulation. We propose an optimal motion adaptation method to retarget arbitrary human commands to feasible robot pose references while maintaining payload stability. The framework comprises three modules: 1) a task-space sequential equilibrium and inverse kinematics optimization ( task-space SEIKO ) for retargeting human commands and enforcing feasibility constraints, 2) an admittance controller to facilitate compliant human–robot physical interactions, and 3) a low-level controller improving stability during physical interactions. Experimental results show that the proposed framework successfully adapted infeasible and dangerous human commands into continuous motions within safe boundaries and achieved stable grasping and maneuvering of large and heavy objects on a real dual-arm robot via teleoperation and physical interaction. Furthermore, the framework demonstrated the capability in the assembly task of building blocks and the insertion task of industrial power connectors
Optimization of Humanoid Robot Designs for Human-Robot Ergonomic Payload Lifting
When a human and a humanoid robot collaborate physically, ergonomics is a key
factor to consider. Assuming a given humanoid robot, several control
architectures exist nowadays to address ergonomic physical human-robot
collaboration. This paper takes one step further by considering robot hardware
parameters as optimization variables in the problem of collaborative payload
lifting. The variables that parametrize robot's kinematics and dynamics ensure
their physical consistency, and the human model is considered in the
optimization problem. By leveraging the proposed modelling framework, the
ergonomy of the interaction is maximized, here given by the agents' energy
expenditure. Robot kinematic, dynamics, hardware constraints and human
geometries are considered when solving the associated optimization problem. The
proposed methodology is used to identify optimum hardware parameters for the
design of the ergoCub robot, a humanoid possessing a degree of embodied
intelligence for ergonomic interaction with humans. For the optimization
problem, the starting point is the iCub humanoid robot. The obtained robot
design reaches loads at heights in the range of 0.8-1.5 m with respect to the
iCub robot whose range is limited to 0.8-1.2 m. The robot energy expenditure is
decreased by about 33%, meanwhile, the human ergonomy is preserved, leading
overall to an improved interaction.Comment: Accepted to 2022 IEEE-RAS International Conference on Humanoid
Robotics (Humanoids
Safety-Aware Human-Robot Collaborative Transportation and Manipulation with Multiple MAVs
Human-robot interaction will play an essential role in various industries and
daily tasks, enabling robots to effectively collaborate with humans and reduce
their physical workload. Most of the existing approaches for physical
human-robot interaction focus on collaboration between a human and a single
ground robot. In recent years, very little progress has been made in this
research area when considering aerial robots, which offer increased versatility
and mobility compared to their grounded counterparts. This paper proposes a
novel approach for safe human-robot collaborative transportation and
manipulation of a cable-suspended payload with multiple aerial robots. We
leverage the proposed method to enable smooth and intuitive interaction between
the transported objects and a human worker while considering safety constraints
during operations by exploiting the redundancy of the internal transportation
system. The key elements of our system are (a) a distributed payload external
wrench estimator that does not rely on any force sensor; (b) a 6D admittance
controller for human-aerial-robot collaborative transportation and
manipulation; (c) a safety-aware controller that exploits the internal system
redundancy to guarantee the execution of additional tasks devoted to preserving
the human or robot safety without affecting the payload trajectory tracking or
quality of interaction. We validate the approach through extensive simulation
and real-world experiments. These include as well the robot team assisting the
human in transporting and manipulating a load or the human helping the robot
team navigate the environment. To the best of our knowledge, this work is the
first to create an interactive and safety-aware approach for quadrotor teams
that physically collaborate with a human operator during transportation and
manipulation tasks.Comment: Guanrui Li and Xinyang Liu contributed equally to this pape
Data-Driven Approach to Simulating Realistic Human Joint Constraints
Modeling realistic human joint limits is important for applications involving
physical human-robot interaction. However, setting appropriate human joint
limits is challenging because it is pose-dependent: the range of joint motion
varies depending on the positions of other bones. The paper introduces a new
technique to accurately simulate human joint limits in physics simulation. We
propose to learn an implicit equation to represent the boundary of valid human
joint configurations from real human data. The function in the implicit
equation is represented by a fully connected neural network whose gradients can
be efficiently computed via back-propagation. Using gradients, we can
efficiently enforce realistic human joint limits through constraint forces in a
physics engine or as constraints in an optimization problem.Comment: To appear at ICRA 2018; 6 pages, 9 figures; for associated video, see
https://youtu.be/wzkoE7wCbu
Safe Human Robot-Interaction using Switched Model Reference Admittance Control
Physical Human-Robot Interaction (pHRI) task involves tight coupling between
safety constraints and compliance with human intentions. In this paper, a novel
switched model reference admittance controller is developed to maintain
compliance with the external force while upholding safety constraints in the
workspace for an n-link manipulator involved in pHRI. A switched reference
model is designed for the admittance controller to generate the reference
trajectory within the safe workspace. The stability analysis of the switched
reference model is carried out by an appropriate selection of the Common
Quadratic Lyapunov Function (CQLF) so that asymptotic convergence of the
trajectory tracking error is ensured. The efficacy of the proposed controller
is validated in simulation on a two-link robot manipulator
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