135 research outputs found
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
Shared control of an aerial cooperative transportation system with a cable-suspended payload
This paper presents a novel bilateral shared framework for a cooperative aerial transportation and manipulation system composed by a team of micro aerial vehicles with a cable-suspended payload. The human operator is in charge of steering the payload and he/she can also change online the desired shape of the formation of robots. At the same time, an obstacle avoidance algorithm is in charge of avoiding collisions with the static environment. The signals from the user and from the obstacle avoidance are blended together in the trajectory generation module, by means of a tracking controller and a filter called dynamic input boundary (DIB). The DIB filters out the directions of motions that would bring the system too close to singularities, according to a suitable metric. The loop with the user is finally closed with a force feedback that is informative of the mismatch between the operator’s commands and the trajectory of the payload. This feedback intuitively increases the user’s awareness of obstacles or configurations of the system that are close to singularities. The proposed framework is validated by means of realistic hardware-in-the-loop simulations with a person operating the system via a force-feedback haptic interface
Whole-Body Control of a Mobile Manipulator for Passive Collaborative Transportation
Human-robot collaborative tasks foresee interactions between humans and
robots with various degrees of complexity. Specifically, for tasks which
involve physical contact among the agents, challenges arise in the modelling
and control of such interaction. In this paper we propose a control
architecture capable of ensuring a flexible and robustly stable physical
human-robot interaction, focusing on a collaborative transportation task. The
architecture is deployed onto a mobile manipulator, modelled as a whole-body
structure, which aids the operator during the transportation of an unwieldy
load. Thanks to passivity techniques, the controller adapts its interaction
parameters online while preserving robust stability for the overall system,
thus experimentally validating the architecture
Equilibria, Stability, and Sensitivity for the Aerial Suspended Beam Robotic System subject to Parameter Uncertainty
This work studies how parametric uncertainties affect the cooperative
manipulation of a cable-suspended beam-shaped load by means of two aerial
robots not explicitly communicating with each other. In particular, the work
sheds light on the impact of the uncertain knowledge of the model parameters
available to an established communication-less force-based controller. First,
we find the closed-loop equilibrium configurations in the presence of the
aforementioned uncertainties, and then we study their stability. Hence, we show
the fundamental role played in the robustness of the load attitude control by
the internal force induced in the manipulated object by non-vertical cables.
Furthermore, we formally study the sensitivity of the attitude error to such
parametric variations, and we provide a method to act on the load position
error in the presence of the uncertainties. Eventually, we validate the results
through an extensive set of numerical tests in a realistic simulation
environment including underactuated aerial vehicles and sagging-prone cables,
and through hardware experiments
Touch the Wind: Simultaneous Airflow, Drag and Interaction Sensing on a Multirotor
Disturbance estimation for Micro Aerial Vehicles (MAVs) is crucial for
robustness and safety. In this paper, we use novel, bio-inspired airflow
sensors to measure the airflow acting on a MAV, and we fuse this information in
an Unscented Kalman Filter (UKF) to simultaneously estimate the
three-dimensional wind vector, the drag force, and other interaction forces
(e.g. due to collisions, interaction with a human) acting on the robot. To this
end, we present and compare a fully model-based and a deep learning-based
strategy. The model-based approach considers the MAV and airflow sensor
dynamics and its interaction with the wind, while the deep learning-based
strategy uses a Long Short-Term Memory (LSTM) neural network to obtain an
estimate of the relative airflow, which is then fused in the proposed filter.
We validate our methods in hardware experiments, showing that we can accurately
estimate relative airflow of up to 4 m/s, and we can differentiate drag and
interaction force.Comment: The first two authors contributed equall
Collaborative Trolley Transportation System with Autonomous Nonholonomic Robots
Cooperative object transportation using multiple robots has been intensively
studied in the control and robotics literature, but most approaches are either
only applicable to omnidirectional robots or lack a complete navigation and
decision-making framework that operates in real time. This paper presents an
autonomous nonholonomic multi-robot system and an end-to-end hierarchical
autonomy framework for collaborative luggage trolley transportation. This
framework finds kinematic-feasible paths, computes online motion plans, and
provides feedback that enables the multi-robot system to handle long lines of
luggage trolleys and navigate obstacles and pedestrians while dealing with
multiple inherently complex and coupled constraints. We demonstrate the
designed collaborative trolley transportation system through practical
transportation tasks, and the experiment results reveal their effectiveness and
reliability in complex and dynamic environments
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