85 research outputs found
Model-Based Control of Flying Robots for Robust Interaction under Wind Influence
Model-Based Control of Flying Robots for Robust Interaction under Wind Influence The main goal of this thesis is to bridge the gap between trajectory tracking and interaction control for flying robots in order to allow physical interaction under wind influence by making aerial robots aware of the disturbance, interaction, and faults acting on them. This is accomplished by reasoning about the external wrench (force and torque) acting on the robot, and discriminating (distinguishing) between wind, interactions, and collisions. This poses the following research questions. First, is discrimination between the external wrench components even possible in a continuous real-time fashion for control purposes? Second, given the individual wrench components, what are effective control schemes for interaction and trajectory tracking control under wind influence? Third, how can unexpected faults, such as collisions with the environment, be detected and handled efficiently and effectively? In the interest of the first question, a fourth can be posed: is it possible to obtain a measurement of the wind speed that is independent of the external wrench? In this thesis, model-based methods are applied in the pursuit of answers to these questions. This requires a good dynamics model of the robot, as well as accurately identified parameters. Therefore, a systematic parameter identification procedure for aerial robots is developed and applied. Furthermore, external wrench estimation techniques from the field of robot manipulators are extended to be suitable for aerial robots without the need of velocity measurements, which are difficult to obtain in this context. Based on the external wrench estimate, interaction control techniques (impedance and admittance control) are extended and applied to flying robots, and a thorough stability proof is provided. Similarly, the wrench estimate is applied in a geometric trajectory tracking controller to compensate
external disturbances, to provide zero steady-state error under wind influence without the need of integral control action. The controllers are finally combined into a novel compensated impedance controller, to facilitate the main goal of the thesis. Collision detection is applied to flying robots, providing a low level reflex reaction that increases safety of these autonomous robots. In order to identify aerodynamic models for wind speed estimation, flight experiments in a three-dimensional wind tunnel were performed using a custom-built hexacopter. This data is used to investigate wind speed estimation using different data-driven aerodynamic models. It is shown that good performance can be obtained using relatively simple linear regression models. In this context, the propeller aerodynamic power model is used to obtain information about wind speed from available motor power measurements. Leveraging the wind tunnel data, it is shown that power can be used to obtain the wind speed. Furthermore, a novel optimization-based method that leverages the propeller aerodynamics model is developed to estimate the wind speed. Essentially, these two methods use the propellers as wind speed sensors, thereby providing an additional measurement independent of the external force. Finally, the novel topic of simultaneously discriminating between aerodynamic, interaction, and fault wrenches is opened up. This enables the implementation of novel types of controllers that are e.g. compliant to physical interaction, while compensating wind disturbances at the same time. The previously unexplored force discrimination topic has the potential to even open a new research avenue for flying robots
To Collide or Not To Collide -- Exploiting Passive Deformable Quadrotors for Contact-Rich Tasks
With an increase in aerial vehicle applications, passive deformable
quadrotors are getting significant attention in the research community due to
their potential to perform physical interaction tasks. Such quadrotors are
capable of undergoing collisions, both planned and unplanned, which are
harnessed to induce deformation and retain stability by dissipating collision
energies. In this article, we utilize one such passive deforming quadrotor,
XPLORER, to complete various contact-rich tasks by exploiting its compliant
chassis via various impact-aware planning and control algorithms. At the core
of these algorithms is a novel external wrench estimation technique developed
specifically for the unique multi-linked structure of XPLORER's chassis. The
external wrench information is then employed for designing interaction
controllers to obtain three additional flight modes: static-wrench application,
disturbance rejection and yielding to the disturbance. These modes are then
incorporated into a novel online exploration scheme to enable navigation in
unknown flight spaces with only tactile feedback and generate a map of the
environment without requiring additional sensors. Experiments show the efficacy
of this scheme to generate maps of the previously unexplored flight space with
an accuracy of 96.72%. Finally, we develop a novel collision-aware trajectory
planner (CATAAN) to generate minimum time maneuvers for waypoint tracking by
integrating collision-induced state jumps for both elastic and inelastic cases.
We experimentally validate that minimum time trajectories can be obtained with
CATAAN leading to a 40.38% reduction of settling time accompanied by improved
tracking performance of a root mean squared error in position within 0.5cm as
compared to 3cm of conventional methods
A flow disturbance estimation and rejection strategy for multirotors with round-trip trajectories
This paper presents a round-trip strategy of multirotors subject to unknown
flow disturbances. During the outbound flight, the vehicle immediately utilizes
the wind disturbance estimations in feedback control, as an attempt to reduce
the tracking error. During this phase, the disturbance estimations with respect
to the position are also recorded for future use. For the return flight, the
disturbances previously collected are then routed through a feedforward
controller. The major assumption here is that the disturbances may vary over
space, but not over time during the same mission. We demonstrate the
effectiveness of this feedforward strategy via experiments with two different
types of wind flows; a simple jet flow and a more complex flow. To use as a
baseline case, a cascaded PD controller with an additional feedback loop for
disturbance estimation was employed for outbound flights. To display our
contributions regarding the additional feedforward approach, an additional
feedforward correction term obtained via prerecorded data was integrated for
the return flight. Compared to the baseline controller, the feedforward
controller was observed to produce 43% less RMSE position error at a vehicle
ground velocity of 1 m/s with 6 m/s of environmental wind velocity. This
feedforward approach also produced 14% less RMSE position error for the complex
flows as well
Disturbance rejection for legged robots through a hybrid observer
A legged robot needs to move in unstructured environments continuously subject to disturbances. Existing disturbance observers are not enough when significant forces act on both the center of mass and the robot’s legs, and they usually employ indirect measures of the floating base’s velocity. This paper presents a solution combining a momentum-based observer for the angular term and an acceleration-based observer for the translational one, employing directly measurable values from the sensors. Due to this combination, we define this observer as ”hybrid,” and it can detect disturbances acting on both the legged robot’s center of mass and its legs. The estimation is employed in a whole-body controller. The framework is tested in simulation on a quadruped robot subject to significant disturbances, and it is compared with existing observer-based techniques
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
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