338 research outputs found
Nonlinear Receding-Horizon Control of Rigid Link Robot Manipulators
The approximate nonlinear receding-horizon control law is used to treat the
trajectory tracking control problem of rigid link robot manipulators. The
derived nonlinear predictive law uses a quadratic performance index of the
predicted tracking error and the predicted control effort. A key feature of
this control law is that, for their implementation, there is no need to perform
an online optimization, and asymptotic tracking of smooth reference
trajectories is guaranteed. It is shown that this controller achieves the
positions tracking objectives via link position measurements. The stability
convergence of the output tracking error to the origin is proved. To enhance
the robustness of the closed loop system with respect to payload uncertainties
and viscous friction, an integral action is introduced in the loop. A nonlinear
observer is used to estimate velocity. Simulation results for a two-link rigid
robot are performed to validate the performance of the proposed controller.
Keywords: receding-horizon control, nonlinear observer, robot manipulators,
integral action, robustness
Can't Touch This: Real-Time, Safe Motion Planning and Control for Manipulators Under Uncertainty
Ensuring safe, real-time motion planning in arbitrary environments requires a
robotic manipulator to avoid collisions, obey joint limits, and account for
uncertainties in the mass and inertia of objects and the robot itself. This
paper proposes Autonomous Robust Manipulation via Optimization with
Uncertainty-aware Reachability (ARMOUR), a provably-safe, receding-horizon
trajectory planner and tracking controller framework for robotic manipulators
to address these challenges. ARMOUR first constructs a robust controller that
tracks desired trajectories with bounded error despite uncertain dynamics.
ARMOUR then uses a novel recursive Newton-Euler method to compute all inputs
required to track any trajectory within a continuum of desired trajectories.
Finally, ARMOUR over-approximates the swept volume of the manipulator; this
enables one to formulate an optimization problem that can be solved in
real-time to synthesize provably-safe motions. This paper compares ARMOUR to
state of the art methods on a set of challenging manipulation examples in
simulation and demonstrates its ability to ensure safety on real hardware in
the presence of model uncertainty without sacrificing performance. Project
page: https://roahmlab.github.io/armour/.Comment: 20 pages, 6 figure
Reachability-based Trajectory Design
Autonomous mobile robots have the potential to increase the availability and accessibility of goods and services throughout society. However, to enable public trust in such systems, it is critical to certify that they are safe. This requires formally specifying safety, and designing motion planning methods that can guarantee safe operation (note, this work is only concerned with planning, not perception).
The typical paradigm to attempt to ensure safety is receding-horizon planning, wherein a robot creates a short plan, then executes it while creating its next short plan in an iterative fashion, allowing a robot to incorporate new sensor information over time. However, this requires a robot to plan in real time. Therefore, the key challenge in making safety guarantees lies in balancing performance (how quickly a robot can plan) and conservatism (how cautiously a robot behaves). Existing methods suffer from a tradeoff between performance and conservatism, which is rooted in the choice of model used describe a robot; accuracy typically comes at the price of computation speed.
To address this challenge, this dissertation proposes Reachability-based Trajectory Design (RTD), which performs real-time, receding-horizon planning with a simplified planning model, and ensures safety by describing the model error using a reachable set of the robot.
RTD begins with the offline design of a continuum of parameterized trajectories for the plan- ning model; each trajectory ends with a fail-safe maneuver such as braking to a stop. RTD then computes the robot’s Forward Reachable Set (FRS), which contains all points in workspace reach- able by the robot for each parameterized trajectory. Importantly, the FRS also contains the error model, since a robot can typically never track planned trajectories perfectly. Online (at runtime), the robot intersects the FRS with sensed obstacles to provably determine which trajectory plans could cause collisions. Then, the robot performs trajectory optimization over the remaining safe trajectories. If no new safe plan can be found, the robot can execute its previously-found fail-safe maneuver, enabling perpetual safety.
This dissertation begins by presenting RTD as a theoretical framework, then presents three representations of a robot’s FRS, using (1) sums-of-squares (SOS) polynomial programming, (2) zonotopes (a special type of convex polytope), and (3) rotatotopes (a generalization of zonotopes that enable representing a robot’s swept volume). To enable real-time planning, this work also de- velops an obstacle representation that enables provable safety while treating obstacles as discrete, finite sets of points. The practicality of RTD is demonstrated on four different wheeled robots (using the SOS FRS), two quadrotor aerial robots (using the zonotope FRS), and one manipulator robot (using the rotatotope FRS). Over thousands of simulations and dozens of hardware trials, RTD performs safe, real-time planning in arbitrary and challenging environments.
In summary, this dissertation proposes RTD as a general purpose, practical framework for provably safe, real-time robot motion planning.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162884/1/skousik_1.pd
Towards a Universal Modeling and Control Framework for Soft Robots
Traditional rigid-bodied robots are designed for speed, precision, and repeatability. These traits make them well suited for highly structured industrial environments, but poorly suited for the unstructured environments in which humans typically operate.
Soft robots are well suited for unstructured human environments because they them to can safely interact with delicate objects, absorb impacts without damage, and passively adapt their shape to their surroundings. This makes them ideal for applications that require safe robot-human interaction, but also presents modeling and control challenges. Unlike rigid-bodied robots, soft robots exhibit continuous deformation and coupling between structure and actuation and these behaviors are not readily captured by traditional robot modeling and control techniques except under restrictive simplifying assumptions.
The contribution of this work is a modeling and control framework tailored specifically to soft robots. It consists of two distinct modeling approaches. The first is a physics-based static modeling approach for systems of fluid-driven actuators. This approach leverages geometric relationships and conservation of energy to derive models that are simple and accurate enough to inform the design of soft robots, but not accurate enough to inform their control. The second is a data-driven dynamical modeling approach for arbitrary (soft) robotic systems. This approach leverages Koopman operator theory to construct models that are accurate and computationally efficient enough to be integrated into closed-loop optimal control schemes.
The proposed framework is applied to several real-world soft robotic systems, enabling the successful completion of control tasks such as trajectory following and manipulating objects of unknown mass. Since the framework is not robot specific, it has the potential to become the dominant paradigm for the modeling and control of soft robots and lead to their more widespread adoption.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163062/1/bruderd_1.pd
Whole-body MPC for highly redundant legged manipulators: experimental evaluation with a 37 DoF dual-arm quadruped
Recent progress in legged locomotion has rendered quadruped manipulators a
promising solution for performing tasks that require both mobility and
manipulation (loco-manipulation). In the real world, task specifications and/or
environment constraints may require the quadruped manipulator to be equipped
with high redundancy as well as whole-body motion coordination capabilities.
This work presents an experimental evaluation of a whole-body Model Predictive
Control (MPC) framework achieving real-time performance on a dual-arm quadruped
platform consisting of 37 actuated joints. To the best of our knowledge this is
the legged manipulator with the highest number of joints to be controlled with
real-time whole-body MPC so far. The computational efficiency of the MPC while
considering the full robot kinematics and the centroidal dynamics model builds
upon an open-source DDP-variant solver and a state-of-the-art optimal control
problem formulation. Differently from previous works on quadruped manipulators,
the MPC is directly interfaced with the low-level joint impedance controllers
without the need of designing an instantaneous whole-body controller. The
feasibility on the real hardware is showcased using the CENTAURO platform for
the challenging task of picking a heavy object from the ground. Dynamic
stepping (trotting) is also showcased for first time with this robot. The
results highlight the potential of replanning with whole-body information in a
predictive control loop.Comment: Accepted at the 2023 IEEE-RAS International Conference on Humanoid
Robots (Humanoids 2023), final version with video and acknowledgement
Serving Time: Real-Time, Safe Motion Planning and Control for Manipulation of Unsecured Objects
A key challenge to ensuring the rapid transition of robotic systems from the
industrial sector to more ubiquitous applications is the development of
algorithms that can guarantee safe operation while in close proximity to
humans. Motion planning and control methods, for instance, must be able to
certify safety while operating in real-time in arbitrary environments and in
the presence of model uncertainty. This paper proposes Wrench Analysis for
Inertial Transport using Reachability (WAITR), a certifiably safe motion
planning and control framework for serial link manipulators that manipulate
unsecured objects in arbitrary environments. WAITR uses reachability analysis
to construct over-approximations of the contact wrench applied to unsecured
objects, which captures uncertainty in the manipulator dynamics, the object
dynamics, and contact parameters such as the coefficient of friction. An
optimization problem formulation is presented that can be solved in real-time
to generate provably-safe motions for manipulating the unsecured objects. This
paper illustrates that WAITR outperforms state of the art methods in a variety
of simulation experiments and demonstrates its performance in the real-world.Comment: 8 pages, 3 figures. For project page with code, videos, and
supplementary appendices, see https://roahmlab.github.io/waitr-dev/. arXiv
admin note: text overlap with arXiv:2301.1330
Full-Body Torque-Level Non-linear Model Predictive Control for Aerial Manipulation
Non-linear model predictive control (nMPC) is a powerful approach to control
complex robots (such as humanoids, quadrupeds, or unmanned aerial manipulators
(UAMs)) as it brings important advantages over other existing techniques. The
full-body dynamics, along with the prediction capability of the optimal control
problem (OCP) solved at the core of the controller, allows to actuate the robot
in line with its dynamics. This fact enhances the robot capabilities and
allows, e.g., to perform intricate maneuvers at high dynamics while optimizing
the amount of energy used. Despite the many similarities between humanoids or
quadrupeds and UAMs, full-body torque-level nMPC has rarely been applied to
UAMs.
This paper provides a thorough description of how to use such techniques in
the field of aerial manipulation. We give a detailed explanation of the
different parts involved in the OCP, from the UAM dynamical model to the
residuals in the cost function. We develop and compare three different nMPC
controllers: Weighted MPC, Rail MPC, and Carrot MPC, which differ on the
structure of their OCPs and on how these are updated at every time step. To
validate the proposed framework, we present a wide variety of simulated case
studies. First, we evaluate the trajectory generation problem, i.e., optimal
control problems solved offline, involving different kinds of motions (e.g.,
aggressive maneuvers or contact locomotion) for different types of UAMs. Then,
we assess the performance of the three nMPC controllers, i.e., closed-loop
controllers solved online, through a variety of realistic simulations. For the
benefit of the community, we have made available the source code related to
this work.Comment: Submitted to Transactions on Robotics. 17 pages, 16 figure
Model Predictive Control of a Two-Link Flexible Manipulator
Flexible manipulators are widely used because of the many advantages it provides like low weight, low power consumption leading to low overall cost. However due to the inherent structural flexibility they undergo vibrations and take time to come to the desired position once the actuating force is removed .The most crucial problems associated while designing a feedback control system for a flexible-link are that the system being non-minimum phase, under-actuated and non-collocated because of the physical separation between the actuators and the sensors. Moreover from mathematical point of view we can say that the dynamics of the rigid link robot can be derived assuming the total mass to be concentrated at centre of gravity of the body hence dynamics of the robot would result in terms of differential equations. On contrary flexible robot position is not constant and hence partial differential equation is used to represent the distributed nature of position which results in large number of equations increasing the computational effort. In this work a two link flexible manipulator is modelled using Assumed Mode Method considering two modes of vibration. Further fuzzy identification is also performed using T-S modelling approach which minimises the computation and takes into account higher modes of vibration. The input spaces consists of the torque inputs to the link and membership function of Gaussian form is chosen. The consequent parameters are calculated using Least Square Algorithm. For controlling the tip vibration a controller is designed using Model Predictive Control. The Model Predictive Control is an optimal control method in which the control law is calculated using the system output. MPC is widely used in the industry due to its better performance. The results are compared with another controller based on Linear Quadratic Regulator
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