3,943 research outputs found
Cooperative Adaptive Control for Cloud-Based Robotics
This paper studies collaboration through the cloud in the context of
cooperative adaptive control for robot manipulators. We first consider the case
of multiple robots manipulating a common object through synchronous centralized
update laws to identify unknown inertial parameters. Through this development,
we introduce a notion of Collective Sufficient Richness, wherein parameter
convergence can be enabled through teamwork in the group. The introduction of
this property and the analysis of stable adaptive controllers that benefit from
it constitute the main new contributions of this work. Building on this
original example, we then consider decentralized update laws, time-varying
network topologies, and the influence of communication delays on this process.
Perhaps surprisingly, these nonidealized networked conditions inherit the same
benefits of convergence being determined through collective effects for the
group. Simple simulations of a planar manipulator identifying an unknown load
are provided to illustrate the central idea and benefits of Collective
Sufficient Richness.Comment: ICRA 201
A passivity approach to controller-observer design for robots
Passivity-based control methods for robots, which achieve the control objective by reshaping the robot system's natural energy via state feedback, have, from a practical point of view, some very attractive properties. However, the poor quality of velocity measurements may significantly deteriorate the control performance of these methods. In this paper the authors propose a design strategy that utilizes the passivity concept in order to develop combined controller-observer systems for robot motion control using position measurements only. To this end, first a desired energy function for the closed-loop system is introduced, and next the controller-observer combination is constructed such that the closed-loop system matches this energy function, whereas damping is included in the controller- observer system to assure asymptotic stability of the closed-loop system. A key point in this design strategy is a fine tuning of the controller and observer structure to each other, which provides solutions to the output-feedback robot control problem that are conceptually simple and easily implementable in industrial robot applications. Experimental tests on a two-DOF manipulator system illustrate that the proposed controller-observer systems enable the achievement of higher performance levels compared to the frequently used practice of numerical position differentiation for obtaining a velocity estimat
Active Inference for Integrated State-Estimation, Control, and Learning
This work presents an approach for control, state-estimation and learning
model (hyper)parameters for robotic manipulators. It is based on the active
inference framework, prominent in computational neuroscience as a theory of the
brain, where behaviour arises from minimizing variational free-energy. The
robotic manipulator shows adaptive and robust behaviour compared to
state-of-the-art methods. Additionally, we show the exact relationship to
classic methods such as PID control. Finally, we show that by learning a
temporal parameter and model variances, our approach can deal with unmodelled
dynamics, damps oscillations, and is robust against disturbances and poor
initial parameters. The approach is validated on the `Franka Emika Panda' 7 DoF
manipulator.Comment: 7 pages, 6 figures, accepted for presentation at the International
Conference on Robotics and Automation (ICRA) 202
Global regulation of robots using only position measurements
In this note we propose a simple solution to the regulation problem of rigid robots based on the availability of only joint position measurements. The controller consists of two parts: (1) a gravitation compensation, (2) a linear dynamic first-order compensator. The gravitation compensation part can be chosen to be a function of either the actual joint position or the desired joint position. Both possibilities are aproved to yield global asymptotic stability. Performance issues of the controller are illustrated in a simulation study of a two degrees-of-freedom robot manipulator
Verification and Validation of Robot Manipulator Adaptive Control with Actuator Deficiency
This work addresses the joint tracking problem of robotic manipulators with uncertain dynamical parameters and actuator deficiencies, in the form of an uncertain control effectiveness matrix, through adaptive control design, simulation, and experimentation. Specifically, two novel adaptive controller formulations are implemented and tested via simulation and experimentation. The proposed adaptive control formulations are designed to compensate for uncertainties in the dynamical system parameters as well as uncertainties in the control effectiveness matrix that pre-multiplies the control input. The uncertainty compensation of the dynamical parameters is achieved via the use of the desired model compensation–based adaptation, while the uncertainties related to the control effectiveness matrix are dealt with via two fundamentally different novel adaptation methods, namely with bound-based and projection operator-based methods. The stability of the system states and convergence of the error terms to the origin are proven via Lyapunov–based arguments. Extensive numerical studies are performed on a two–link planar robotic device, and experimental studies are preformed on Quansers QArm to illustrate the effectiveness of both adaptive controllers. In the experimental validation of the theory, both adaptive controllers demonstrate remarkable resilience, maintaining control of the Quanser QArm even with up to an 80% control input deficiency. After tuning the gains, both joints satisfactorily tracked the desired trajectories. When evaluating the entire experiment, the norm of the square of the total error is averaged. The bound-based controller exhibited an average error of 2.816◦ across all cases, while the projection operator-based controller had a reduced average error of 1.012◦ across all cases. Furthermore, over time, there is a noticeable decrease in error for both joints. These results underscore the robustness and effectiveness of the proposed adaptive controllers, even under substantial actuator deficiencies. The results highlight the significance of achieving near-perfect system knowledge and the careful selection of controls for desirable system performanc
Biomimetic Manipulator Control Design for Bimanual Tasks in the Natural Environment
As robots become more prolific in the human environment, it is important that safe operational
procedures are introduced at the same time; typical robot control methods are
often very stiff to maintain good positional tracking, but this makes contact (purposeful
or accidental) with the robot dangerous. In addition, if robots are to work cooperatively
with humans, natural interaction between agents will make tasks easier to perform with
less effort and learning time. Stability of the robot is particularly important in this
situation, especially as outside forces are likely to affect the manipulator when in a close
working environment; for example, a user leaning on the arm, or task-related disturbance
at the end-effector.
Recent research has discovered the mechanisms of how humans adapt the applied force
and impedance during tasks. Studies have been performed to apply this adaptation to
robots, with promising results showing an improvement in tracking and effort reduction
over other adaptive methods. The basic algorithm is straightforward to implement,
and allows the robot to be compliant most of the time and only stiff when required by
the task. This allows the robot to work in an environment close to humans, but also
suggests that it could create a natural work interaction with a human. In addition, no
force sensor is needed, which means the algorithm can be implemented on almost any
robot.
This work develops a stable control method for bimanual robot tasks, which could also
be applied to robot-human interactive tasks. A dynamic model of the Baxter robot is
created and verified, which is then used for controller simulations. The biomimetic control
algorithm forms the basis of the controller, which is developed into a hybrid control
system to improve both task-space and joint-space control when the manipulator is disturbed
in the natural environment. Fuzzy systems are implemented to remove the need
for repetitive and time consuming parameter tuning, and also allows the controller to
actively improve performance during the task. Experimental simulations are performed,
and demonstrate how the hybrid task/joint-space controller performs better than either
of the component parts under the same conditions. The fuzzy tuning method is then applied
to the hybrid controller, which is shown to slightly improve performance as well as
automating the gain tuning process. In summary, a novel biomimetic hybrid controller
is presented, with a fuzzy mechanism to avoid the gain tuning process, finalised with a
demonstration of task-suitability in a bimanual-type situation.EPSR
Bilevel shared control for teleoperators
A shared system is disclosed for robot control including integration of the human and autonomous input modalities for an improved control. Autonomously planned motion trajectories are modified by a teleoperator to track unmodelled target motions, while nominal teleoperator motions are modified through compliance to accommodate geometric errors autonomously in the latter. A hierarchical shared system intelligently shares control over a remote robot between the autonomous and teleoperative portions of an overall control system. Architecture is hierarchical, and consists of two levels. The top level represents the task level, while the bottom, the execution level. In space applications, the performance of pure teleoperation systems depend significantly on the communication time delays between the local and the remote sites. Selection/mixing matrices are provided with entries which reflect how each input's signals modality is weighted. The shared control minimizes the detrimental effects caused by these time delays between earth and space
Cartesian Parallel Manipulator Modeling, Control and Simulation
Ayssam Elkady, Galal Elkobrosy, Sarwat Hanna, and Tarek Sobh's book chapter on robotic parallel manipulators
Model-Based Robot Control and Multiprocessor Implementation
Model-based control of robot manipulators has been gaining momentum in recent years. Unfortunately there are very few experimental validations to accompany simulation results and as such majority of conclusions drawn lack the credibility associated with the real control implementation
Task-space dynamic control of underwater robots
This thesis is concerned with the control aspects for underwater tasks performed by
marine robots. The mathematical models of an underwater vehicle and an underwater
vehicle with an onboard manipulator are discussed together with their associated
properties.
The task-space regulation problem for an underwater vehicle is addressed where the
desired target is commonly specified as a point. A new control technique is proposed
where the multiple targets are defined as sub-regions. A fuzzy technique is used to
handle these multiple sub-region criteria effectively. Due to the unknown gravitational
and buoyancy forces, an adaptive term is adopted in the proposed controller.
An extension to a region boundary-based control law is then proposed for an underwater
vehicle to illustrate the flexibility of the region reaching concept. In this novel
controller, a desired target is defined as a boundary instead of a point or region. For a
mapping of the uncertain restoring forces, a least-squares estimation algorithm and the
inverse Jacobian matrix are utilised in the adaptive control law.
To realise a new tracking control concept for a kinematically redundant robot, subregion
tracking control schemes with a sub-tasks objective are developed for a UVMS.
In this concept, the desired objective is specified as a moving sub-region instead of a
trajectory. In addition, due to the system being kinematically redundant, the controller
also enables the use of self-motion of the system to perform sub-tasks (drag
minimisation, obstacle avoidance, manipulability and avoidance of mechanical joint
limits)
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