1,385 research outputs found
Safety-related Tasks within the Set-Based Task-Priority Inverse Kinematics Framework
In this paper we present a framework that allows the motion control of a
robotic arm automatically handling different kinds of safety-related tasks. The
developed controller is based on a Task-Priority Inverse Kinematics algorithm
that allows the manipulator's motion while respecting constraints defined
either in the joint or in the operational space in the form of equality-based
or set-based tasks. This gives the possibility to define, among the others,
tasks as joint-limits, obstacle avoidance or limiting the workspace in the
operational space. Additionally, an algorithm for the real-time computation of
the minimum distance between the manipulator and other objects in the
environment using depth measurements has been implemented, effectively allowing
obstacle avoidance tasks. Experiments with a Jaco manipulator, operating in
an environment where an RGB-D sensor is used for the obstacles detection, show
the effectiveness of the developed system
Control of Redundant Robots Under Hard Joint Constraints: Saturation in the Null Space
We present an efficient method for addressing online the inversion of differential task kinematics for redundant manipulators, in the presence of hard limits on joint space motion that can never be violated. The proposed SNS (Saturation in the Null Space) algorithm proceeds by successively discarding the use of joints that would exceed their motion bounds when using the minimum norm solution. When processing multiple tasks with priority, the SNS method realizes a preemptive strategy by preserving the correct order of priority in spite of the presence of saturations. In the single- and multi-task case, the algorithm automatically integrates a least possible task scaling procedure, when an original task is found to be unfeasible. The optimality properties of the SNS algorithm are analyzed by considering an associated Quadratic Programming problem. Its solution leads to a variant of the algorithm, which guarantees optimality also when the basic SNS algorithm does not. Numerically efficient versions of these algorithms are proposed. Their performance allows real-time control of robots executing many prioritized tasks with a large number of hard bounds. Experimental results are reported
A General Framework for Hierarchical Redundancy Resolution Under Arbitrary Constraints
The increasing interest in autonomous robots with a high number of degrees of
freedom for industrial applications and service robotics demands control
algorithms to handle multiple tasks as well as hard constraints efficiently.
This paper presents a general framework in which both kinematic (velocity- or
acceleration-based) and dynamic (torque-based) control of redundant robots are
handled in a unified fashion. The framework allows for the specification of
redundancy resolution problems featuring a hierarchy of arbitrary (equality and
inequality) constraints, arbitrary weighting of the control effort in the cost
function and an additional input used to optimize possibly remaining
redundancy. To solve such problems, a generalization of the Saturation in the
Null Space (SNS) algorithm is introduced, which extends the original method
according to the features required by our general control framework. Variants
of the developed algorithm are presented, which ensure both efficient
computation and optimality of the solution. Experiments on a KUKA LBRiiwa
robotic arm, as well as simulations with a highly redundant mobile manipulator
are reported.Comment: 19 pages, 19 figures, submitted to the IEE
Motion Control of the Hybrid Wheeled-Legged Quadruped Robot Centauro
Emerging applications will demand robots to deal with a complex environment, which lacks the structure and predictability of the industrial workspace. Complex scenarios will require robot complexity to increase as well, as compared to classical topologies such as fixed-base manipulators, wheeled mobile platforms, tracked vehicles, and their combinations. Legged robots, such as humanoids and quadrupeds, promise to provide platforms which are flexible enough to handle real world scenarios; however, the improved flexibility comes at the cost of way higher control complexity. As a trade-off, hybrid wheeled-legged robots have been proposed, resulting in the mitigation of control complexity whenever the ground surface is suitable for driving. Following this idea, a new hybrid robot called Centauro has been developed inside the Humanoid and Human Centered Mechatronics lab at Istituto Italiano di Tecnologia (IIT). Centauro is a wheeled-legged quadruped with a humanoid bi-manual upper-body. Differently from other platform of similar concept, Centauro employs customized actuation units, which provide high torque outputs, moderately fast motions, and the possibility to control the exerted torque. Moreover, with more than forty motors moving its limbs, Centauro is a very redundant platform, with the potential to execute many different tasks at the same time. This thesis deals with the design and development of a software architecture, and a control system, tailored to such a robot; both wheeled and legged locomotion strategies have been studied, as well as prioritized, whole-body and interaction controllers exploiting the robot torque control capabilities, and capable to handle the system redundancy. A novel software architecture, made of (i) a real-time robotic middleware, and (ii) a framework for online, prioritized Cartesian controller, forms the basis of the entire work
Ein hierarchisches Framework fĂĽr physikalische Mensch-Roboter-Interaktion
Robots are becoming more than machines performing repetitive tasks behind safety fences, and are expected to perform multiple complex tasks and work together with a human. For that purpose, modern robots are commonly built with two main characteristics: a large number of joints to increase their versatility and the capability to feel the environment through torque/force sensors. Controlling such complex robots requires the development of sophisticated frameworks capable
of handling multiple tasks. Various frameworks have been proposed in the last years to deal with the redundancy caused by a large number of joints. Those hierarchical frameworks prioritize the achievement of the main task with the whole robot capability, while secondary tasks are performed as well as the remaining mobility allows it. This methodology presents considerable drawbacks in applications requiring that the robot respects constraints imposed by, e.g., safety restrictions or physical limitations. One particular case is unilateral constraints imposed by, e.g., joint or workspace limits. To implement them, task hierarchical frameworks rely on the activation of repulsive potential fields when approaching the limit. The performance of the potential field depends on the configuration and speed of the robot. Additionally, speed limitation is commonly required in collaborative scenarios, but it has been insufficiently treated for torque-controlled robots.
With the aim of controlling redundant robots in collaborative scenarios, this thesis proposes a framework that handles multiple tasks under multiple constraints. The robot’s reaction to physical interaction must be intuitive and safe for humans: The robot must not impose high forces on the human or react unexpectedly to external forces. The proposed framework uses novel methods to avoid exceeding position, velocity and acceleration limits in joint and Cartesian spaces.
A comparative study is carried out between different redundancy resolution solvers to contrast the diverse approaches used to solve the redundancy problem. Widely used projector-based and quadratic programming-based hierarchical solvers were experimentally analyzed when reacting to external forces. Experiments were performed using an industrial redundant collaborative robot. Results demonstrate that the proposed method to handle unilateral constraints produces a safe and
expected reaction in the presence of external forces exerted by humans. The robot does not exceed the given limits, while the tasks performed are prioritized hierarchically
On-line Joint Limit Avoidance for Torque Controlled Robots by Joint Space Parametrization
This paper proposes control laws ensuring the stabilization of a time-varying
desired joint trajectory, as well as joint limit avoidance, in the case of
fully-actuated manipulators. The key idea is to perform a parametrization of
the feasible joint space in terms of exogenous states. It follows that the
control of these states allows for joint limit avoidance. One of the main
outcomes of this paper is that position terms in control laws are replaced by
parametrized terms, where joint limits must be avoided. Stability and
convergence of time-varying reference trajectories obtained with the proposed
method are demonstrated to be in the sense of Lyapunov. The introduced control
laws are verified by carrying out experiments on two degrees-of-freedom of the
humanoid robot iCub.Comment: 8 pages, 4 figures. Submitted to the 2016 IEEE-RAS International
Conference on Humanoid Robot
Learning soft task priorities for control of redundant robots
Movement primitives (MPs) provide a powerful
framework for data driven movement generation that has been
successfully applied for learning from demonstrations and robot
reinforcement learning. In robotics we often want to solve a
multitude of different, but related tasks. As the parameters
of the primitives are typically high dimensional, a common
practice for the generalization of movement primitives to new
tasks is to adapt only a small set of control variables, also
called meta parameters, of the primitive. Yet, for most MP
representations, the encoding of these control variables is precoded
in the representation and can not be adapted to the
considered tasks. In this paper, we want to learn the encoding of
task-specific control variables also from data instead of relying
on fixed meta-parameter representations. We use hierarchical
Bayesian models (HBMs) to estimate a low dimensional latent
variable model for probabilistic movement primitives (ProMPs),
which is a recent movement primitive representation. We show
on two real robot datasets that ProMPs based on HBMs
outperform standard ProMPs in terms of generalization and
learning from a small amount of data and also allows for an
intuitive analysis of the movement. We also extend our HBM by
a mixture model, such that we can model different movement
types in the same dataset
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