2,184 research outputs found
A Self-Tuning Impedance-based Interaction Planner for Robotic Haptic Exploration
This paper presents a novel interaction planning method that exploits
impedance tuning techniques in response to environmental uncertainties and
unpredictable conditions using haptic information only. The proposed algorithm
plans the robot's trajectory based on the haptic interaction with the
environment and adapts planning strategies as needed. Two approaches are
considered: Exploration and Bouncing strategies. The Exploration strategy takes
the actual motion of the robot into account in planning, while the Bouncing
strategy exploits the forces and the motion vector of the robot. Moreover,
self-tuning impedance is performed according to the planned trajectory to
ensure compliant contact and low contact forces. In order to show the
performance of the proposed methodology, two experiments with a
torque-controller robotic arm are carried out. The first considers a maze
exploration without obstacles, whereas the second includes obstacles. The
proposed method performance is analyzed and compared against previously
proposed solutions in both cases. Experimental results demonstrate that: i) the
robot can successfully plan its trajectory autonomously in the most feasible
direction according to the interaction with the environment, and ii) a
compliant interaction with an unknown environment despite the uncertainties is
achieved. Finally, a scalability demonstration is carried out to show the
potential of the proposed method under multiple scenarios.Comment: 8 pages, 9 figures, accepted for IEEE Robotics and Automation Letters
(RA-L) and IEEE/RSJ International Conference on Intelligent Robots and
Systems 202
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
Decentralized Ability-Aware Adaptive Control for Multi-robot Collaborative Manipulation
Multi-robot teams can achieve more dexterous, complex and heavier payload
tasks than a single robot, yet effective collaboration is required. Multi-robot
collaboration is extremely challenging due to the different kinematic and
dynamics capabilities of the robots, the limited communication between them,
and the uncertainty of the system parameters. In this paper, a Decentralized
Ability-Aware Adaptive Control is proposed to address these challenges based on
two key features. Firstly, the common manipulation task is represented by the
proposed nominal task ellipsoid, which is used to maximize each robot force
capability online via optimizing its configuration. Secondly, a decentralized
adaptive controller is designed to be Lyapunov stable in spite of heterogeneous
actuation constraints of the robots and uncertain physical parameters of the
object and environment. In the proposed framework, decentralized coordination
and load distribution between the robots is achieved without communication,
while only the control deficiency is broadcast if any of the robots reaches its
force limits. In this case, the object reference trajectory is modified in a
decentralized manner to guarantee stable interaction. Finally, we perform
several numerical and physical simulations to analyse and verify the proposed
method with heterogeneous multi-robot teams in collaborative manipulation
tasks.Comment: The article has been submitted to IEEE Robotics and Automation
Letters (RA-L) with ICRA 2021 conference option; the article has been
accepted for publication in RA-
Predictive Context-Based Adaptive Compliance for Interaction Control of Robot Manipulators
In classical industrial robotics, robots are concealed within structured and well-known environments performing highly-repetitive tasks. In contrast, current robotic applications require more direct interaction with humans, cooperating with them to achieve a common task and entering home scenarios. Above all, robots are leaving the world of certainty to work in dynamically-changing and unstructured environments that might be partially or completely unknown to them. In such environments, controlling the interaction forces that appear when a robot contacts a certain environment (be the environment an object or a person) is of utmost importance. Common sense suggests the need to leave the stiff industrial robots and move towards compliant and adaptive robot manipulators that resemble the properties of their biological counterpart, the human arm. This thesis focuses on creating a higher level of intelligence for active compliance control methods applied to robot manipulators. This work thus proposes an architecture for compliance regulation named Predictive Context-Based Adaptive Compliance (PCAC) which is composed of three main components operating around a 'classical' impedance controller. Inspired by biological systems, the highest-level component is a Bayesian-based context predictor that allows the robot to pre-regulate the arm compliance based on predictions about the context the robot is placed in. The robot can use the information obtained while contacting the environment to update its context predictions and, in case it is necessary, to correct in real time for wrongly predicted contexts. Thus, the predictions are used both for anticipating actions to be taken 'before' proceeding with a task as well as for applying real-time corrective measures 'during' the execution of a in order to ensure a successful performance. Additionally, this thesis investigates a second component to identify the current environment among a set of known environments. This in turn allows the robot to select the proper compliance controller. The third component of the architecture presents the use of neuroevolutionary techniques for selecting the optimal parameters of the interaction controller once a certain environment has been identified
Design of an Elastic Actuation System for a Gait-Assistive Active Orthosis for Incomplete Spinal Cord Injured Subjects
A spinal cord injury severely reduces the quality of life of affected people. Following the injury,
limitations of the ability to move may occur due to the disruption of the motor and sensory functions
of the nervous system depending on the severity of the lesion. An active stance-control
knee-ankle-foot orthosis was developed and tested in earlier works to aid incomplete SCI subjects
by increasing their mobility and independence. This thesis aims at the incorporation of
elastic actuation into the active orthosis to utilise advantages of the compliant system regarding
efficiency and human-robot interaction as well as the reproduction of the phyisological compliance
of the human joints. Therefore, a model-based procedure is adapted to the design of
an elastic actuation system for a gait-assisitve active orthosis. A determination of the optimal
structure and parameters is undertaken via optimisation of models representing compliant actuators
with increasing level of detail. The minimisation of the energy calculated from the positive
amount of power or from the absolute power of the actuator generating one human-like gait cycle
yields an optimal series stiffness, which is similar to the physiological stiffness of the human
knee during the stance phase. Including efficiency factors for components, especially the consideration
of the electric model of an electric motor yields additional information. A human-like
gait cycle contains high torque and low velocities in the stance phase and lower torque combined
with high velocities during the swing. Hence, the efficiency of an electric motor with a gear unit
is only high in one of the phases. This yields a conceptual design of a series elastic actuator with
locking of the actuator position during the stance phase. The locked position combined with the
series compliance allows a reproduction of the characteristics of the human gait cycle during
the stance phase. Unlocking the actuator position for the swing phase enables the selection of
an optimal gear ratio to maximise the recuperable energy. To evaluate the developed concept,
a laboratory specimen based on an electric motor, a harmonic drive gearbox, a torsional series
spring and an electromagnetic brake is designed and appropriate components are selected. A
control strategy, based on impedance control, is investigated and extended with a finite state
machine to activate the locking mechanism. The control scheme and the laboratory specimen
are implemented at a test bench, modelling the foot and shank as a pendulum articulated at the
knee. An identification of parameters yields high and nonlinear friction as a problem of the system,
which reduces the energy efficiency of the system and requires appropriate compensation.
A comparison between direct and elastic actuation shows similar results for both systems at the
test bench, showing that the increased complexity due to the second degree of freedom and
the elastic behaviour of the actuator is treated properly. The final proof of concept requires the
implementation at the active orthosis to emulate uncertainties and variations occurring during
the human gait
A survey of robot manipulation in contact
In this survey, we present the current status on robots performing manipulation tasks that require varying contact with the environment, such that the robot must either implicitly or explicitly control the contact force with the environment to complete the task. Robots can perform more and more manipulation tasks that are still done by humans, and there is a growing number of publications on the topics of (1) performing tasks that always require contact and (2) mitigating uncertainty by leveraging the environment in tasks that, under perfect information, could be performed without contact. The recent trends have seen robots perform tasks earlier left for humans, such as massage, and in the classical tasks, such as peg-in-hole, there is a more efficient generalization to other similar tasks, better error tolerance, and faster planning or learning of the tasks. Thus, in this survey we cover the current stage of robots performing such tasks, starting from surveying all the different in-contact tasks robots can perform, observing how these tasks are controlled and represented, and finally presenting the learning and planning of the skills required to complete these tasks
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