3,137 research outputs found
Adaptive Parallel Iterative Deepening Search
Many of the artificial intelligence techniques developed to date rely on
heuristic search through large spaces. Unfortunately, the size of these spaces
and the corresponding computational effort reduce the applicability of
otherwise novel and effective algorithms. A number of parallel and distributed
approaches to search have considerably improved the performance of the search
process. Our goal is to develop an architecture that automatically selects
parallel search strategies for optimal performance on a variety of search
problems. In this paper we describe one such architecture realized in the
Eureka system, which combines the benefits of many different approaches to
parallel heuristic search. Through empirical and theoretical analyses we
observe that features of the problem space directly affect the choice of
optimal parallel search strategy. We then employ machine learning techniques to
select the optimal parallel search strategy for a given problem space. When a
new search task is input to the system, Eureka uses features describing the
search space and the chosen architecture to automatically select the
appropriate search strategy. Eureka has been tested on a MIMD parallel
processor, a distributed network of workstations, and a single workstation
using multithreading. Results generated from fifteen puzzle problems, robot arm
motion problems, artificial search spaces, and planning problems indicate that
Eureka outperforms any of the tested strategies used exclusively for all
problem instances and is able to greatly reduce the search time for these
applications
Optimal Energy Shaping Control for a Backdrivable Hip Exoskeleton
Task-dependent controllers widely used in exoskeletons track predefined
trajectories, which overly constrain the volitional motion of individuals with
remnant voluntary mobility. Energy shaping, on the other hand, provides
task-invariant assistance by altering the human body's dynamic characteristics
in the closed loop. While human-exoskeleton systems are often modeled using
Euler-Lagrange equations, in our previous work we modeled the system as a
port-controlled-Hamiltonian system, and a task-invariant controller was
designed for a knee-ankle exoskeleton using interconnection-damping assignment
passivity-based control. In this paper, we extend this framework to design a
controller for a backdrivable hip exoskeleton to assist multiple tasks. A set
of basis functions that contains information of kinematics is selected and
corresponding coefficients are optimized, which allows the controller to
provide torque that fits normative human torque for different activities of
daily life. Human-subject experiments with two able-bodied subjects
demonstrated the controller's capability to reduce muscle effort across
different tasks
Influence of the controller design on the accuracy of a forward dynamic simulation of human gait
The analysis of a captured motion can be addressed by means of forward or inverse dynamics approaches. For this purpose, a 12 segment 2D model with 14 degrees of freedom is developed and both methods are implemented using multibody dynamics techniques. The inverse dynamic analysis uses the experimentally captured motion to calculate the joint torques produced by the musculoskeletal system during the movement. This information is then used as input data for a forward dynamic analysis without any control design. This approach is able to reach the desired pattern within half cycle. In order to achieve the simulation of the complete gait cycle two different control strategies are implemented to stabilize all degrees of freedom: a proportional derivative (PD) control and a computed torque control (CTC). The selection of the control parameters is presented in this work: a kinematic perturbation is used for tuning PD gains, and pole placement techniques are used in order to determine the CTC parameters. A performance evaluation of the two controllers is done in order to quantify the accuracy of the simulated motion and the control torques needed when using one or the other control approach to track a known human walking pattern.Postprint (author's final draft
Pepper, Just Show Me The Way! How Robotic Shopping Assistants Should Look And Act
Artificial intelligence enables modern robots to serve as service and sales assistants. Today\u27s robotic shopping assistants (RSAs) can appear either humanoid or non-humanoid and possess utilitarian and/or hedonic attributes. However, many questions remain unexplored regarding an effective customer-centric RSA design. Do customers prefer a humanoid or non-humanoid RSA with hedonic or utilitarian attributes? To answer those questions, the research deploys a mixed-method approach involving a survey of customers who have interacted with the Pepper Robot, a humanoid robot (Study 1), and follow-up experiments examining customer responses to a humanoid/non-humanoid RSA with hedonic/utilitarian attributes (Studies 2 and 3). The research employs an innovative approach that analyzes both unstructured and structured data simultaneously. Study results suggest that customers prefer humanoid RSAs with utilitarian attributes over those with hedonic attributes. The research contributes to the literature by proposing hedonic (vs. utilitarian) attributes of RSAs as new drivers of anthropomorphic perceptions
Hamiltonian Dynamics Learning from Point Cloud Observations for Nonholonomic Mobile Robot Control
Reliable autonomous navigation requires adapting the control policy of a
mobile robot in response to dynamics changes in different operational
conditions. Hand-designed dynamics models may struggle to capture model
variations due to a limited set of parameters. Data-driven dynamics learning
approaches offer higher model capacity and better generalization but require
large amounts of state-labeled data. This paper develops an approach for
learning robot dynamics directly from point-cloud observations, removing the
need and associated errors of state estimation, while embedding Hamiltonian
structure in the dynamics model to improve data efficiency. We design an
observation-space loss that relates motion prediction from the dynamics model
with motion prediction from point-cloud registration to train a Hamiltonian
neural ordinary differential equation. The learned Hamiltonian model enables
the design of an energy-shaping model-based tracking controller for rigid-body
robots. We demonstrate dynamics learning and tracking control on a real
nonholonomic wheeled robot.Comment: 8 pages, 6 figure
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
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