143 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
Multi-segmented Adaptive Feet for Versatile Legged Locomotion in Natural Terrain
Most legged robots are built with leg structures from serially mounted links
and actuators and are controlled through complex controllers and sensor
feedback. In comparison, animals developed multi-segment legs, mechanical
coupling between joints, and multi-segmented feet. They run agile over all
terrains, arguably with simpler locomotion control. Here we focus on developing
foot mechanisms that resist slipping and sinking also in natural terrain. We
present first results of multi-segment feet mounted to a bird-inspired robot
leg with multi-joint mechanical tendon coupling. Our one- and two-segment,
mechanically adaptive feet show increased viable horizontal forces on multiple
soft and hard substrates before starting to slip. We also observe that
segmented feet reduce sinking on soft substrates compared to ball-feet and
cylinder-feet. We report how multi-segmented feet provide a large range of
viable centre of pressure points well suited for bipedal robots, but also for
quadruped robots on slopes and natural terrain. Our results also offer a
functional understanding of segmented feet in animals like ratite birds
Aggressive Aerial Grasping using a Soft Drone with Onboard Perception
Contrary to the stunning feats observed in birds of prey, aerial manipulation
and grasping with flying robots still lack versatility and agility.
Conventional approaches using rigid manipulators require precise positioning
and are subject to large reaction forces at grasp, which limit performance at
high speeds. The few reported examples of aggressive aerial grasping rely on
motion capture systems, or fail to generalize across environments and grasp
targets. We describe the first example of a soft aerial manipulator equipped
with a fully onboard perception pipeline, capable of robustly localizing and
grasping visually and morphologically varied objects. The proposed system
features a novel passively closing tendon-actuated soft gripper that enables
fast closure at grasp, while compensating for position errors, complying to the
target-object morphology, and dampening reaction forces. The system includes an
onboard perception pipeline that combines a neural-network-based semantic
keypoint detector with a state-of-the-art robust 3D object pose estimator,
whose estimate is further refined using a fixed-lag smoother. The resulting
pose estimate is passed to a minimum-snap trajectory planner, tracked by an
adaptive controller that fully compensates for the added mass of the grasped
object. Finally, a finite-element-based controller determines optimal gripper
configurations for grasping. Rigorous experiments confirm that our approach
enables dynamic, aggressive, and versatile grasping. We demonstrate fully
onboard vision-based grasps of a variety of objects, in both indoor and outdoor
environments, and up to speeds of 2.0 m/s -- the fastest vision-based grasp
reported in the literature. Finally, we take a major step in expanding the
utility of our platform beyond stationary targets, by demonstrating
motion-capture-based grasps of targets moving up to 0.3 m/s, with relative
speeds up to 1.5 m/s
Adaptive foot design for small quadruped robots
Biologically inspired robots that are used for research of the animal and the technological realm become more and more refined. Control schemes for sensor-less and sensorized robots were developed, are able to handle torque control and sometimes even adapt to a changing task set. Further mechanics and electronics have evolved and take part in more reliable and robust bio-inspired robots. Robots reproduce animal structures or use bio-mechanical principles to excel in a specific task. Never the less, during this evolution of robots the feet were often oversimplified compared to their animal counterparts. Our current work centers around the foot as a bio-mechanically complex but extremely important end-effector
Legged Robots for Object Manipulation: A Review
Legged robots can have a unique role in manipulating objects in dynamic,
human-centric, or otherwise inaccessible environments. Although most legged
robotics research to date typically focuses on traversing these challenging
environments, many legged platform demonstrations have also included "moving an
object" as a way of doing tangible work. Legged robots can be designed to
manipulate a particular type of object (e.g., a cardboard box, a soccer ball,
or a larger piece of furniture), by themselves or collaboratively. The
objective of this review is to collect and learn from these examples, to both
organize the work done so far in the community and highlight interesting open
avenues for future work. This review categorizes existing works into four main
manipulation methods: object interactions without grasping, manipulation with
walking legs, dedicated non-locomotive arms, and legged teams. Each method has
different design and autonomy features, which are illustrated by available
examples in the literature. Based on a few simplifying assumptions, we further
provide quantitative comparisons for the range of possible relative sizes of
the manipulated object with respect to the robot. Taken together, these
examples suggest new directions for research in legged robot manipulation, such
as multifunctional limbs, terrain modeling, or learning-based control, to
support a number of new deployments in challenging indoor/outdoor scenarios in
warehouses/construction sites, preserved natural areas, and especially for home
robotics.Comment: Preprint of the paper submitted to Frontiers in Mechanical
Engineerin
Benchmarking Agility For Multilegged Terrestrial Robots
In this paper, we present a novel and practical approach for benchmarking agility. We focus on terrestrial, multilegged locomotion in the field of bio-inspired robotics. We define agility as the ability to perform a set of different but specific tasks executed in a fast and efficient manner. This definition is inspired by the analysis of natural role models, such as dogs and horses as well as robotic systems. An evaluation of existing benchmarks in robotics is done and taken into account in our proposed benchmark. After the general definition, the actual normalized benchmarking values are defined, and measuring methods, as well as an online database for agility score collection and distribution, are presented. To provide a baseline for agile locomotion, various videos of dog-agility competitions were analyzed and agility scores calculated wherever applicable. Finally, validation and implementation of the benchmark are done with different robots directly available to the authors. In conclusion, our benchmark will enable researchers not only to compare existing robots and find out strengths and weaknesses in different design approaches, but also give a tool to define new fitness functions for optimization, learning processes, and future robots developments, intensifying the links between biology and technology even further
Motion Planning and Control of Dynamic Humanoid Locomotion
Inspired by human, humanoid robots has the potential to become a general-purpose platform that lives along with human. Due to the technological advances in many field, such as actuation, sensing, control and intelligence, it finally enables humanoid robots to possess human comparable capabilities. However, humanoid locomotion is still a challenging research field. The large number of degree of freedom structure makes the system difficult to coordinate online. The presence of various contact constraints and the hybrid nature of locomotion tasks make the planning a harder problem to solve. Template model anchoring approach has been adopted to bridge the gap between simple model behavior and the whole-body motion of humanoid robot.
Control policies are first developed for simple template models like Linear Inverted Pendulum Model (LIPM) or Spring Loaded Inverted Pendulum(SLIP), the result controlled behaviors are then been mapped to the whole-body motion of humanoid robot through optimization-based task-space control strategies. Whole-body humanoid control framework has been verified on various contact situations such as unknown uneven terrain, multi-contact scenarios and moving platform and shows its generality and versatility. For walking motion, existing Model Predictive Control approach based on LIPM has been extended to enable the robot to walk without any reference foot placement anchoring. It is kind of discrete version of \u201cwalking without thinking\u201d.
As a result, the robot could achieve versatile locomotion modes such as automatic foot placement with single reference velocity command, reactive stepping under large external disturbances, guided walking with small constant external pushing forces, robust walking on unknown uneven terrain, reactive stepping in place when blocked by external barrier. As an extension of this proposed framework, also to increase the push recovery capability of the humanoid robot, two new configurations have been proposed to enable the robot to perform cross-step motions. For more dynamic hopping and running motion, SLIP model has been chosen as the template model. Different from traditional model-based analytical approach, a data-driven approach has been proposed to encode the dynamics of the this model. A deep neural network is trained offline with a large amount of simulation data based on the SLIP model to learn its dynamics.
The trained network is applied online to generate reference foot placements for the humanoid robot. Simulations have been performed to evaluate the effectiveness of the proposed approach in generating bio-inspired and robust running motions. The method proposed based on 2D SLIP model can be generalized to 3D SLIP model and the extension has been briefly mentioned at the end
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