1,586 research outputs found
Gait Generation and Optimization for Legged Robots
This paper presents a general framework for representing and generating gaitsfor legged robots. We introduce a convenient parametrization of gait generators as dynamical systems possessing specified stable limit cycles over an appropriate torus. Inspired by biology, this parametrization affords a continuous selection of operation within a coordination design plane spanned by axes that determine the mix of āfeedforward/feedbackā and centralized/decentralizedā control. Applying optimization to the parameterized gait generation system allowed RHex, our robotic hexapod, to learn new gaits demonstrating significant performance increases. For example, RHex can now run at 2.4m/s (up from 0.8m/s), run with a specific resistance of 0.6 (down from 2.0), climb 45ā¦ inclines (up from 25ā¦), and traverse 35ā¦ inclines (up from 15ā¦)
Information driven self-organization of complex robotic behaviors
Information theory is a powerful tool to express principles to drive
autonomous systems because it is domain invariant and allows for an intuitive
interpretation. This paper studies the use of the predictive information (PI),
also called excess entropy or effective measure complexity, of the sensorimotor
process as a driving force to generate behavior. We study nonlinear and
nonstationary systems and introduce the time-local predicting information
(TiPI) which allows us to derive exact results together with explicit update
rules for the parameters of the controller in the dynamical systems framework.
In this way the information principle, formulated at the level of behavior, is
translated to the dynamics of the synapses. We underpin our results with a
number of case studies with high-dimensional robotic systems. We show the
spontaneous cooperativity in a complex physical system with decentralized
control. Moreover, a jointly controlled humanoid robot develops a high
behavioral variety depending on its physics and the environment it is
dynamically embedded into. The behavior can be decomposed into a succession of
low-dimensional modes that increasingly explore the behavior space. This is a
promising way to avoid the curse of dimensionality which hinders learning
systems to scale well.Comment: 29 pages, 12 figure
Gait Generation and Control in a Climbing Hexapod Robot
We discuss the gait generation and control architecture of a bioinspired climbing robot that presently climbs a variety of vertical surfaces, including carpet, cork and a growing range of stucco-like surfaces in the quasi-static regime. The initial version of the robot utilizes a collection of gaits (cyclic feed-forward motion patterns) to locomote over these surfaces, with each gait tuned for a specific surface and set of operating conditions. The need for more flexibility in gait specification (e.g., adjusting number of feet on the ground), more intricate shaping of workspace motions (e.g., shaping the details of the foot attachment and detachment trajectories), and the need to encode gait ātransitionsā (e.g., tripod to pentapod gait structure) has led us to separate this trajectory generation scheme into the functional composition of a phase assigning transformation of the āclock spaceā (the six dimensional torus) followed by a map from phase into leg joints that decouples the geometric details of a particular gait. This decomposition also supports the introduction of sensory feedback to allow recovery from unexpected event and to adapt to changing surface geometries
Controlling rigid formations of mobile agents under inconsistent measurements
Despite the great success of using gradient-based controllers to stabilize
rigid formations of autonomous agents in the past years, surprising yet
intriguing undesirable collective motions have been reported recently when
inconsistent measurements are used in the agents' local controllers. To make
the existing gradient control robust against such measurement inconsistency, we
exploit local estimators following the well known internal model principle for
robust output regulation control. The new estimator-based gradient control is
still distributed in nature and can be constructed systematically even when the
number of agents in a rigid formation grows. We prove rigorously that the
proposed control is able to guarantee exponential convergence and then
demonstrate through robotic experiments and computer simulations that the
reported inconsistency-induced orbits of collective movements are effectively
eliminated.Comment: 10 page
Multistable Phase Regulation for Robust Steady and Transitional Legged Gaits
We develop robust methods that allow speciļ¬cation, control, and transition of a multi-legged robotās stepping patternāits gaitāduring active locomotion over natural terrain. Resulting gaits emerge through the introduction of controllers that impose appropriately-placed repellors within the space of gaits, the torus of relative leg phases, thereby mitigating against dangerous patterns of leg timing. Moreover, these repellors are organized with respect to a natural cellular decomposition of gait space and result in limit cycles with associated basins that are well characterized by these cells, thus conferring a symbolic character upon the overall behavioral repertoire. These ideas are particularly applicable to four- and six-legged robots, for which a large variety of interesting and useful (and, in many cases, familiar) gaits exist, and whose tradeoļ¬s between speed and reliability motivate the desire for transitioning between them during active locomotion. We provide an empirical instance of this gait regulation scheme by application to a climbing hexapod, whose āphysical layerā sensor-feedback control requires adequate grasp of a climbing surface but whose closed loop control perturbs the robot from its desired gait. We document how the regulation scheme secures the desired gait and permits operator selection of diļ¬erent gaits as required during active climbing on challenging surfaces
The Role of Reflexes Versus Central Pattern Generators
Animals execute locomotor behaviors and more with ease. They have evolved these breath-taking abilities over millions of years. Cheetahs can run, dolphins can swim and flies can fly like no artificial technology can. It is often argued that if human technology could mimic nature, then biological-like performance would follow. Unfortunately, the blind copying or mimicking of a part of nature [Ritzmann et al., 2000] does not often lead to the best design for a variety of reasons [Vogel, 1998]. Evolution works on the just good enough principle. Optimal designs are not the necessary end product of evolution. Multiple satisfactory solutions can result in similar performances. Animals do bring to our attention amazing designs, but these designs carry with them the baggage of their history. Moreover, natural design is constrained by factors that may have no relationship to human engineered designs. Animals must be able to grow over time, but still function along the way. Finally, animals are complex and their parts serve multiple functions, not simply the one we happen to examine. In short, in their daunting complexity and integrated function, understanding animal behaviors remains as intractable as their capabilities are tantalizing
Phase Synchronization Control of Robotic Networks on Periodic Ellipses with Adaptive Network Topologies
This paper presents a novel formation control method for a large number of robots or vehicles described by Euler-Lagrange (EL) systems moving in elliptical orbits. A new
coordinate transformation method for phase synchronization of networked EL systems in elliptical trajectories is introduced to define desired formation patterns. The proposed phase synchronization controller synchronizes the motions of agents, thereby yielding a smaller synchronization error than an uncoupled control law in the presence of bounded disturbances. A complex time-varying and switching network topology, constructed by the
adaptive graph Laplacian matrix, relaxes the standard requirement of consensus stability, even permitting stabilization on an arbitrary unbalanced graph. The proofs of stability are constructed by robust contraction analysis, a relatively new nonlinear stability tool. An
example of reconfiguring swarms of spacecraft in Low Earth Orbit shows the effectiveness of the proposed phase synchronization controller for a large number of complex EL systems moving in elliptical orbits
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