47 research outputs found
Towards Agility: Definition, Benchmark and Design Considerations for Small, Quadrupedal Robots
Agile quadrupedal locomotion in animals and robots is yet to be fully understood, quantified
or achieved. An intuitive notion of agility exists, but neither a concise definition nor a common
benchmark can be found. Further, it is unclear, what minimal level of mechatronic complexity
is needed for this particular aspect of locomotion.
In this thesis we address and partially answer two primary questions: (Q1) What is agile
legged locomotion (agility) and how can wemeasure it? (Q2) How can wemake agile legged
locomotion with a robot a reality?
To answer our first question, we define agility for robot and animal alike, building a common
ground for this particular component of locomotion and introduce quantitative measures
to enhance robot evaluation and comparison. The definition is based on and inspired by
features of agility observed in nature, sports, and suggested in robotics related publications.
Using the results of this observational and literature review, we build a novel and extendable
benchmark of thirteen different tasks that implement our vision of quantitatively classifying
agility. All scores are calculated from simple measures, such as time, distance, angles and
characteristic geometric values for robot scaling. We normalize all unit-less scores to reach
comparability between different systems. An initial implementation with available robots and
real agility-dogs as baseline finalize our effort of answering the first question.
Bio-inspired designs introducing and benefiting from morphological aspects present in nature
allowed the generation of fast, robust and energy efficient locomotion. We use engineering
tools and interdisciplinary knowledge transferred from biology to build low-cost robots able
to achieve a certain level of agility and as a result of this addressing our second question. This
iterative process led to a series of robots from Lynx over Cheetah-Cub-S, Cheetah-Cub-AL,
and Oncilla to Serval, a compliant robot with actuated spine, high range of motion in all joints.
Serval presents a high level of mobility at medium speeds. With many successfully implemented
skills, using a basic kinematics-duplication from dogs (copying the foot-trajectories
of real animals and replaying themotion on the robot using a mathematical interpretation),
we found strengths to emphasize, weaknesses to correct and made Serval ready for future
attempts to achieve even more agile locomotion. We calculated Servalâs agility scores with the
result of it performing better than any of its predecessors. Our small, safe and low-cost robot
is able to execute up to 6 agility tasks out of 13 with the potential to reachmore after extended
development. Concluding, we like to mention that Serval is able to cope with step-downs,
smooth, bumpy terrain and falling orthogonally to the ground
Locomation strategies for amphibious robots-a review
In the past two decades, unmanned amphibious robots have proven the most promising and efficient systems ranging from scientific, military, and commercial applications. The applications like monitoring, surveillance, reconnaissance, and military combat operations require platforms to maneuver on challenging, complex, rugged terrains and diverse environments. The recent technological advancements and development in aquatic robotics and mobile robotics have facilitated a more agile, robust, and efficient amphibious robots maneuvering in multiple environments and various terrain profiles. Amphibious robot
locomotion inspired by nature, such as amphibians, offers augmented flexibility, improved adaptability, and
higher mobility over terrestrial, aquatic, and aerial mediums. In this review, amphibious robots' locomotion
mechanism designed and developed previously are consolidated, systematically The review also analyzes
the literature on amphibious robot highlighting the limitations, open research areas, recent key development
in this research field. Further development and contributions to amphibious robot locomotion, actuation, and
control can be utilized to perform specific missions in sophisticated environments, where tasks are unsafe
or hardly feasible for the divers or traditional aquatic and terrestrial robots
Energetics and Passive Dynamics of Quadruped Robot Planar Running Gaits
Quadruped robots find application in military for load carrying over uneven terrain, humanitarian
de-mining, and search and rescue missions. The energy required for quadruped robot locomotion
needs to be supplied from on-board energy source which can be either electrical batteries or fuels
such as gasolene/diesel. The range and duration of missions very much depend on the amount
of energy carried, which is highly limited. Hence, energy efficiency is of paramount importance in
building quadruped robots. Study of energy efficiency in quadruped robots not only helps in efficient
design of quadruped robots, but also helps understand the biomechanics of quadrupedal animals.
This thesis focuses on the energy efficiency of planar running gaits and presents: (a) derivation of
cost of transport expressions for trot and bounding gaits, (b) advantages of articulated torso over
rigid torso for quadruped robot, (c) symmetry based control laws for passive dynamic bounding and
design for inherent stability, and (d) effect of asymmetry in zero-energy bounding gaits
Active shape discrimination with compliant bodies as reservoir computers
Compliant bodies with complex dynamics can be used both to simplify control problems and to lead to adaptive reflexive behavior when engaged with the environment in the sensorimotor loop. By revisiting an experiment introduced by Beer and replacing the continuous-time recurrent neural network therein with reservoir computing networks abstracted from compliant bodies, we demonstrate that adaptive behavior can be produced by an agent in which the body is the main computational locus. We show that bodies with complex dynamics are capable of integrating, storing, and processing information in meaningful and useful ways, and furthermore that with the addition of the simplest of nervous systems such bodies can generate behavior that could equally be described as reflexive or minimally cognitive
Fast Sensing and Adaptive Actuation for Robust Legged Locomotion
Robust legged locomotion in complex terrain demands fast perturbation detection and reaction. In animals, due to the neural transmission delays, the high-level control loop involving the brain is absent from mitigating the initial disturbance. Instead, the low-level compliant behavior embedded in mechanics and the mid-level controllers in the spinal cord are believed to provide quick response during fast locomotion. Still, it remains unclear how these low- and mid-level components facilitate robust locomotion.
This thesis aims to identify and characterize the underlining elements responsible for fast sensing and actuation. To test individual elements and their interplay, several robotic systems were implemented. The implementations include active and passive mechanisms as a combination of elasticities and dampers in multi-segment robot legs, central pattern generators inspired by intraspinal controllers, and a synthetic robotic version of an intraspinal sensor.
The first contribution establishes the notion of effective damping. Effective damping is defined as the total energy dissipation during one step, which allows quantifying how much ground perturbation is mitigated. Using this framework, the optimal damper is identified as viscous and tunable. This study paves the way for integrating effective dampers to legged designs for robust locomotion.
The second contribution introduces a novel series elastic actuation system. The proposed system tackles the issue of power transmission over multiple joints, while featuring intrinsic series elasticity. The design is tested on a hopper with two more elastic elements, demonstrating energy recuperation and enhanced dynamic performance.
The third contribution proposes a novel tunable damper and reveals its influence on legged hopping. A bio-inspired slack tendon mechanism is implemented in parallel with a spring. The tunable damping is rigorously quantified on a central-pattern-generator-driven hopping robot, which reveals the trade-off between locomotion robustness and efficiency.
The last contribution explores the intraspinal sensing hypothesis of birds. We speculate that the observed intraspinal structure functions as an accelerometer. This accelerometer could provide fast state feedback directly to the adjacent central pattern generator circuits, contributing to birds’ running robustness. A biophysical simulation framework is established, which provides new perspectives on the sensing mechanics of the system, including the influence of morphologies and material properties.
Giving an overview of the hierarchical control architecture, this thesis investigates the fast sensing and actuation mechanisms in several control layers, including the low-level mechanical response and the mid-level intraspinal controllers. The contributions of this work provide new insight into animal loco-motion robustness and lays the foundation for future legged robot design
Exploring the effects of robotic design on learning and neural control
The ongoing deep learning revolution has allowed computers to outclass humans
in various games and perceive features imperceptible to humans during
classification tasks. Current machine learning techniques have clearly
distinguished themselves in specialized tasks. However, we have yet to see
robots capable of performing multiple tasks at an expert level. Most work in
this field is focused on the development of more sophisticated learning
algorithms for a robot's controller given a largely static and presupposed
robotic design. By focusing on the development of robotic bodies, rather than
neural controllers, I have discovered that robots can be designed such that
they overcome many of the current pitfalls encountered by neural controllers in
multitask settings. Through this discovery, I also present novel metrics to
explicitly measure the learning ability of a robotic design and its resistance
to common problems such as catastrophic interference.
Traditionally, the physical robot design requires human engineers to plan
every aspect of the system, which is expensive and often relies on human
intuition. In contrast, within the field of evolutionary robotics, evolutionary
algorithms are used to automatically create optimized designs, however, such
designs are often still limited in their ability to perform in a multitask
setting. The metrics created and presented here give a novel path to automated
design that allow evolved robots to synergize with their controller to improve
the computational efficiency of their learning while overcoming catastrophic
interference.
Overall, this dissertation intimates the ability to automatically design
robots that are more general purpose than current robots and that can perform
various tasks while requiring less computation.Comment: arXiv admin note: text overlap with arXiv:2008.0639
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Moving softly : the role of morphological computation in the generation of intelligent behaviour
The central theme of this thesis is ‘morphological computation’, in particular as it pertains to the generation of purposeful and intelligent behaviour.
The first part of the thesis covers experiments in simulations, aimed at exploring a previously unasked question: can genuinely computational soft-body reservoirs play a central role in generating behaviour which has previously been described as ‘minimally cognitive’? We conclude in the affirmative, but also note that it remains unclear whether or how they can also exceed such abilities. The main part of this work has been presented at ALIFE 14 and ECAL 15, and published in the Artificial Life journal.
In part two, the focus moves to consideration of how morphological computation in soft bodies may collude with the action of nervous systems in the production of adaptive intelligent behaviour. The massive and hypnotic complexity of brains in present day species, and lasting traditions in the design and control of animals’ mechanical analogies (robots), still lead many to perceive bodies in the neuromuscular species as being in service to the goals of their controllers (brains), but in fact the two are of a piece, and work together to the same ends. We hypothesise that this would have been more apparent than now at a time closer to the evolutionary introduction of neuromuscular systems, and introduce our new software framework for the evolution of virtual neuromuscular swimmers, which may be used to study artificial parallels to those distant origins. We begin with a demonstration of the technical innovations which we required in order to simulate soft-bodied swimmers in two-dimensional particle-based fluids, and then proceed to a description of our new concept for mapping arbitrary neural networks to arbitrary body morphologies