495 research outputs found
Kinetic energy fluctuation-driven locomotor transitions on potential energy landscapes of beam obstacle traversal and self-righting
Despite contending with constraints imposed by the environment, morphology,
and physiology, animals move well by physically interactingwith the environment
to use and transition between modes such as running, climbing, and
self-righting. By contrast, robots struggle to do so in real world.
Understanding the principles of how locomotor transitions emerge from
constrained physical interaction is necessary for robots to move robustly using
similar strategies. Recent studies discovered that discoid cockroaches use and
transition between diverse locomotor modes to traverse beams and self-right on
ground. For both systems, animals probabilistically transitioned between modes
via multiple pathways, while its self-propulsion created kinetic energy
fluctuation. Here, we seek mechanistic explanations for these observations by
adopting a physics-based approach that integrates biological and robotic
studies.
We discovered that animal and robot locomotor transitions during beam
obstacle traversal and ground self-righting are barrier-crossing transitions on
potential energy landscapes. Whereas animals and robot traversed stiff beams by
rolling their body betweenbeam, they pushed across flimsy beams, suggesting a
concept of terradynamic favorability where modes with easier physical
interaction are more likely to occur. Robotic beam traversal revealed that,
system state either remains in a favorable mode or transitions to one when
energy fluctuation is comparable to the transition barrier. Robotic
self-righting transitions occurred similarly and revealed that changing system
parameters lowers barriers over which comparable fluctuation can induce
transitions. Thetransitionsof animalsin both systems mostly occurred similarly,
but sensory feedback may facilitate its beam traversal. Finally, we developed a
method to measure animal movement across large spatiotemporal scales in a
terrain treadmill.Comment: arXiv admin note: substantial text overlap with arXiv:2006.1271
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
Development of Motion Control Systems for Hydraulically Actuated Cranes with Hanging Loads
Automation has been used in industrial processes for several decades to increase efficiency and safety. Tasks that are either dull, dangerous, or dirty can often be performed by machines in a reliable manner. This may provide a reduced risk to human life, and will typically give a lower economic cost. Industrial robots are a prime example of this, and have seen extensive use in the automotive industry and manufacturing plants. While these machines have been employed in a wide variety of industries, heavy duty lifting and handling equipment such as hydraulic cranes have typically been manually operated. This provides an opportunity to investigate and develop control systems to push lifting equipment towards the same level of automation found in the aforementioned industries. The use of winches and hanging loads on cranes give a set of challenges not typically found on robots, which requires careful consideration of both the safety aspect and precision of the pendulum-like motion. Another difference from industrial robots is the type of actuation systems used. While robots use electric motors, the cranes discussed in this thesis use hydraulic cylinders. As such, the dynamics of the machines and the control system designmay differ significantly. In addition, hydraulic cranes may experience significant deflection when lifting heavy loads, arising from both structural flexibility and the compressibility of the hydraulic fluid.
The work presented in this thesis focuses on motion control of hydraulically actuated cranes. Motion control is an important topic when developing automation systems, as moving from one position to another is a common requirement for automated lifting operations. A novel path controller operating in actuator space is developed, which takes advantage of the load-independent flow control valves typically found on hydraulically actuated cranes. By operating in actuator space the motion of each cylinder is inherently minimized. To counteract the pendulum-like motion of the hanging payload, a novel anti-swing controller is developed and experimentally verified. The anti-swing controller is able to suppress the motion from the hanging load to increase safety and precision. To tackle the challenges associated with the flexibility of the crane, a deflection compensator is developed and experimentally verified. The deflection compensator is able to counteract both the static deflection due to gravity and dynamic de ection due to motion. Further, the topic of adaptive feedforward control of pressure compensated cylinders has been investigated.
A novel adaptive differential controller has been developed and experimentally verified, which adapts to system uncertainties in both directions of motion. Finally, the use of electro-hydrostatic actuators for motion control of cranes has been investigated using numerical time domain simulations. A novel concept is proposed and investigated using simulations.publishedVersio
Concurrent design and motion planning in robotics using differentiable optimal control
Robot design optimization (what the robot is) and motion planning (how the robot
moves) are two problems that are connected. Robots are limited by their design in
terms of what motions they can execute – for instance a robot with a heavy base has
less payload capacity compared to the same robot with a lighter base. On the other
hand, the motions that the robot executes guide which design is best for the task.
Concurrent design (co-design) is the process of performing robot design and motion
planning together. Although traditionally co-design has been viewed as an offline
process that can take hours or days, we view interactive co-design tools as the next
step as they enable quick prototyping and evaluation of designs across different tasks
and environments.
In this thesis we adopt a gradient-based approach to co-design. Our baseline
approach embeds the motion planning into bi-level optimization and uses gradient
information via finite differences from the lower motion planning level to optimize
the design in the upper level. Our approach uses the full rigid-body dynamics of the
robot and allows for arbitrary upper-level design constraints, which is key for finding
physically realizable designs. Our approach is also between 1.8 and 8.4 times faster on
a quadruped trotting and jumping co-design task as compared to the popular genetic
algorithm covariance matrix adaptation evolutionary strategy (CMA-ES). We further
demonstrate the speed of our approach by building an interactive co-design tool that
allows for optimization over uneven terrain with varying height.
Furthermore, we propose an algorithm to analytically take the derivative of nonlinear
optimal control problems via differential dynamic programming (DDP). Analytical
derivatives are a step towards addressing the scalability and accuracy issues of finite
differences. We further compared with a simultaneous approach for co-design that
optimizes both motion and design in one nonlinear program. On a co-design task for
the Kinova robotic arm we observed a 54-times improvement in computational speed.
We additionally carry out hardware validation experiments on the quadruped robot
Solo. We designed longer lower legs for the robot, which minimize the peak torque
used during trotting. Although we always observed an improvement in peak torque,
it was less than in simulation (7.609% versus 28.271%). We discuss some of the sim-toreal
issues including the structural stability of joints and slipping of feet that need to
be considered and how they can be addressed using our framework.
In the second part of this thesis we propose solutions to some open problems
in motion planning. Firstly, in our co-design approach we assumed fixed contact
locations and timings. Ideally we would like the motion planner to choose the contacts
instead. We solve a related, but simpler problem, which is the control of satellite
thrusters, which are similar to robot feet but do not have the constraint of having to be
in contact with the ground to exert force on the robot. We introduce a sparse, L1 cost
on control inputs (thrusters) and implement optimization via DDP-style solvers. We
use full rigid-body dynamics and achieve bang-bang control via optimization, which
is a difficult problem due to the discrete switching nature of the thrusters.
Lastly, we present a method for planning and control of a hybrid, wheel-legged
robot. This is a difficult problem, as the robot needs to always actively balance on
the wheel even when not driving or jumping forward. We propose the variablelength
wheeled inverted pendulum (VL-WIP) template model that captures only the
necessary dynamic interactions between wheels and base. We embedded this into
a model-predictive controller (MPC) and demonstrated highly dynamic behaviors,
including swinging-up and jumping over a gap.
Both of these motion planning problems expand the ability of our motion planning
tools to new domains, which is an integral part also of the co-design algorithms, as
co-design aims to optimize both design, and motion, together
Learning Motion Skills for a Humanoid Robot
This thesis investigates the learning of motion skills for humanoid robots. As groundwork, a humanoid robot with integrated fall management was developed as an experimental platform. Then, two different approaches for creating motion skills were investigated. First, one that is based on Cartesian quintic splines with optimized parameters.
Second, a reinforcement learning-based approach that utilizes the first approach as a reference motion to guide the learning. Both approaches were tested on the developed robot and on further simulated robots to show their generalization. A special focus was set on the locomotion skill, but a standing-up and kick skill are also discussed.
Diese Dissertation beschäftigt sich mit dem Lernen von Bewegungsfähigkeiten für humanoide Roboter. Als Grundlage wurde zunächst ein humanoider Roboter mit integriertem Fall Management entwickelt, welcher als Experimentalplatform dient. Dann wurden zwei verschiedene Ansätze für die Erstellung von Bewegungsfähigkeiten untersucht. Zu erst einer der kartesische quintische Splines mit optimierten Parametern nutzt.
Danach wurde ein Ansatz basierend auf bestärkendem Lernen untersucht, welcher den ersten Ansatz als Referenzbewegung benutzt. Beide Ansätze wurden sowohl auf der entwickelten Roboterplatform, als auch auf weiteren simulierten Robotern getestet um die Generalisierbarkeit zu zeigen. Ein besonderer Fokus wurde auf die Fähigkeit des Gehens gelegt, aber auch Aufsteh- und Schussfähigkeiten werden diskutiert
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