Dynamic Mobile Manipulation via Whole-Body Bilateral Teleoperation of a Wheeled Humanoid

Humanoid robots have the potential to help human workers by realizing physically demanding manipulation tasks such as moving large boxes within warehouses. We define such tasks as Dynamic Mobile Manipulation (DMM). This paper presents a framework for DMM via whole-body teleoperation, built upon three key contributions: Firstly, a teleoperation framework employing a Human Machine Interface (HMI) and a bi-wheeled humanoid, SATYRR, is proposed. Secondly, the study introduces a dynamic locomotion mapping, utilizing human-robot reduced order models, and a kinematic retargeting strategy for manipulation tasks. Additionally, the paper discusses the role of whole-body haptic feedback for wheeled humanoid control. Finally, the system's effectiveness and mappings for DMM are validated through locomanipulation experiments and heavy box pushing tasks. Here we show two forms of DMM: grasping a target moving at an average speed of 0.4 m/s, and pushing boxes weighing up to 105\% of the robot's weight. By simultaneously adjusting their pitch and using their arms, the pilot adjusts the robot pose to apply larger contact forces and move a heavy box at a constant velocity of 0.2 m/s

A survey on uninhabited underwater vehicles (UUV)

ASME Early Career Technical Conference, ASME ECTC, October 2-3, 2009, Tuscaloosa, Alabama, USAThis work presents the initiation of our underwater robotics research which will be focused on underwater vehicle-manipulator systems. Our aim is to build an underwater vehicle with a robotic manipulator which has a robust system and also can compensate itself under the influence of the hydrodynamic effects. In this paper, overview of the existing underwater vehicle systems, thruster designs, their dynamic models and control architectures are given. The purpose and results of the existing methods in underwater robotics are investigated

Shared control for navigation and balance of a dynamically stable robot.

by Law Kwok Ho Cedric.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 106-112).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Related work --- p.4Chapter 1.3 --- Thesis overview --- p.5Chapter 2 --- Single wheel robot: Gyrover --- p.9Chapter 2.1 --- Background --- p.9Chapter 2.2 --- Robot concept --- p.11Chapter 2.3 --- System description --- p.14Chapter 2.4 --- Flywheel characteristics --- p.16Chapter 2.5 --- Control patterns --- p.20Chapter 3 --- Learning Control --- p.22Chapter 3.1 --- Motivation --- p.22Chapter 3.2 --- Cascade Neural Network with Kalman filtering --- p.24Chapter 3.3 --- Learning architecture --- p.27Chapter 3.4 --- Input space --- p.29Chapter 3.5 --- Model evaluation --- p.30Chapter 3.6 --- Training procedures --- p.35Chapter 4 --- Control Architecture --- p.38Chapter 4.1 --- Behavior-based approach --- p.38Chapter 4.1.1 --- Concept and applications --- p.39Chapter 4.1.2 --- Levels of competence --- p.44Chapter 4.2 --- Behavior-based control of Gyrover: architecture --- p.45Chapter 4.3 --- Behavior-based control of Gyrover: case studies --- p.50Chapter 4.3.1 --- Vertical balancing --- p.51Chapter 4.3.2 --- Tiltup motion --- p.52Chapter 4.4 --- Discussions --- p.53Chapter 5 --- Implement ation of Learning Control --- p.57Chapter 5.1 --- Validation --- p.57Chapter 5.1.1 --- Vertical balancing --- p.58Chapter 5.1.2 --- Tilt-up motion --- p.62Chapter 5.1.3 --- Discussions --- p.62Chapter 5.2 --- Implementation --- p.65Chapter 5.2.1 --- Vertical balanced motion --- p.65Chapter 5.2.2 --- Tilt-up motion --- p.68Chapter 5.3 --- Combined motion --- p.70Chapter 5.4 --- Discussions --- p.72Chapter 6 --- Shared Control --- p.74Chapter 6.1 --- Concept --- p.74Chapter 6.2 --- Schemes --- p.78Chapter 6.2.1 --- Switch mode --- p.79Chapter 6.2.2 --- Distributed mode --- p.79Chapter 6.2.3 --- Combined mode --- p.80Chapter 6.3 --- Shared control of Gyrover --- p.81Chapter 6.4 --- How to share --- p.83Chapter 6.5 --- Experimental study --- p.88Chapter 6.5.1 --- Heading control --- p.89Chapter 6.5.2 --- Straight path --- p.90Chapter 6.5.3 --- Circular path --- p.91Chapter 6.5.4 --- Point-to-point navigation --- p.94Chapter 6.6 --- Discussions --- p.95Chapter 7 --- Conclusion --- p.103Chapter 7.1 --- Contributions --- p.103Chapter 7.2 --- Future work --- p.10

Feedback Control as a Framework for Understanding Tradeoffs in Biology

Control theory arose from a need to control synthetic systems. From regulating steam engines to tuning radios to devices capable of autonomous movement, it provided a formal mathematical basis for understanding the role of feedback in the stability (or change) of dynamical systems. It provides a framework for understanding any system with feedback regulation, including biological ones such as regulatory gene networks, cellular metabolic systems, sensorimotor dynamics of moving animals, and even ecological or evolutionary dynamics of organisms and populations. Here we focus on four case studies of the sensorimotor dynamics of animals, each of which involves the application of principles from control theory to probe stability and feedback in an organism's response to perturbations. We use examples from aquatic (electric fish station keeping and jamming avoidance), terrestrial (cockroach wall following) and aerial environments (flight control in moths) to highlight how one can use control theory to understand how feedback mechanisms interact with the physical dynamics of animals to determine their stability and response to sensory inputs and perturbations. Each case study is cast as a control problem with sensory input, neural processing, and motor dynamics, the output of which feeds back to the sensory inputs. Collectively, the interaction of these systems in a closed loop determines the behavior of the entire system.Comment: Submitted to Integr Comp Bio

Predictive Whole-Body Control of Humanoid Robot Locomotion

Humanoid robots are machines built with an anthropomorphic shape. Despite decades of research into the subject, it is still challenging to tackle the robot locomotion problem from an algorithmic point of view. For example, these machines cannot achieve a constant forward body movement without exploiting contacts with the environment. The reactive forces resulting from the contacts are subject to strong limitations, complicating the design of control laws. As a consequence, the generation of humanoid motions requires to exploit fully the mathematical model of the robot in contact with the environment or to resort to approximations of it. This thesis investigates predictive and optimal control techniques for tackling humanoid robot motion tasks. They generate control input values from the system model and objectives, often transposed as cost function to minimize. In particular, this thesis tackles several aspects of the humanoid robot locomotion problem in a crescendo of complexity. First, we consider the single step push recovery problem. Namely, we aim at maintaining the upright posture with a single step after a strong external disturbance. Second, we generate and stabilize walking motions. In addition, we adopt predictive techniques to perform more dynamic motions, like large step-ups. The above-mentioned applications make use of different simplifications or assumptions to facilitate the tractability of the corresponding motion tasks. Moreover, they consider first the foot placements and only afterward how to maintain balance. We attempt to remove all these simplifications. We model the robot in contact with the environment explicitly, comparing different methods. In addition, we are able to obtain whole-body walking trajectories automatically by only specifying the desired motion velocity and a moving reference on the ground. We exploit the contacts with the walking surface to achieve these objectives while maintaining the robot balanced. Experiments are performed on real and simulated humanoid robots, like the Atlas and the iCub humanoid robots

Exploiting inherent robustness and natural dynamics in the control of bipedal walking robots

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (p. 115-120).Walking is an easy task for most humans and animals. Two characteristics which make it easy are the inherent robustness (tolerance to variation) of the walking problem and the natural dynamics of the walking mechanism. In this thesis we show how understanding and exploiting these two characteristics can aid in the control of bipedal robots. Inherent robustness allows for the use of simple, low impedance controllers. Natural dynamics reduces the requirements of the controller. We present a series of simple physical models of bipedal walking. The insight gained from these models is used in the development of three planar (motion only in the sagittal plane) control algorithms. The first uses simple strategies to control the robot to walk. The second exploits the natural dynamics of a kneecap, compliant ankle, and passive swing-leg. The third achieves fast swing of the swing-leg in order to enable the robot to walk quickly (1.25m). These algorithms are implemented on Spring Flamingo, a planar bipedal walking robot, which was designed and built for this thesis. Using these algorithms, the robot can stand and balance, start and stop walking, walk at a range of speeds, and traverse slopes and rolling terrain. Three-dimensional walking on flat ground is implemented and tested in simulation. The dynamics of the sagittal plane are sufficiently decoupled from the dynamics of the frontal and transverse planes such that control.-of each can be treated separately. We achieve three-dimensional walking by adding lateral balance to the planar algorithms. Tests of this approach on a real three-dimensional robot will lead to a more complete understanding of the control of bipedal walking in robots and humans.by Jerry E. Pratt.Ph.D