Feasibility Investigation of Obstacle-Avoiding Sensors Unit without Image Processing

Feasibility of a simple method to detect step height, slope angle, and trench width using four infrared-light-source PSD range sensors is examined, and the reproducibility and accuracy of characteristic parameter detection are also examined. Detection error of upward slope angle is within 2.5 degrees, while it is shown that the detection error of downward slope angle exceeding 20 degrees is very large. In order to reduce such errors, a method to improve range-voltage performance of a range sensor is proposed, and its availability is demonstrated. We also show that increase in trial frequency is a better way, although so as not to increase the detection delay. Step height is identified with an error of ±1.5 mm. It is shown that trench width cannot be reliably measured at this time. It is suggested that an additional method is needed if we have to advance the field of obstacle detection

Nonprehensile Manipulation of Deformable Objects: Achievements and Perspectives from the RobDyMan Project

International audienceThe goal of this work is to disseminate the results achieved so far within the RODYMAN project related to planning and control strategies for robotic nonprehensile manipulation. The project aims at advancing the state of the art of nonprehensile dynamic manipulation of rigid and deformable objects to future enhance the possibility of employing robots in anthropic environments. The final demonstrator of the RODYMAN project will be an autonomous pizza maker. This article is a milestone to highlight the lessons learned so far and pave the way towards future research directions and critical discussions

Data-Driven Methods to Build Robust Legged Robots

For robots to ever achieve signicant autonomy, they need to be able to mitigate performance loss due to uncertainty, typically from a novel environment or morphological variation of their bodies. Legged robots, with their complex dynamics, are particularly challenging to control with principled theory. Hybrid events, uncertainty, and high dimension are all confounding factors for direct analysis of models. On the other hand, direct data-driven methods have proven to be equally dicult to employ. The high dimension and mechanical complexity of legged robots have proven challenging for hardware-in-the-loop strategies to exploit without signicant eort by human operators. We advocate that we can exploit both perspectives by capitalizing on qualitative features of mathematical models applicable to legged robots, and use that knowledge to strongly inform data-driven methods. We show that the existence of these simple structures can greatly facilitate robust design of legged robots from a data-driven perspective. We begin by demonstrating that the factorial complexity of hybrid models can be elegantly resolved with computationally tractable algorithms, and establish that a novel form of distributed control is predicted. We then continue by demonstrating that a relaxed version of the famous templates and anchors hypothesis can be used to encode performance objectives in a highly redundant way, allowing robots that have suffered damage to autonomously compensate. We conclude with a deadbeat stabilization result that is quite general, and can be determined without equations of motion.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155053/1/gcouncil_1.pd

Motion planning and control methods for nonprehensile manipulation and multi-contact locomotion tasks

Many existing works in the robotic literature deal with the problem of nonprehensile dynamic manipulation. However, a unified control framework does not exist so far. One of the ambitious goals of this Thesis is to contribute to identify planning and control frameworks solving classes of nonprehensile dynamic manipulation tasks, dealing with the non linearity of their dynamic models and, consequently, with the inherited design complexity. Besides, while passing through a number of connections between dynamic nonprehensile manipulation and legged locomotion, the Thesis presents novel methods for generating walking motions in multi-contact situations

Spatial and Timing Regulation of Upper-Limb Movements in Rhythmic Tasks

Rhythmic movement is vital to humans and a foundation of such activities as locomotion, handwriting, and repetitive tool use. The spatiotemporal regularity characterizing such movements reflects a level of automaticity and coordination that is believed to emerge from mutually inhibitory or other pattern generating neural networks in the central nervous system. Although many studies have provided descriptions of this regularity and have illuminated the types of sensory information that influence rhythmic behavior, an understanding of how the brain uses sensory feedback to regulate rhythmic behavior on a cycle-by-cycle basis has been elusive. This thesis utilizes the model task of paddle juggling, or vertical ball bouncing, to address how three types of feedback---visual, auditory, and haptic---contribute to spatial and temporal regulation of rhythmic upper-limb movements. We use a multi-level approach in accordance with the well-known dictum of Marr and Poggio. The crux of this thesis describes a method and suite of experiments to understand how the brain uses visual, audio, and haptic feedback to regulate spatial or timing regularity, and formulate acycle-by-cycle description of this control: to wit, the nature and algorithms of sensory-feedback guided regulation. Part I motivates our interest in this problem, by discussing the biological ``hardware'' that the nervous system putatively employs in these movements, and reviewing insights from previous studies of paddle juggling that suggest how the ``hardware'' may manifest itself in these behaviors. The central experimental approach of this thesis is to train participants to perform the paddle juggling task with spatiotemporal regularity (in other words, to achieve limit-cycle behavior), and then interrogate how the brain applies regulates closed-loop performance by perturbing task feedback. In Part II, we review the development of a novel hard-real-time virtual-reality juggling simulator that enabled precise spatial and temporal feedback perturbations. We then outline the central experimental approach, in which we perturb spatial feedback of the ball at apex phase (vision), and timing feedback of collision- (audio and haptic) and apex-phase events to understand spatial and timing regulation. Part III describes two experiments that yield the main research findings of this thesis. In Experiment 1, we use a sinusoidal-perturbation-based system identification approach to determine that spatial and timing feedback are used in two dissociable and complementary control processes: spatial error correction and temporal synchronization. In Experiment 2, a combination of sinusoidal and step perturbations is used to establish that these complementary processes obey different dynamics. Namely, spatial correction is a proportional-integral process based on a one-step memory of feedback, while temporal synchronization is a proportional process that is dependent only on the most recent feedback. We close in Part IV with a discussion of how insights and approaches from this thesis can lead to improved rehabilitation approaches and understanding of the physiological basis of rhythmic movement regulation

Modeling, analysis and control of robot-object nonsmooth underactuated Lagrangian systems: A tutorial overview and perspectives

International audienceSo-called robot-object Lagrangian systems consist of a class of nonsmooth underactuated complementarity Lagrangian systems, with a specific structure: an "object" and a "robot". Only the robot is actuated. The object dynamics can thus be controlled only through the action of the contact Lagrange multipliers, which represent the interaction forces between the robot and the object. Juggling, walking, running, hopping machines, robotic systems that manipulate objects, tapping, pushing systems, kinematic chains with joint clearance, crawling, climbing robots, some cable-driven manipulators, and some circuits with set-valued nonsmooth components, belong this class. This article aims at presenting their main features, then many application examples which belong to the robot-object class, then reviewing the main tools and control strategies which have been proposed in the Automatic Control and in the Robotics literature. Some comments and open issues conclude the article