Wearable Cleaning Robot

Clothing stains and spills often go undetected by the wearer of the clothes. A clothing spill usually prompts an urgent trip to swap clothing, which is an inconvenience. This disclosure describes a portable miniature cleaning robot that can detect spills or stains on clothing using computer vision, dispense cleaning fluid, and dry the clothing to remove the stains even as the user continues wearing the clothes. The robot, which is detachable and wearable, can be attached to an article of clothing whenever needed. The robot moves up and down the clothing and detects stains based on discrepancies in color or patterns. The robot can be filled with cleaning fluid that can be dispensed on stained or covered areas to remove the stain

Control strategies for cleaning robots in domestic applications: A comprehensive review:

Service robots are built and developed for various applications to support humans as companion, caretaker, or domestic support. As the number of elderly people grows, service robots will be in increasing demand. Particularly, one of the main tasks performed by elderly people, and others, is the complex task of cleaning. Therefore, cleaning tasks, such as sweeping floors, washing dishes, and wiping windows, have been developed for the domestic environment using service robots or robot manipulators with several control approaches. This article is primarily focused on control methodology used for cleaning tasks. Specifically, this work mainly discusses classical control and learning-based controlled methods. The classical control approaches, which consist of position control, force control, and impedance control , are commonly used for cleaning purposes in a highly controlled environment. However, classical control methods cannot be generalized for cluttered environment so that learning-based control methods could be an alternative solution. Learning-based control methods for cleaning tasks can encompass three approaches: learning from demonstration (LfD), supervised learning (SL), and reinforcement learning (RL). These control approaches have their own capabilities to generalize the cleaning tasks in the new environment. For example, LfD, which many research groups have used for cleaning tasks, can generate complex cleaning trajectories based on human demonstration. Also, SL can support the prediction of dirt areas and cleaning motion using large number of data set. Finally, RL can learn cleaning actions and interact with the new environment by the robot itself. In this context, this article aims to provide a general overview of robotic cleaning tasks based on different types of control methods using manipulator. It also suggest a description of the future directions of cleaning tasks based on the evaluation of the control approaches

Geometry-Aware Coverage Path Planning for Depowdering on Complex 3D Surfaces

This paper presents a new approach to obtaining nearly complete coverage paths (CP) with low overlapping on 3D general surfaces using mesh models. The CP is obtained by segmenting the mesh model into a given number of clusters using constrained centroidal Voronoi tessellation (CCVT) and finding the shortest path from cluster centroids using the geodesic metric efficiently. We introduce a new cost function to harmoniously achieve uniform areas of the obtained clusters and a restriction on the variation of triangle normals during the construction of CCVTs. Here, we utilize the planned VPs as cleaning configurations to perform residual powder removal in additive manufacturing using manipulator robots. The self-occlusion of VPs and ensuring collision-free robot configurations are addressed by integrating a proposed optimization-based strategy to find a set of candidate rays for each VP into the motion planning phase. CP planning benchmarks and physical experiments are conducted to demonstrate the effectiveness of the proposed approach. We show that our approach can compute the CPs and VPs of various mesh models with a massive number of triangles within a reasonable time.Comment: 8 pages, 8 figure

From Human Physical Interaction To Online Motion Adaptation Using Parameterized Dynamical Systems

In this work, we present an adaptive motion planning approach for impedance-controlled robots to modify their tasks based on human physical interactions. We use a class of parameterized time-independent dynamical systems for motion generation where the modulation of such parameters allows for motion flexibility. To adapt to human interactions, we update the parameter of our dynamical system in order to reduce the tracking error (i.e., between the desired trajectory generated by the dynamical system and the real trajectory influenced by the human interaction). We provide analytical analysis and several simulations of our method. Finally, we investigate our approach through real world experiments with 7-DOF KUKA LWR 4+ robot performing tasks such as polishing and pick-and-place

PLANNING FOR AUTOMATED OPTICAL MICROMANIPULATION OF BIOLOGICAL CELLS

Optical tweezers (OT) can be viewed as a robot that uses a highly focused laser beam for precise manipulation of biological objects and dielectric beads at micro-scale. Using holographic optical tweezers (HOT) multiple optical traps can be created to allow several operations in parallel. Moreover, due to the non-contact nature of manipulation OT can be potentially integrated with other manipulation techniques (e.g. microfluidics, acoustics, magnetics etc.) to ensure its high throughput. However, biological manipulation using OT suffers from two serious drawbacks: (1) slow manipulation due to manual operation and (2) severe effects on cell viability due to direct exposure of laser. This dissertation explores the problem of autonomous OT based cell manipulation in the light of addressing the two aforementioned limitations. Microfluidic devices are well suited for the study of biological objects because of their high throughput. Integrating microfluidics with OT provides precise position control as well as high throughput. An automated, physics-aware, planning approach is developed for fast transport of cells in OT assisted microfluidic chambers. The heuristic based planner employs a specific cost function for searching over a novel state-action space representation. The effectiveness of the planning algorithm is demonstrated using both simulation and physical experiments in microfluidic-optical tweezers hybrid manipulation setup. An indirect manipulation approach is developed for preventing cells from high intensity laser. Optically trapped inert microspheres are used for manipulating cells indirectly either by gripping or pushing. A novel planning and control approach is devised to automate the indirect manipulation of cells. The planning algorithm takes the motion constraints of the gripper or pushing formation into account to minimize the manipulation time. Two different types of cells (Saccharomyces cerevisiae and Dictyostelium discoideum) are manipulated to demonstrate the effectiveness of the indirect manipulation approach

The Future of Humanoid Robots

This book provides state of the art scientific and engineering research findings and developments in the field of humanoid robotics and its applications. It is expected that humanoids will change the way we interact with machines, and will have the ability to blend perfectly into an environment already designed for humans. The book contains chapters that aim to discover the future abilities of humanoid robots by presenting a variety of integrated research in various scientific and engineering fields, such as locomotion, perception, adaptive behavior, human-robot interaction, neuroscience and machine learning. The book is designed to be accessible and practical, with an emphasis on useful information to those working in the fields of robotics, cognitive science, artificial intelligence, computational methods and other fields of science directly or indirectly related to the development and usage of future humanoid robots. The editor of the book has extensive R&D experience, patents, and publications in the area of humanoid robotics, and his experience is reflected in editing the content of the book

A Dynamical System Based Approach for Controlling Robotic Manipulators During Non-contact/Contact Transitions

Many daily life tasks require precise control when making contact with surfaces. Ensuring a smooth transition from free motion to contact is crucial as incurring a large impact force may lead to unstable contact with the robot bouncing on the surface, i.e. chattering. Stabilizing the forces at contact is not possible as the impact lasts less than a millisecond, leaving no time for the robot to react to the impact force. We present a strategy in which the robot adapts its dynamic before entering into contact. The speed is modulated so as to align with the surface. We leverage the properties of autonomous dynamical systems for immediate re-planning and handling unforeseen perturbations and exploit local modulations of the dynamics to control for the smooth transitions at contact. We show theoretically and empirically that by using the modulation framework, the robot can (I) stably touch the contact surface, even when the surface’s location is uncertain, (II) at a desired location, and finally (III) leave the surface or stop on the surface at a desired point