Vision-based interface applied to assistive robots

This paper presents two vision-based interfaces for disabled people to command a mobile robot for personal assistance. The developed interfaces can be subdivided according to the algorithm of image processing implemented for the detection and tracking of two different body regions. The first interface detects and tracks movements of the user's head, and these movements are transformed into linear and angular velocities in order to command a mobile robot. The second interface detects and tracks movements of the user's hand, and these movements are similarly transformed. In addition, this paper also presents the control laws for the robot. The experimental results demonstrate good performance and balance between complexity and feasibility for real-time applications.Fil: Pérez Berenguer, María Elisa. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: López Celani, Natalia Martina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Nasisi, Oscar Herminio. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Mut, Vicente Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentin

Grasping unknown objects in clutter by superquadric representation

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, a quick and efficient method is presented for grasping unknown objects in clutter. The grasping method relies on real-time superquadric (SQ) representation of partial view objects and incomplete object modelling, well suited for unknown symmetric objects in cluttered scenarios which is followed by optimized antipodal grasping. The incomplete object models are processed through a mirroring algorithm that assumes symmetry to first create an approximate complete model and then fit for SQ representation. The grasping algorithm is designed for maximum force balance and stability, taking advantage of the quick retrieval of dimension and surface curvature information from the SQ parameters. The pose of the SQs with respect to the direction of gravity is calculated and used together with the parameters of the SQs and specification of the gripper, to select the best direction of approach and contact points. The SQ fitting method has been tested on custom datasets containing objects in isolation as well as in clutter. The grasping algorithm is evaluated on a PR2 robot and real time results are presented. Initial results indicate that though the method is based on simplistic shape information, it outperforms other learning based grasping algorithms that also work in clutter in terms of time-efficiency and accuracy.Peer ReviewedPostprint (author's final draft

Kinematic calibration of Orthoglide-type mechanisms from observation of parallel leg motions

The paper proposes a new calibration method for parallel manipulators that allows efficient identification of the joint offsets using observations of the manipulator leg parallelism with respect to the base surface. The method employs a simple and low-cost measuring system, which evaluates deviation of the leg location during motions that are assumed to preserve the leg parallelism for the nominal values of the manipulator parameters. Using the measured deviations, the developed algorithm estimates the joint offsets that are treated as the most essential parameters to be identified. The validity of the proposed calibration method and efficiency of the developed numerical algorithms are confirmed by experimental results. The sensitivity of the measurement methods and the calibration accuracy are also studied

FLAT2D: Fast localization from approximate transformation into 2D

Many autonomous vehicles require precise localization into a prior map in order to support planning and to leverage semantic information within those maps (e.g. that the right lane is a turn-only lane.) A popular approach in automotive systems is to use infrared intensity maps of the ground surface to localize, making them susceptible to failures when the surface is obscured by snow or when the road is repainted. An emerging alternative is to localize based on the 3D structure around the vehicle; these methods are robust to these types of changes, but the maps are costly both in terms of storage and the computational cost of matching. In this paper, we propose a fast method for localizing based on 3D structure around the vehicle using a 2D representation. This representation retains many of the advantages of "full" matching in 3D, but comes with dramatically lower space and computational requirements. We also introduce a variation of Graph-SLAM tailored to support localization, allowing us to make use of graph-based error-recovery techniques in our localization estimate. Finally, we present real-world localization results for both an indoor mobile robotic platform and an autonomous golf cart, demonstrating that autonomous vehicles do not need full 3D matching to accurately localize in the environment

Sampling-Based Methods for Factored Task and Motion Planning

This paper presents a general-purpose formulation of a large class of discrete-time planning problems, with hybrid state and control-spaces, as factored transition systems. Factoring allows state transitions to be described as the intersection of several constraints each affecting a subset of the state and control variables. Robotic manipulation problems with many movable objects involve constraints that only affect several variables at a time and therefore exhibit large amounts of factoring. We develop a theoretical framework for solving factored transition systems with sampling-based algorithms. The framework characterizes conditions on the submanifold in which solutions lie, leading to a characterization of robust feasibility that incorporates dimensionality-reducing constraints. It then connects those conditions to corresponding conditional samplers that can be composed to produce values on this submanifold. We present two domain-independent, probabilistically complete planning algorithms that take, as input, a set of conditional samplers. We demonstrate the empirical efficiency of these algorithms on a set of challenging task and motion planning problems involving picking, placing, and pushing