Norman Hendrich

Silicon compilation and rapid prototyping of microprogrammed VLSI-Circuits with MIMOLA and SOLO 1400

Abstract We describe the MIM2SOLO Silicon Compiler which integrates the MIMOLA high-level synthesis system and the SOLO 1400 standard-cell IC design system. Key features of the system are synthesis from an algorithmic description, full simulation support with automatic test pattern generation, and support of hybrid (analogue/digital) designs. We present a CISC processor designed both traditionally and using MIM2SOLO and compare design efforts and layout. As an example of hybrid designs, we discuss a digital filter with on-chip analogue-digital and digital-analogue converters.

Multi sensor fusion of camera and 3D laser range finder for object recognition

This paper proposes multi sensor fusion based on an effective calibration method for a perception system designed for mobile robots and intended for later object recognition. The perception system consists of a camera and a three-dimensional laser range finder. The three-dimensional laser range finder is based on a two-dimensional laser scanner and a pan-tilt unit as a moving platform. The calibration permits the coalescence of the two most important sensors for three-dimensional environment perception, namely a laser scanner and a camera. Both sensors permit multi sensor fusion consisting of color and depth information. The calibration process based upon a specific calibration pattern is used to define the extrinsic parameters and calculate the transformation between a laser range finder and a camera. The found transformation assigns an exact position and the color information to each point of the surroundings. As a result, the advantages of both sensors can be combined. The resulting structure consists of colored unorganized point clouds. The achieved results can be visualized with OpenGL and used for surface reconstruction. This way, typical robotic tasks like object recognition, grasp calculation or handling of objects can be realized. The results of our experiments are presented in this paper.

Precision grasp synergies for dexterous robotic hands

In this paper we present a method for the generation of hand postural synergies for different precision grasp types to be used in dextrous robot hands. Our method records the robot hand motions while teleoperated by human subjects via a dataglove, doing different grasp types on a series of objects. This exploits the fact that humans automatically compensate for calibration errors on the glove to robot mapping. The method is applied to the Shadow Robot Hand and to the iCub Hand. Despite the significantly different kinematics and different number and mechanism of actuators, the analysis results in useful postural synergies for both hands. The effective number of degrees-of-freedom to reproduce the recorded variance using the synergies is shown to be 2..6 for the different grasps, corresponding to a massive reduction of grasp-search complexity. Therefore, the existing actuators are enough to drive the hands with realistic human-like postures and in-hand movements. While previous work on synergies mostly concentrated on static grasping and power-grasps, our work confirms that human precision grasps and manipulation motions also lie on low-dimensional spaces.

Multi-sensor based segmentation of human manipulation tasks

In this paper we present an overview of a multi-sensor setup designed to record and analyse human in-hand manipulation — tasks consisting of several phases of finger motions following the initial grasp. During the experiments all of the hand, finger, and object positions are recorded, as are the contact forces applied to the manipulated objects. The use of instrumented sensing objects complements the data. The goal is to understand and extract a basic set of finger and hand movement patterns, which can then be combined to perform a complete manipulation task, and which can be transferred to control robotic hands. The segmentation of whole manipulation traces into several phases corresponding to individual basic patterns is the first step towards this goal. Initial analysis and segmentation of two typical manipulation tasks are presented, showing the advantages of the multi-modal analysis.

An efficient hybridization of Genetic Algorithms and Particle Swarm Optimization for inverse kinematics

This paper presents a novel biologically-inspired approach to solving the inverse kinematics problem efficiently on arbitrary joint chains. It provides high accuracy, convincing success rates and is capable of finding suitable solutions for full pose objectives in real-time while incorporating joint constraints. The algorithm tackles the problem by evolutionary optimization and merges the benefits of genetic algorithms with those of swarm intelligence which results in a hybridization that is inspired by individual social behaviour. A multi-objective fitness function is designed which follows the principle of natural evolution within continually changing environments. A further simultaneous exploitation of local extrema then allows obtaining more accurate solutions where dead-end paths can be detected by a simple heuristic. Experimental results show that the presented solution performs significantly more robustly and adaptively than traditional or various related methods and might also be applied to other problems that can be solved by optimization techniques.

Architecture and Software Design for a Service Robot in an Elderly-Care Scenario

ABSTRACT Systems for ambient assisted living (AAL) that integrate service robots with sensor networks and user monitoring can help elderly people with their daily activities, allowing them to stay in their homes and live active lives for as long as possible. In this paper, we outline the AAL system currently developed in the European project Robot-Era, and describe the engineering aspects and the service-oriented software architecture of the domestic robot, a service robot with advanced manipulation capabilities. Based on the robot operating system (ROS) middleware, our software integrates a large set of advanced algorithms for navigation, perception, and manipulation. In tests with real end users, the performance and acceptability of the platform are evaluated.

Tactile sensor value preprocessing pipeline

Tactile sensing plays an important role in robot hand manipulation. This papers presents an analysis of the raw values produced by a biomimetic tactile sensor and the proposed solution organized in a pipeline processing structure to overcome the signals noncompliances and issues for further processing. Even if this paper focuses on the data generated by the SynTouch BioTac sensor, the results and implemented modules can be applied to a wide range of tactile sensors.

Hybrid physics simulation of multi-fingered hands for dexterous in-hand manipulation

Dextrous object manipulation with multi-fingered robot hands remains one of the key challenges of service robotics. So far, most theoretical approaches and simulators have concentrated on the search for and evaluation of static stable grasps, but with neither a model of the full hand-arm system nor the system dynamics. GraspIt! is probably the best-known simulator of this kind. In this work we present a simulator that uses the JBullet physics engine to realistically model grasps with multi-fingered hands. It supports manipulation tasks based on a complete arm and hand system, with full calculation of hand and object dynamics. A hybrid dynamics and kinematics approach avoids the oscillations introduced by the different size scale of the arm and hand, so that force-closure grasps are possible in addition to form-closure grasps. The software includes detailed models of our 24-DOF Shadow Dextrous hand and the 6-DOF Mitsubishi PA-10 robot arm. A real-time interface allows us to prepare or to replay and analyze grasp experiments performed on our real robots.

A scalable architecture for binary couplings attractor neural networks

This paper presents a digital architecture with on-chip learning for Hopfield attractor neural networks with binary weights. A new learning rule for the binary weights network is proposed that allows pattern storage up to capacity /spl alpha/=0.4 and incurs very low hardware overhead. Due to the use of binary couplings the network has minimal storage requirements. A flexible communication structure allows cascading of multiple chips in order to build fully connected, block connected, or feed-forward networks. System performance and communication bandwidth scale linear with the number of chips. A prototype chip has been fabricated and is fully functional. A pattern recognition application shows the performance of the binary couplings network.

PEIS, MIRA, and ROS: Three frameworks, one service robot — A tale of integration

Designing and building a mobile service robot is a complex task, involving many design decisions regarding hardware components and software architecture. A robot design always constitutes a delicate compromise between capabilities, performance, safety, reliability and costs. Only the availability of high-quality robot middleware frameworks like ROS has allowed several university groups to succeed in this task. In this paper, we describe the hardware and software design of the Domestic Robot of project Robot-Era, a medium cost mobile manipulation platform designed to help elderly people with their daily routines in an assisted living environment. Assembled from standard hardware components, the robot is characterized by its services-based software architecture, which integrates three different software frameworks, exploiting the strengths of each. We highlight the development process as well as some problems that occurred during the design, implementation and testing phases.