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Mobile manipulators are intended to be deployed in domestic and industrial environments where they will carry out tasks that require physical interaction with the surrounding world, for example, picking or handing over fragile objects. In-hand slippage, i.e. a grasped object moving within the robot’s grasp, is inherent to many of these tasks and thus, a robot’s ability to detect a slippage is vital for executing a manipulation task successfully. In this paper, we develop a slip detection approach which is based on the robot’s tactile sensors, a force/torque sensor and a combination thereof. The evaluation of our approach, carried out on the Care-O-bot 3 platform, highly suggests that the actions and motions performed by the robot during grasping should be taken into account during slip detection for improved performance. Based on this insight, we propose an in-hand slip detection architecture that is able to adapt to the current robot’s actions at run time.
In this article we describe the architecture, algorithms and real-world benchmarks performed by Johnny Jackanapes, an autonomous service robot for domestic environments. Johnny serves as a research and development platform to explore, develop and integrate capabilities required for real-world domestic service applications. We present a control architecture which allows to cope with various and changing domestic service robot tasks. A software architecture supporting the rapid integration of functionality into a complete system is as well presented. Further, we describe novel and robust algorithms centered around multi-modal human robot interaction, semantic scene understanding and SLAM. Evaluation of the complete system has been performed during the last years in the RoboCup@Home competition where Johnnys outstanding performance led to successful participation. The results and lessons learned of these benchmarks are explained in more detail.
The goal of this work is to develop an integration framework for a robotic software system which enables robotic learning by experimentation within a distributed and heterogeneous setting. To meet this challenge, the authors specified, defined, developed, implemented and tested a component-based architecture called XPERSIF. The architecture comprises loosely-coupled, autonomous components that offer services through their well-defined interfaces and form a service-oriented architecture. The Ice middleware is used in the communication layer. Additionally, the successful integration of the XPERSim simulator into the system has enabled simultaneous quasi-realtime observation of the simulation by numerous, distributed users.
Swedish wheeled mobile robots have remarkable mobility properties allowing them to rotate and translate at the same time. Being holonomic systems, their kinematics model results in the possibility of designing separate and independent position and heading trajectory tracking control laws. Nevertheless, if these control laws should be implemented in the presence of unaccounted actuator saturation, the resulting saturated linear and angular velocity commands could interfere with each other thus dramatically affecting the overall expected performance. Based on Lyapunov’s direct method, a position and heading trajectory tracking control law for Swedish wheeled robots is developed. It explicitly accounts for actuator saturation by using ideas from a prioritized task based control framework.
RoboCup projects can face a lack of progress and continuity. The teams change continuously and knowledge gets lost. The approach used in previous years is no longer valid due to rule changes and specialists leaving the team leave black boxes that no-one understands. This article presents the application of a recent software development technique called eXtreme Programming to the realm of RoboCup. Many common problems typical for teams of students seem to be solvable with this technique. It also gradually spreads out in professional software production companies. Students mastering it are of high use for their further career after having left the university. The strategy is being tested on a real RoboCup Mid-Size and an Aibo league project and produces very promising results. The approach makes it possible to modularize scientific knowledge into software that can be re-used. Both the scientist/expert, who has the knowledge, and the software development team benefit from this approach without much overhead on the project.
The development of robot control programs is a complex task. Many robots are different in their electrical and mechanical structure which is also reflected in the software. Specific robot software environments support the program development, but are mainly text-based and usually applied by experts in the field with profound knowledge of the target robot. This paper presents a graphical programming environment which aims to ease the development of robot control programs. In contrast to existing graphical robot programming environments, our approach focuses on the composition of parallel action sequences. The developed environment allows to schedule independent robot actions on parallel execution lines and provides mechanism to avoid side-effects of parallel actions. The developed environment is platform-independent and based on the model-driven paradigm. The feasibility of our approach is shown by the application of the sequencer to a simulated service robot and a robot for educational purpose.
Finding good echo state networks to control an underwater robot using evolutionary computations
(2004)
Robots, which are able to carry out their tasks robustly in real world environments, are not only desirable but necessary if we want them to be more welcome for a wider audience. But very often they may fail to execute their actions successfully because of insufficient information about behaviour of objects used in the actions.
We describe the development of an architecture for the DESIRE technology demonstrator based on principles of classical component based software engineering. The architecture is directly derived from the project requirements and resides on the concept of an Autonomous Component utilizing a smart feedback value called WishLists. This return type is able to provide expert advice about the reasons of occurring failures and give hints for possible recovery strategies. This is of key importance to advance towards robustness. The integration of an AI task planner allows the realization of higher flexibility, dependability and capability during task execution and may resolve conflicts between occurring WishLists. Furthermore the necessity of a central system-state model (Eigenmodel), which represents the current state and configuration of the whole system at runtime, is explained and illustrated. We conclude with some lessons learned.
This paper presents a robust object tracking method using a sparse shape-based object model. Our approach consists of three ingredients namely shapes, a motion model and a sparse (non-binary) subsampling of colours in background and foreground parts based on the shape assumption. The tracking itself is inspired by the idea of having a short-term and a long-term memory. A lost object is ”missed” by the long-term memory when it is no longer recognized by the short-term memory. Moreover, the long-term memory allows to re-detect vanished objects and using their new position as a new initial position for object tracking. The short-term memory is implemented with a new Monte Carlo variant which provides a heuristic to cope with the loss-of-diversity problem. It enables simultaneous tracking of multiple (visually) identical objects. The long-term memory is implemented with a Bayesian Multiple Hypothesis filter. We demonstrate the robustness of our approach with respect to object occlusions and non-Gaussian/non-linear movements of the tracked object. We also show that tracking can be significantly improved by using compensating ego-motion. Our approach is very scalable since one can tune the parameters for a trade-off between precision and computational time.
With regard to performance well established SW-only design methodologies proceed by making the initial specification run first, then by enhancing its functionality and finally by optimizing it. When designing Embedded Systems (EbS) this approach is not viable since decisive design decisions like e.g. the estimation of required processing power or the identification of those parts of the specification which need to be delegated to dedicated HW depend on the fastness and fairness of the initial specification. We here propose a sequence of optimization steps embedded into the design flow, which enables a structured way to accelerate a given working EbS specification at different layers. This sequence of accelerations comprises algorithm selection, algorithm transformation, data transformation, implementation optimization and finally HW acceleration. It is analyzed how all acceleration steps are influenced by the specific attributes of the underlying EbS. The overall acceleration procedure is explained and quantified at hand of a real-life industrial example.
Cognitive robotics aims at understanding biological processes, though it has also the potential to improve future robotics systems. Here we show how a biologically inspired model of motor control with neural fields can be augmented with additional components such that it is able to solve a basic robotics task, that of obstacle avoidance. While obstacle avoidance is a well researched area, the focus here is on the extensibility of a biologically inspired framework. This work demonstrates how easily the biological inspired system can be used to adapt to new tasks. This flexibility is thought to be a major hallmark of biological agents.
The ability to track moving people is a key aspect of autonomous robot systems in real-world environments. Whilst for many tasks knowing the approximate positions of people may be sufficient, the ability to identify unique people is needed to accurately count people in the real world. To accomplish the people counting task, a robust system for people detection, tracking and identification is needed.
Robot deployment in realistic environments is challenging despite the fact that robots can be quite skilled at a large number of isolated tasks. One reason for this is that robots are rarely equipped with powerful introspection capabilities, which means that they cannot always deal with failures in an acceptable manner; in addition, manual diagnosis is often a tedious task that requires technicians to have a considerable set of robotics skills. In this paper, we discuss our ongoing efforts to address some of these problems. In particular, we (i) present our early efforts at developing a robotic black box and consider some factors that complicate its design, (ii) explain our component and system monitoring concept, and (iii) describe the necessity for remote monitoring and experimentation as well as our initial attempts at performing those. Our preliminary work opens a range of promising directions for making robots more usable and reliable in practice.
The increasing complexity of tasks that are required to be executed by robots demands higher reliability of robotic platforms. For this, it is crucial for robot developers to consider fault diagnosis. In this study, a general non-intrusive fault diagnosis system for robotic platforms is proposed. A mini-PC is non-intrusively attached to a robot that is used to detect and diagnose faults. The health data and diagnosis produced by the mini-PC is then standardized and transmitted to a remote-PC. A storage device is also attached to the mini-PC for data logging of health data in case of loss of communication with the remote-PC. In this study, a hybrid fault diagnosis method is compared to consistency-based diagnosis (CBD), and CBD is selected to be deployed on the system. The proposed system is modular and can be deployed on different robotic platforms with minimum setup.
A way of combining a relatively new sensor-technology, that is optical analog VLSI devices, with a standard digital omni-directional vision system is investigated. The sensor used is a neuromorphic analog VLSI sensor that estimates the global visual image motion. The sensor provides two analog output voltages that represent the components of the global optical flow vector. The readout is guided by an omni-directional mirror that maps the location of the ball and directs the robot to align its position so that a sensor-actuator module that includes the analog VLSI optical flow sensor can be activated. The purpose of the sensor-actuator module is to operate with a higher update rate than the standard vision system and thus increase the reactivity of the robot for very specific situations. This paper will demonstrate an application example where the robot is a goalkeeper with the task of defending the goal during a penalty kick.
Vision-based motion detection, an important skill for an autonomous mobile robot operating in dynamic environments, is particularly challenging when the robot's camera is in motion. In this paper, we use a Fourier-Mellin transform-based image registration method to compensate for camera motion before applying temporal differencing for motion detection. The approach is evaluated online as well as offline on a set of sequences recorded with a Care-O-bot 3, and compared with a feature-based method for image registration. In comparison to the feature-based method, our method performs better both in terms of robustness of the registration and the false discovery rate.
Co-design is concerned with the joint design of hardware and software making up an embedded computer system [Wol94]. A top down design flow for an embedded system begins with a system specification. If it is executable, it may be used for simulation, system verification or to identify algorithmical bottlenecks. In contrast to other chapters of this book, the specification is not developed in this case study, rather it is given from the beginning. Furthermore we are not concerned with partitioning or synthesis of dedicated HW. Instead we focus on the problem how to find an off-the-shelf micro-controller which implements the desired functionality and meets all specification constraints. If feasible, this is usually much cheaper then using dedicated hardware. This chapter will answer the question of feasibility for a real life problem from automobile industry.
CASTLE is a co-design platform developed at GMD SET institute. It provides a number of design tools for configuring application specific design flows. This paper presents a walk through the CASTLE co-design environment, following the design flow of a video processing system. The design methodology and the tool usage for this real life example are described, as seen from a designers point of view. The design flow starts with a C/C++ program and gradually derives a register-transfer level description of a processor hardware, as well as the corresponding compiler for generating the processor opcode. The main results of each design step are presented and the usage of the CASTLE tools at each step is explained.
