智能海洋机器人技术进展

智能海洋机器人是可以在复杂海洋环境中执行各种任务的智能化无人平台, 包括智能水下机器人和智能水面机器人. 基于实践和在相关技术难题上的经验, 提出了一些关键技术问题的解决方案. 在智能水下机器人方面, 探讨了体系结构、运动控制、智能规划与决策和系统仿真等关键技术. 在智能水面机器人方面, 探讨了快速性和智能化问题. 我国在智能海洋机器人技术研究方面已经取得了较大的进步, 但距离全面应用还有很大差距.     

Autonomous robot navigation using optimal control of probabilistic regular languages

This paper addresses autonomous intelligent navigation of mobile robotic platforms based on the recently reported algorithms of language-measure-theoretic optimal control. Real-time sensor data and model-based information on the robot's motion dynamics are fused to construct a probabilistic finite state automaton model that dynamically computes a time-dependent discrete-event supervisory control policy. The paper also addresses detection and avoidance of livelocks that might occur during execution of the robot navigation algorithm. Performance and robustness of autonomous intelligent navigation under the proposed algorithm have been experimentally validated on Segway RMP robotic platforms in a laboratory environment.     

Implementing spiking neural networks for real-time signal-processing and control applications: a model-validated FPGA approach.

In this paper, we present two versions of a hardware processing architecture for modeling large networks of leaky-integrate-and-fire (LIF) neurons; the second version provides performance enhancing features relative to the first. Both versions of the architecture use fixed-point arithmetic and have been implemented using a single field-programmable gate array (FPGA). They have successfully simulated networks of over 1000 neurons configured using biologically plausible models of mammalian neural systems. The neuroprocessor has been designed to be employed primarily for use on mobile robotic vehicles, allowing bio-inspired neural processing models to be integrated directly into real-world control environments. When a neuroprocessor has been designed to act as part of the closed-loop system of a feedback controller, it is imperative to maintain strict real-time performance at all times, in order to maintain integrity of the control system. This resulted in the reevaluation of some of the architectural features of existing hardware for biologically plausible neural networks (NNs). In addition, we describe a development system for rapidly porting an underlying model (based on floating-point arithmetic) to the fixed-point representation of the FPGA-based neuroprocessor, thereby allowing validation of the hardware architecture. The developmental system environment facilitates the cooperation of computational neuroscientists and engineers working on embodied (robotic) systems with neural controllers, as demonstrated by our own experience on the Whiskerbot project, in which we developed models of the rodent whisker sensory system.     

A Semi-Autonomous Reactive Control Architecture

This paper presents a novel intelligent control architecture for semi-autonomous systems. A semi-autonomous system is defined here as that autonomous system (machine) which interacts intelligently with a human user (collaborator) who might command, modify, or override its behavior. This work has been motivated by the need for a control architecture that can interact with human users of different perceptual and cognitive capabilities. A dynamic arbitration layer forms the core of the proposed architecture. Accordingly, the architecture evolves around three main variables: degree of autonomy to reflect the user's capabilities, user's level of confidence in commanding the machine, and strength of conflict between the user's command and the machine's autonomous command. The analogy between this architecture and horseback riding is presented and finally a demonstrative application example of a robotic wheelchair is given.     

An intelligent assembly robotic system based on Relative Pose measurements

One of the major problems in realizing intelligent assembly robotic systems has been the difficulty of compensating for uncertain and erroneous situations. In particular, pose (position and orientation) uncertainties lead to a challenging problem. In this paper, we outline a Relative Pose-based Strategy for effective dynamic end-pont control of manipulators in uncertain environments. The introduced strategy imposes new requirements on the higher levels of the control. Besides appropriate planning, control and sensing strategies, an advanced supervision system architecture needs to be provided. In this paper, the fundamental issues relating to intelligent assembly robotic system design are briefly discussed and an overall control architecture is introduced. The lower portions of the proposed architecture, viz. the Execution and the Supervisory Levels are described in detail.     

Development of a real-time control architecture for a semi-autonomous underwater vehicle for intervention missions

The need for autonomous underwater vehicles (AUVs) for intervention missions becomes greater as they can perform underwater tasks requiring physical contacts with the underwater environment, such as underwater plug-in/plug-out, construction and repair, cable streaming, mine hunting, munitions retrieval, and scientific sampling. This paper describes a semi-autonomous underwater vehicle for intervention missions that has multiple on-board CPUs, redundant sensors and actuators, on-board power source and a robotic manipulator for dextrous underwater performance. Such a complex robotic vehicle system requires advanced control software architecture for on-board intelligence with a wide range of sensors and actuators to carry out required missions. In this paper, AUV control architectures are reviewed and a sensor data bus based control architecture (SDBCA) is presented. SDBCA is a modified hierarchical architecture that offers good controllability and stability while sensor data bus increases flexibility of system design, making it possible to have a prompt response from high-level control with respect to low-level sensor data. The overall sensor input mechanism of SDBCA becomes similar to the sensor input mechanism of subsumption architecture.     

An Anthropomorphic Robotic Platform for Progressive and Adaptive Sensorimotor Learning

In recent years, advances and improvements in engineering and robotics have in part been due to strengthened interactions with the biological sciences. Robots that mimic the complexity and adaptability of biological systems have become a central goal in research and development in robotics. Usually, such a collaboration is addressed to a 2-fold perspective of (i) setting up anthropomorphic platforms as test beds for studies in neuroscience and (ii) promoting new mechatronic and robotic technologies for the development of bio-inspired or humanoid high-performance robotic platforms. This paper provides a brief overview of recent studies on sensorimotor coordination in human motor control and proposes a novel paradigm of adaptive learning for sensorimotor control, based on a multi-network high-level control architecture. The proposed neurobiologically inspired model has been applied to a robotic platform, purposely designed to provide anthropomorphic solutions to neuroscientific requirements. The goal of this work is to use the bio-inspired robotic platform as a test bed for validating the proposed model of high-level sensorimotor control, with the aim of demonstrating adaptive and modular control based on acquired competences, with a higher degree of flexibility and generality than conventional robotic controllers, while preserving their robustness. To this purpose, a set of object-dependent, visually guided reach-and-grasp tasks and the associated training phases were first implemented in a multi-network control architecture in simulation. Subsequently, the offline learning realized in simulation was used to produce the input command of reach-and-grasp to the low-level position control of the robotic platform. Experimental trials demonstrated that the adaptive and modular high-level control allowed reaching and grasping of objects located at different positions and objects of variable size, shape and orientation. A future goal would be to address autonomous and progressive learning based on growing competences.     

The general architecture of adaptive robotic systems for manufacturing applications

Adaptive system often refers to the adaptive control system. In this paper, we discuss the adaptive system concept from an architectural perspective of the system. We use the robotic system as an example to illustrate the adaptive system concept, because the robotic system is a generic system underlying many manufacturing systems. Specifically, the paper presents our work on the development of a general architecture of the adaptive robotic system for manufacturing applications. The contribution of this paper includes: (i) elaboration of the concept of adaptive system and classification of means to make the system adaptive and (ii) formulation of a general architecture of the adaptive robotic system based on the criterion that the more system variants out of the architecture implies the more generality of the architecture. Throughout this paper, we use an industrial robotic system for illustration.     

The road to intelligence

It is argued that robotic platforms are the way forward towards building intelligent systems, where multiple sensors and manipulation are used for cognitive processes. It is also argued that the cue for developing the right architecture for such a system is human language.     

Design and Implementation of the Robotic Platform

The diversity of robotic research areas along with the complex requirements of hardware and software for robotic systems have always presented a challenge for system developers. Many past robot control platforms were complex, expensive, and not very user friendly. Even though several of the previous platforms were designed to provide an open architecture system, very few of the previous platforms have been reused. To address previous disadvantages, this paper describes the design and implementation of the Robotic Platform, an object-oriented development platform for robotic applications. The Robotic Platform includes hardware interfacing, servo control, trajectory generation, 3D simulation, a graphical user interface, and a math library. As opposed to distributed solutions, the Robotic Platform implements all these components in a homogenous architecture that utilizes a single hardware platform (a standard PC), a single programming language (C++), and a single operating system (the QNX Real-Time Platform) while guaranteeing deterministic real-time performance. This design leads to an open architecture that is less complex, easier to use, and easier to extend. Particularly, the area of multiple cooperating robots benefits from this kind of architecture, since the Robotic Platform achieves a high integration of its components and provides a simple and flexible means of communication. The architecture of the Robotic Platform builds on the following state-of-the-art technologies and general purpose components to further increase simplicity and reliability: (i) PC technology, (ii) the QNX Real-Time Platform, (iii) the Open Inventor library, (iv) object-oriented design, and (v) the QMotor control environment.     

Using intelligent agents to manage fuzzy business processes

Businesses are undergoing a major paradigm shift, moving from traditional management into a world of agile organizations and processes. An agile corporation should be able to rapidly respond to market changes. For this reason, corporations have been seeking to develop numerous information technology (IT) systems to assist with the management of their business processes. Many of the coming new business processes may contain embedded intelligent agent-based systems. Agent technology looks set to radically alter not only the way in which computers are interacted, but also the way complex processes, e.g., product development, are conceptualized and built. The paper presents a fuzzy approach based on an intelligent agent framework to develop modular products. This approach aims to address the research issue: "How can modular design be carried out through intelligent agents to meet a customer's fuzzy requirements using modules that come from suppliers that are geographically separated and operate on differing computer platforms?" The proposed methodology is applied to a real-world case that involves module-based synthesis at one of largest distribution centers in the world     

An FPGA-based multiprocessor-architecture for intelligent environments

In this paper we propose a SoPC-based multiprocessor embedded system for controlling ambiental parameters in an Intelligent Inhabited Environment. The intelligent features are achieved by means of a Neuro-Fuzzy system which has the ability to learn from samples, reason and adapt itself to changes in the environment or in user preferences. In particular, a modified version of the well known ANFIS (Adaptive Neuro-Fuzzy Inference System) scheme is used, which allows the development of very efficient implementations. The architecture proposed here is based on two soft-core microprocessors: one microprocessor is dedicated to the learning and adaptive procedures, whereas the other is dedicated to the on-line response. This second microprocessor is endowed with 4 efficient ad hoc hardware modules intended to accelerate the neuro-fuzzy algorithms. The implementation has been carried out on a Xilinx Virtex-5 FPGA and obtained results show that a very high performance system is achieved.     

Requirements for intelligent network service operations

A service operation architecture and operation system platform are proposed that separate commonly used information from operations functions, and that use access control functions. This enables new applications to be developed more easily and increases operating efficiency. The operation system platform is related to several surrounding platforms, and requires standardized reference points such as CMIS/P and managed objects. A managed object methodology is a suitable approach for accessing the operation system platform, and managed object classes and methods are proposed for intelligent network service operations. This architecture and platform will allow telecommunication to meet the demands created by intelligent networks for enhanced customer services, more reliable operation systems, and lower development costs. On the basis of proposed platform, service surveillance prototype systems for free-phone services have already been developed, and the next versions of the service operations systems for virtual private networks services are being developed.     

多移动机器人合作系统中的单机控制体系结构研究

随着机器人技术的不断发展,出现了合作多移动机器人系统这一新的研究和应用领域,随之而来的是对机器人控制体系的新的要求．本文分析了合作多移动机器人系统对单机控制体系结构的要求,并以此为背景,在比较两种典型的智能机器人体系结构的基础上,提出一种混合分层的体系结构     

基于ROS的智能工业机器人系统开发平台

目前,工业机器人对于机器视觉、自主路径规划等智能化功能需求日益增长.然而在传统工业机器人系统中添加智能化功能模块时需要修改大量的源码,浪费了人力和成本.本文提出的基于ROS的易扩展机器人系统开发平台,能为开发者开发智能工业机器人系统提供了方便.本平台分为服务器端和机器人端.将机器人端作为一级节点,与安装ROS的PC服务器端进行通信.机器人一级节点由二级功能节点与功能模块组成.根据此平台开发实现的JPB06六自由度工业机器人系统具有机器视觉、自主定位、语音控制等智能化功能,可以满足工业机器人对于智能化和实时控制的需求.     

A Neural Tactile Architecture Applied to Real-time Stiffness Estimation for a Large Scale of Robotic Grasping Systems

This paper presents a model for solving the problem of real-time neural estimation of stiffness characteristics for unknown objects. For that, an original neural architecture is proposed for a large scale robotic grasping systems applied for unknown object with unspecified stiffness characteristics. The force acquisition is based on tactile information from force sensors in robotic manipulator. The proposed model has been implemented on a robotic gripper with two parallel fingers and on a one d.o.f. robotic finger with opponent artificial muscles and angular displacements. This self-organized model is inspired of human biological system, and is carried out by means of Topographic Maps and Vector Associative Maps. Experimental results demonstrate the efficiency of this new approach.     

Coordinated Control Architecture for Motion Management in ADAS Systems

Advanced driver assistance systems (ADAS) seek to provide drivers and passengers of automotive vehicles increased safety and comfort. Original equipment manufacturers are integrating and developing systems for distance keeping, lane keeping and changing and other functionalities. The modern automobile is a complex system of systems. How the functionalities of advanced driver assistance are implemented and coordinated across the systems of the vehicle is generally not made available to the wider research community by the developers and manufactures. This paper seeks to begin filling this gap by assembling open source physics models of the vehicle dynamics and ADAS command models. Additionally, in order to facilitate ADAS development and testing without having access to the details of ADAS, a coordinated control architecture for motion management is also proposed for distributing ADAS motion control commands over vehicle systems. The architecture is demonstrated in a case study where motion is coordinated between the steering and the braking systems, which are typically used only for a single functionality. The integrated vehicle and system dynamics using the coordinated control architecture are simulated for various driving tasks. It is seen that improved trajectory following can be achieved by the proposed coordinated control architecture. The models, simulations and control architecture are made available for open access.      

Realization of task-based designs involving DH parameters: a modular approach

Task-based designs—proven to be successful for constrained environments—may face challenges at prototype development phase. To assist in generalized design and development of task-based serial manipulators for cluttered environments, a parameters-based modular approach is proposed. First, a task-based design strategy for serial manipulators is exhibited, using all the robotic parameters (DH parameters) as variables. The flexibility in robotic parameters enhances the possibility of good designs even for highly cluttered workspaces, but the realization of the resulting complicated designs is challenging. This work is an attempt to develop modular manipulators in correspondence to the task-based designs. The DH parameters-based proposed link modules, with reconfigurable architecture, can be adjusted and assembled to acquire the serial manipulators with designed robotic parameters. To validate the concept, some standardized 3R-configurations have been modelled using the proposed link modules. Case studies are presented on task-based design of robotic manipulators, with six and eight degrees of freedom, for service applications in realistic environments. The selected case studies include the robot design processes for applications of cleaning solar panels, and for the maintenance of the nuclear plants.