The design of a planar robotic manipulator for optimum performance of prescribed tasks

The application of a general optimization methodology, previously proposed by the authors, is extended here to the design of a three link revolute-joint planar manipulator performing more practical and complicated prescribed tasks. In particular a tool moving task and a spray painting task are considered. Both the minimization of average torque and energy usage required for execution of the tasks are addressed and the optimization is carried out with the link lengths and base coordinates taken as the five design variables. In addition to simple physical bounds placed on the variables, the maximum deliverable torques of the driving motors represent further constraints on the system. Joint angle constraints, not previously considered but of great practical importance, are also imposed in this study. This results in significantly more challenging optimization problems than those previously tackled. The complications arising from lock-up and nonassembly are handled by specially devised procedures. The optimization is carried out via penalty function formulations of the constrained problems to which the Snyman unconstrained trajectory optimization algorithm is applied in a special way. For both tasks and for both objective functions, with the full complement of constraints imposed, feasible designs with low objective function values are obtained by using, in each case, four different infeasible designs as starting points for the algorithm.     

A revisit of fast greedy heuristics for mapping a class of independent tasks onto heterogeneous computing systems

Mixed-machine heterogeneous computing (HC) environments utilize a distributed suite of different high-performance machines, interconnected with high-speed links, to perform groups of computing-intensive applications that have diverse computational requirements and constraints. The problem of optimally mapping a class of independent tasks onto the machines of an HC environment has been proved, in general, to be NP-complete, thus requiring the development of heuristic techniques for practical usage. If the mapping has real-time requirements such that the mapping process is performed during task execution, fast greedy heuristics must be adopted. This paper investigates fast greedy heuristics for this problem and identifies the importance of the concept of task consistency in designing this mapping heuristic. We further propose task priority graph based fast greedy heuristics, which consider the factors of both task consistency and machine consistency (the same concept of consistency as in previous studies). A collection of 20 greedy heuristics, including 17 newly proposed ones, are implemented, analyzed, and systematically compared within a uniform model of task execution time. This model is implemented by the coefficient-of-variation based method. The experimental results illuminate the circumstances when a specific greedy heuristic would outperform the other 19 greedy heuristics.     

Multiple tasks manipulation for a robotic manipulator

Robotic manipulators can execute multiple tasks precisely at the same time and, thus, the task-priority scheme plays an important role in implementing multiple tasks. Until now, several algorithms for task-priority have been used in solving the inverse kinematics for redundant manipulators. In this paper, through the comparative study of existing algorithms, we will propose a new method for task-priority manipulation in terms of two important criteria—algorithmic singularity and task error. This manipulation scheme will be applied to a planar three-link manipulator to demonstrate its effectiveness.     

Toward a scalable modular robotic system

In this article, we propose a simple docking method using an onboard camera module. It has two key ideas: one is image processing using a camera module and LEDs equipped on the modules, and the other is a special modular configuration designed for docking, which absorbs positional errors. We also designed a self-reconfiguration sequence to integrate a docked robot into a periodic structure. The effectiveness of the proposed method is examined by docking/integration experiments using 18 modules.     

Design of a cat-inspired robotic leg for fast running

This paper introduces a novel design for a robotic leg to achieve a fast running mechanism that is inspired by domestic cats. The skeletomuscular system and parallel leg movement of a cat are analyzed and applied to determine the link parameters and the linkage structure of the proposed mechanism. The linkage design of the leg mechanism is explained, and a kinematic analysis based on vector loop equations is performed. The effectiveness of the proposed mechanism is verified experimentally. A single leg clamped to a vertically moving slider exhibits a step frequency of 4.45 Hz while supporting a 0.5 kg body weight. The biped robot runs at an average speed of 0.75 m/s at a step frequency of 2.8 Hz for a trot gait on flat ground. This leg mechanism can facilitate the development of fast running robot systems.     

A fast two-stage ACO algorithm for robotic path planning

Ant colony optimization (ACO) algorithms are often used in robotic path planning; however, the algorithms have two inherent problems. On one hand, the distance elicitation function and transfer function are usually used to improve the ACO algorithms, whereas, the two indexes often fail to balance between algorithm efficiency and optimization effect; On the other hand, the algorithms are heavily affected by environmental complexity. Based on the scent pervasion principle, a fast two-stage ACO algorithm is proposed in this paper, which overcomes the inherent problems of traditional ACO algorithms. The basic idea is to split the heuristic search into two stages: preprocess stage and path planning stage. In the preprocess stage, the scent information is broadcasted to the whole map and then ants do path planning under the direction of scent information. The algorithm is tested in maps of various complexities and compared with different algorithms. The results show the good performance and convergence speed of the proposed algorithm, even the high grid resolution does not affect the quality of the path found.     

Incremental reinforcement learning for multi-objective robotic tasks

Recently reinforcement learning has been widely applied to robotic tasks. However, most of these tasks hide more than one objective. In these cases, the construction of a reward function is a key and difficult issue. A typical solution is combining the multiple objectives into one single-objective reward function. However, quite often this formulation is far from being intuitive, and the learning process might converge to a behaviour far from what we need. Another alternative to face these multi-objective tasks is to use what is called transfer learning. In this case, the idea is to reuse the experience gained after the learning of an objective to learn a new one. Nevertheless, the transfer affects only to the learned policy, leaving out other gained information that might be relevant. In this paper, we propose a different approach to learn problems with more than one objective. In particular, we describe a two-stage approach. During the first stage, our algorithm will learn a policy compatible with a main goal at the same time that it gathers relevant information for a subsequent search process. Once this is done, a second stage will start, which consists of a cyclical process of small perturbations and stabilizations, and which tries to avoid degrading the performance of the system while it searches for a new valid policy but that also optimizes a sub-objective. We have applied our proposal for the learning of the biped walking. We have tested it on a humanoid robot, both on simulation and on a real robot.     

Biomimetic control architecture for robotic cooperative tasks

This article proposes some control algorithms to be applied to the MIROSOT robot league architecture. The MIROSOT league soccer game concept is fairly simple: two teams of robots, with 3–5 robots per side, play football autonomously. The ball that the teams play with is an orange golf ball. Above the pitch is a machine vision camera running at 60 frames per second. This camera is linked to a server, which calculates the positions and velocities of each of the robots and the ball, and then determines what each robot should be doing. These instructions are then communicated to the robots over wireless links. In order to develop an efficient control strategy and architecture, the robots have to use strategies from the real human soccer game. Using the software Simi Scout, a suitable analysis of tactics can be extracted from the games. After analyzing the soccer game, a number of attributes are specified and then embedded at different levels. The specified attributes are interconnected, and the analysis of the game is processed for optimization. Using this information, the robot program is adapted and experimental tests/games are played. We comment on the results, and propose an improved control architecture based on practical results.     

A software engineering approach for the development of heterogeneous robotic applications

One of the most evident characteristics of robotic applications is heterogeneity: large robotic projects involve many different researchers with very different programming needs and areas of research, using a variety of hardware and software that must be integrated efficiently (i.e.: with a low development cost) to construct applications that satisfy not only classic robotic requirements (fault-tolerance, real-time specifications, intensive access to hardware, etc.) but also software engineering aspects (reusability, maintainability, etc.). Most existing solutions to this problem either do not deal with such heterogeneity or do not cover specific robotic needs. In this paper we propose a framework for the integration of heterogeneous robotic software through a software engineering approach: the BABEL development system, which is aimed to cover the main phases of the application lifecycle (design, implementation, testing, and maintainance) when unavoidable heterogeneity conditions are present. The capabilities of our system are shown by its support for designing and implementing diverse real robotic applications that use several programming languages (C, C++, JAVA), execution platforms (RT-operating systems, MS-Windows, no operating system at all), communication middleware (CORBA, TCP/IP, USB), and also a variety of hardware components (Personal Computers, microcontrollers, and a wide diversity of sensor and actuator devices in mobile robots and manipulator arms).     

A database design for the runtime environment of a robotic workcell

Intelligent robotic workcell activities have come to require a database framework for arranging, storing and accessing information in real-time about the workcell environment in a standard way. After a brief introduction to the theory of databases (DB), we examine the general topic of robotic workcells, identify characteristics typical of robotic applications, and then present a survey of DB-related work in the robotics domain. We then construct a set of design constraints based on our analysis of robotic applications, and describe a suitable software architecture. The paper concludes with a discussion of experience gained with two generations of DB implementations.     

A two-phase analytic approach to robotic system design

A two-phase analytic approach to robotic system design is presented. The first phase evaluates the robotic technological classes according to their functional adequacy; the next phase specifies the desired robotic configuration. The methodology developed here is demonstrated for the case of installing a robot in an automated investment casting shelling production line.     

A framework for modular analysis and exploration of heterogeneous embedded systems

The increasing complexity of heterogeneous systems-on-chip, SoC, and distributed embedded systems makes system optimization and exploration a challenging task. Ideally, a designer would try all possible system configurations and choose the best one regarding specific system requirements. Unfortunately, such an approach is not possible because of the tremendous number of design parameters with sophisticated effects on system properties. Consequently, good search techniques are needed to find design alternatives that best meet constraints and cost criteria. In this paper, we present a compositional design space exploration framework for system optimization and exploration using SymTA/S, a software tool for formal performance analysis. In contrast to many previous approaches pursuing closed automated exploration strategies over large sets of system parameters, our approach allows the designer to effectively control the exploration process to quickly find good design alternatives. An important aspect and key novelty of our approach is system optimization with traffic shaping.     

A fast directional filter set for pyramidal image-based robotic systems

In seeking to develop a real-time, pyramidal imaging system for tracking and robotic visual servoing applications, inadequacies in the standard, commonly available kernels for directional filtering have led to the implementation of an alternative set of simple filters that combine to act as oriented edge-contrast operators fulfilling the familiar requirements for both speed and a highly localized response pattern. Although numerous edge operators, kernels and directional filters have been proposed, and low-level filtering is a well-established and mature area of computer vision, within the application described here, this operator set is proving superior to more established methods in efficiently preserving the response localization requirement while propagating the oriented gradient energies through multichannel resolution pyramids for further recursive processing. This paper provides a detailed discussion of the technique, the context of its development, and supporting empirical tests and statistical results. The evidence presented shows that the scheme provides a unified response to both step and line edge types, with acceptable performance in the presence of anticipated noise levels. The approach is pragmatic, of low complexity, and of general utility.     

Bio-inspired robotic impedance adaptation for human-robot collaborative tasks

To improve the robotic flexibility and dexterity in a human-robot collaboration task,it is important to adapt the robot impedance in a real-time manner to its p...     

Specialization analysis of embodied evolution for robotic collective tasks

The objective of this work is to analyze embodied evolution based algorithms in coordinated multi-robot tasks that require specialization. This type of algorithm performs a Darwinian open-ended evolution where the individuals that make up the population are embodied in the physical robots and situated in an environment. The robots interact autonomously in an asynchronous fashion, leading to a complex dynamic system in continuous evolution with dependencies among parameters that make theoretical studies of specialization quite difficult in real cases. Consequently, the aim here is to perform a theoretical analysis of this type of embodied evolution based algorithms, establishing a set of canonical parameters that define their operation. A generic algorithm of this type is designed that allows us to formally study the relevance of the canonical parameters. In this paper this study concentrates on specialization for the construction of heterogeneous robotic teams. The conclusions obtained in the theoretical framework are confirmed in a real multi-robot collective gathering task using one of the many real embodied evolution based algorithms and showing that two canonical parameters are the most relevant in terms of specialization for this type of algorithms. Some insights into how to adjust these canonical parameters in a real problem are provided.     

Extension of impedance matching to nonlinear dynamics of robotic tasks

The concept of impedance matching for linear electric circuits is extended to nonlinear position-dependent circuits that express nonlinear dynamics of robotic tasks such as holding an object of soft material and handling a rigid object with soft fingers. At the first step, impedance control is realized by negative-feedback connection of two passive (hyper-stable) blocks, one is in the forward path expressing position control of the tool endpoint and the other is in the feedback path expressing force control of pressing the object. This negative-feedback framework is naturally introduced owing to the situation that both the tool mass and the nonlinear characteristics of reproducing force of the soft material are unknown. Extension of the concept of impedance matching to such nonlinear circuits is fulfilled by optimizing the regulation of impedance control and subsequently choosing optimal parameters from the viewpoint of both the transient and stead-state responses. The relations of this extension with the well-known theorem of maximum power supply and the H-infinity tuning for disturbance attenuation are also presented.     

A genetic algorithm-based solution methodology for modular design

Combinatorial optimization problems usually have a finite number of feasible solutions. However, the process of solving these types of problems can be a very long and tedious task. Moreover, the cost and time for getting accurate and acceptable results is usually quite large. As the complexity and size of these problems grow, the current methods for solving problems such as the scheduling problem or the classification problem have become obsolete, and the need for an efficient method that will ensure good solutions for these complicated problems has increased. This paper presents a genetic algorithm (GA)-based method used in the solution of a set of combinatorial optimization problems. A definition of a combinatorial optimization problem is first given. The definition is followed by an introduction to genetic algorithms and an explanation of their role in solving combinatorial optimization problems such as the traveling salesman problem. A heuristic GA is then developed and used as a tool for solving various combinatorial optimization problems such as the modular design problem. A modularity case study is used to test and measure the performance of the developed algorithm.     

Improving scheduling of tasks in a heterogeneous environment

Optimal scheduling of parallel tasks with some precedence relationship, onto a parallel machine is known to be NP-complete. The complexity of the problem increases when task scheduling is to be done in a heterogeneous environment, where the processors in the network may not be identical and take different amounts of time to execute the same task. We introduce a task duplication-based scheduling algorithm for network of heterogeneous systems (TANH), with complexity O(V/sup 2/), which provides optimal results for applications represented by directed acyclic graphs (DAGs), provided a simple set of conditions on task computation and network communication time could be satisfied. The performance of the algorithm is illustrated by comparing the scheduling time with an existing "best imaginary level scheduling (BIL)" scheme for heterogeneous systems. The scalability for a higher or lower number of processors, as per their availability is also discussed. We have shown to provide substantial improvement over existing work on the task duplication-based scheduling algorithm (TDS).     

一种用于无线传感器网络的模块化设计方法

针对无线传感器网络应用多样化的特点,建立了基于ZigBee技术的无线传感器网络节点与网关节点的模块化软、硬件设计方案。该硬件方案具有模块化与集成度高的特点,软件方案基于嵌入式操作系统进行多种功能的模块化设计,具有良好扩展性以及可维护性。实现了一种基于单芯片平台的传感器节点与ARM平台的网关节点,讨论了当前与未来适用的各种嵌入式设计关键技术。     

A modular CMOS design of a Hamming network.

A modular design approach for the CMOS implementation of a Hamming network is proposed. The Hamming network is an optimum minimum error classifier for binary patterns and is very suitable for a VLSI implementation due to its primarily feedforward structure. First, a modular chip that contains an array of NxM exclusive-NOR transconductors computes the matching scores between M encoded exemplar patterns (with N elements per exemplar) and an unknown input pattern. Then, a winner-take-all (WTA) circuit selects the exemplar pattern that most resembles the input pattern. By interconnecting multiple modular chips, the number and size of the patterns that can be stored in the network can be easily expanded. Measured experimental results are given to illustrate the performance and limitations of the hardware implementations of the Hamming network.