Section A of the special issue consisting of 13 papers covering a wide field is devoted to the topic of languages and software in robotics, with an emphasis on practical applications. Diverse themes are analysed and discussed, such as neural networks, fuzzy approaches, object-oriented programming, simulation and modelling, graphical robot languages and other pertinent topics. Authors from a number of countries have contributed papers describing recent research and different approaches to this important field, particularly in view of the potential applications in various areas.

This paper investigates the neural network approach to solve the inverse kinematics problem of redundant robot manipulators in an environment with obstacles. The solution technique proposed requires only the knowledge of the robot forward kinematics functions and the neural network is trained in the inverse modeling manner. Training algorithms for both the obstacle free case and the obstacle avoidance case are developed. For the obstacle free case, sample points can be selected in the work space as training patterns for the neural network. For the obstacle avoidance case, the training algorithm is augmented with a distance penalty function. A ball-covering object modeling technique is employed to calculate the distances between the robot links and the objects in the work space. It is shown that this technique is very computationally efficient. Extensive simulation results are presented to illustrate the success of the proposed solution schemes. Experimental results performed on a PUMA 560 robot manipulator is also presented.

This paper proposes a novel design method of a shared controller for telerobot systems. A shared controller can enlarge a reflected force by combining force reflection and compliance control. However, the maximum boundary of the force reflection gain guaranteeing the stability greatly depends upon characteristics of the elements in the system such as; a master arm which is combined with the human operator's hand, the environments where the slave arm contacts and the compliance controller. In normal practice, it is therefore, very difficult to determine such a maximum boundary of the gain. To overcome this difficulty, the paper proposes a force reflection gain-selecting algorithm based on neural network and fuzzy logic features. The method estimates characteristic of the master arm and the environments by using neural networks, and then, determines the force reflection gain from the estimated characteristics by using fuzzy logic. The algorithm can work in an on-line manner, and can be easily applied to any telerobot system because it requires no a priori knowledge on the system. The effectiveness of the proposed control scheme is verified through a series of experiments using a laboratory-made telerobot system.

This paper develops a method for neural network control design with sliding modes in which robustness is inherent. Neural network control is formulated to become a class of variable structure (VSS) control. Sliding modes are used to determine best values for parameters in neural network learning rules, thereby robustness in learning control can be improved. A switching manifold is prescribed and the phase trajectory is demanded to satisfy both, the reaching condition and the sliding condition for sliding modes.

This paper describes an interactive software system, developed at the Robotics and Automation Laboratory at UCLA to demonstrate innovative approaches to off-line robot programming and work-cell layout design. The software computes the time-optimal motions along specified paths, local optimal paths around an initial guess, and the global optimal path between given end-points. It considers the full robot dynamics, actuator constraints, on the payload acceleration or the gripping force, and any number of polygonal obstacles of any shape. The graphic displays provide a useful tool for interactive motion planning and workcell design.

The paper presents an object-oriented approach to the implementation of a software library (MRROC+) which contains building blocks for the construction of multi-robot system controllers tailored to meet specific demands of a task at hand. Moreover, the paper supplies a brief overview of robot programming methods.

MDSF is a generic manipulator development and simulation facility developed by Spar Aerospace Limited, as a prime contractor to the Canadian Space Agency. It is an integral part of Canada's contribution to the International Space Station (ISS) program. MDSF is capable of simulating general robotic systems with complex and flexible structures undergoing all kinds of tasks including payload hand-over and contact with the environment. Rigorous validation and configuration control have been imposed throughout the development course of the simulation facility. Examples of current applications in the ISS program are included.

In this paper we present the SMAR CAD-robotics system (Système de Modélisation et d'Animation de Robots), which we developed at the University of Poitiers. This system allows its user to deal with a great number of robotics problems through the use of a graphic simulator. We will discuss the different parts which form the SMAR system. This includes the following:

—The modeler which allows the user to build a database, describing the robot and its environment. The database generated by the system is composed of the geometric description of the objects and the kinematics description of the environment.

—The simulator and the coordinates reverser, which simulate the robot's movements.

—The collision detection algorithms used to verify task accomplishment.

—A calculation algorithm in order to find optimal placement, which determines the relative position robot/task, allowing the robot to efficiently execute the assigned task.

—The collision free-path planning algorithm allowing the system to generate trajectories in a cluttered environment.

An example dealing with a complex robotized cell will also be presented in order to demonstrate the capabilities of the system.

The first stage in developing a task planning language for redundant manipulators is that of constructing an algorithm to produce a schedule of joint displacements for the specified task. This algorithm may be based upon the use of an inverse infinitesimal kinematic analysis for the majority of the path with a finite inverse analysis used to provide an initial joint configuration and periodically to correct for any joint errors that may have accumulated. Here, it is assumed that there is complete correlation and coherence of solutions between these analyses, otherwise problems, such as a lack of repeatability may occur. This may be accomplished by modelling the manipulator as an elastic system which permits the development of a task planning method by using analyses which are all mutually coherent. In this paper, the method is developed and demonstrated using a redundant manipulator that simultaneously avoids obstables, singularities and joint motion limitations. The task planning method and associated analyses are applicable to the general serial redundant manipulator with an arbitrary degree of redundancy.

One of the important characteristics of an autonomous robot lies in the capability of self-organizing its own behaviors in order to adapt to an unknown environment. The goal of this paper is to present an effective means for investigating such a capability, which enables us to graphically build a model of the autonomous robot and dynamically observe the emergence of reactive behaviors as a result of external stimulus controlled behavioral self-organization. In particular, the paper describes an integrated software workbench, called C4 (which stands for Create, Coordinate, Condition, and Co-evolve), for modeling and simulating autonomous robotic systems, e.g., legged and dual-arm ones. The module create enables us to build top-down graphical models of novel mechanisms, followed by primitive motion pattern (e.g., gaits) specification and visualization in the coordinate module. Learning mechanisms, as embedded in module condition further allow us to test how the animated robots acquire new behavioral rules triggerable by external stimuli. Finally, the module co-evolve facilitates the analysis of the distributed intelligence of robot groups, which manifests itself from the behaviors emergent from the interaction among individual robots.

A graphical robot programming system has been developed. This system with a graphical interface is user-friendly and easy-to-learn for low-skill users. It has been developed as a prototype system under a project by the Japan Robot Association (JARA) since 1994. The system runs on a personal computer and consists of a graphical user interface and an editing system. It is designed for programming an arc welding robot in small batch production and is expected to provide low-skill users with a means to use industrial robots with ease.

Syntax-directed development is a software development method in which, the syntax of the input of the application plays a central role. The syntax description forms a frame on which semantic actions, attributes, local and global information can be attached. Much research has been done to develop theories and algorithms needed for robots to process information and interact with the environment. This paper describes how LL(1) descriptions can be used to produce automatically motion control software, object recognizers and human-computer interfaces.

The ORCCAD programming environment for robotic systems gathers control laws in continuous time at the low levels and discrete time logical aspects at higher levels. Based upon a formal definition of robotic actions, complex applications can be designed, verified and generated incrementally. The approach and tools prototypes have been validated through several applications.

There is growing interest in robots that are designed specifically to interact with people and which respond to voice commands. Very little attention has been paid, however, to the kind of verbal interaction that is possible or desirable with robots. This paper presents recent work in multimodal interfaces that addresses this question. It proposes a new form of robot-user interface, namely a collaborative conversational interface. This article explains what collaborative conversational interfaces are, argues for their application in robots, and presents strategies for designing good conversational interfaces. It concludes with a discussion of the particular challenges faced in designing conversational interfaces for robots.