Showing papers on "Autonomous system (mathematics) published in 1994"

Structured control for autonomous robots

Abstract: To operate in rich, dynamic environments, autonomous robots must be able to effectively utilize and coordinate their limited physical and computational resources. As complexity increases, it becomes necessary to impose explicit constraints on the control of planning, perception, and action to ensure that unwanted interactions between behaviors do not occur. This paper advocates developing complex robot systems by layering reactive behaviors onto deliberative components. In this structured control approach, the deliberative components handle normal situations and the reactive behaviors, which are explicitly constrained as to when and how they are activated, handle exceptional situations. The Task Control Architecture (TCA) has been developed to support this approach. TCA provides an integrated set of control constructs useful for implementing deliberative and reactive behaviors. The control constructs facilitate modular and evolutionary system development: they are used to integrate and coordinate planning, perception, and execution, and to incrementally improve the efficiency and robustness of the robot systems. To date, TCA has been used in implementing a half-dozen mobile robot systems, including an autonomous six-legged rover and indoor mobile manipulator. >

Behavior control for robotic exploration of planetary surfaces

Abstract: This paper describes a series of robots developed at JPL to demonstrate the feasibility of using a behavior-control approach to control small robots on planetary surfaces. The round-trip light-time delay makes direct teleoperation of a mobile robot on a planetary surface impossible. Planetary rovers must therefore possess a certain degree of autonomy. However, small robots can only support small computers (due mostly to power, not size constraints). Behavior control provides a means of autonomous control that requires very little computation. The robots described in this paper all used 8-bit, 1-MIP microprocessors with as little as 4 k and no more than 40 k of memory, and extremely simple sensors. Despite these limitations they reliably perform both autonomous navigation and manipulation in both indoor and outdoor rough-terrain environments. >

An autonomous vision-based mobile robot

Abstract: This paper describes the theoretical development and experimental implementation of a complete navigation procedure for use in an autonomous mobile robot for structured environments. Estimates of the vehicle's position and orientation are based on the rapid observation of visual cues located at discrete positions within the environment. The extended Kalman filter is used to combine these visual observations with sensed wheel rotations to produce optimal estimates continuously. The complete estimation procedure, as well as the control algorithm, developed are time independent. A naturally suitable quantity involving wheel rotations is used as the independent variable. One consequence of this choice is that the vehicle speed can be specified independently of the estimation and control algorithms. Reference paths are "taught" by manually leading the vehicle through the desired path. Estimates produced by the extended Kalman filter during this teaching session are then used to represent the geometry of the path. The tracking of taught reference paths is accomplished by controlling the position and orientation of the vehicle relative to the reference path. Time-independence path tracking has necessitated the development of a novel, geometry-based means for advancing along the reference path. >

Distributed and autonomous control method for generating shape of multiple mobile robot group

Abstract: This paper proposes a distributed and autonomous control method for generating the shape of a group that consists of multiple mobile robots. This control method is called linear autonomous system (LAS). With LAS, the controllability of group shape can be defined. This is because a decision of a shape of the group corresponds to designing a potential field that is spread on a space of relative distance between mobile robots. Since each mobile robot has parameter for designing the spread potential field, a shape of the group can be adapted to various situations by means of changing the parameters with respect to state of environment, for example, the position of wall, a velocity of an obstacle, etc. Effectiveness of this control method is verified by a computer simulation. It means that a mobile robot is captured by a group that consists of multiple mobile robots. >

A reflexive navigation algorithm for an autonomous mobile robot

Abstract: A new, reflexive type of navigation for the Leuven intelligent autonomous system (LIAS), implemented in a distributed transputer system, is presented. The mobile robot is meant to travel as fast and as safe as possible in a dynamic, vague or even completely unknown and unstructured factory environment. The navigation system consists of a reactive planning module combined with a low level fuzzy logic avoidance behaviour which enables the robot to move to a goal by only specifying the goal coordinates. A fusion algorithm combines perception information of three different sensors for accurate modelling of the world. The presented navigation method was tested with the real robot and it proved to be useful in real world applications. >

Distributed Strategy-making Method in Multiple Mobile Robot System

Abstract: A distributed algorithm for making strategies in multiple autonomous system is presented in this paper. Here, every robot in the system has several tactics to be selected, and a set of probabilities to select each tactic is called a strategy. The problem is “how does each robot get one’s proper strategy independently by trying tactics, getting responses from the environment around itself and revising each strategy?” From the viewpoint of simplicity of modeling and analysis, authors adopt reinforcement-learning-like approach. Each robot memorizes “evaluated pay-off values for every tactic” and the “strategy,” and revises those values asymptotically by iterating four steps as follows: (1) select a certain tactic by reflecting the present strategy of its own and try it, (2) get pay-off value for the applied tactic from the environment, (3) revise the estimated pay-off value of tactics, (4) revise the strategy by using (3). Path-selecting simulation of multiple mobile robots, in which fifty robots move between two areas through two paths, is made to verify effectiveness of the proposed method. By evaluating turnaround time of robots with variety of path widths′ ratios, the proposed algorithm is shown to be superior to other algorithms such as one-way-traffic-strategy. Convergence to global optimal solution derived by the proposed method is discussed for simplified situation by using payoff matrix in theory of game.

The autonomous instrument: A design

Abstract: A design of software for an autonomous atomic emission instrument is presented with the emphasis on a subsystem for automatic selection of calibration methodology for inductively coupled plasma atomic emission spectrometry (ICP-AES) is described. The suitability of the 1st-Class expert system shell for development and final implementation of this system is discussed. The calibration methodology system is described in the context of ongoing research into the development of autonomous ICP instrumentation.

Event Diagnosis and Recovery in Real-Time On-Board Autonomous Mission Control

Abstract: Space missions will require, in a near future, different levels of on-board autonomy. An autonomous system shall have the possibility to detect eventual malfunctions or failures. As it is a rather complex system, the usual checks on the status of the subsystems are not enough to give a clear understanding of the possible situations that could occur.

Global path planning for mobile robot with grid-type world model

Abstract: This paper presents a new methodology for global path planning for an autonomous mobile robot in a grid-type world model. The value of a certainty grid representing the existence of an obstacle in the grid is calculated from readings of sonar sensors. In the calculation, a way of utilizing three sonar sensor readings at a time is introduced, resulting in a more accurate world model. Once the world model is obtained, a network for path planning is built by using the model. The global paths, defined as the shortest paths between all pairs of nodes in the network, are calculated. A fast algorithm using a decomposition technique is proposed for real-time calculation. The new methodology has been implemented on the mobile robot whose role is to transport materials in a flexible manufacturing system. The results show that the proposed method of certainty grids satisfactorily represents a precise environment, including the locations of obstacles. Thus, the robot successfully comprehends its surroundings, and navigates to its destinations along optimal paths.

Advanced Underwater Power Systems

Abstract: The defence, commercial and scientific communities are all aware of the strategic importance that the oceans hold. Future underwater operations are under evaluation using autonomous ocean-ranging submersibles of the unmanned variety. Such vessels are commonly termed autonomous underwater vehicles (AUVs), and the search for suitable power systems that are able to provide high reliability coupled with long underwater durations has intensified over the last decade. This paper presents a review of those power systems that are under consideration, design and development, mainly for the AUV application. No attempt is made by the authors to directly compare individual systems, as this can only be effectively undertaken once a well-defined vehicle and associated mission profile has been defined.

Complex neural architectures for emerging cognitive abilities in an autonomous system

Abstract: We propose a novel neural architecture named PerAc which is a systematic way to decompose the control of an autonomous robot in perception and action flows We first present an application of the PerAc architecture to the simulation of a vision system with a moving eye Then we propose a second application where the robot learns to return from any starting place to a previously discovered and learned position without any a priori symbolic representation

AMOS; active perception of an autonomous system

Abstract: In the autonomous mobile systems project (AMOS), the FAW uses a mobile robot to study questions related to the deep integration of sub-symbolic and symbolic information processing. AMOS aims at methods for autonomously acquiring new concepts via induction from its interaction with its environment. This paper presents an architecture which integrates both symbolic planning as well as nonsymbolic reactive mechanisms, thus providing a basic autonomy, so that the robot can freely maneuver around without any detailed model of itself and its complex real-world environment . Substantial differences between expectation and observation are used as hints-generated via the robot's interaction with the environment-to situations which are of relevance for the robot. In particular, the concepts of plan breakdown and region of interest play a fundamental role. Autonomously, based on the robot's decision, images are taken and clustered without supervision into groups which are expected to correspond to semantically similar situations. These hypotheses shall be used in further work as a necessary pre-requisite in order to autonomously generate a new concept relating the recognition of such perception classes to appropriate actions. >

Vision‐based path following by using a neural network Guidance System

Abstract: This article describes a neural network controller for guidance of a robot arm, used to model some aspects of autonomous vehicle technology. The controller uses video images with adaptive view-angles for the sensory input, and the system was configured to simulate an autonomous vehicle guidance system on a flat terrain using a high-contrast guiding path. To demonstrate the feasibility of using neural networks in this type of application, an Intelledex 405 robot fitted with a video camera and associated vision system was used. Phase I of the project consisted of a single-speed implementation and limited network training. Phase II featured a multi-speed implementation using adaptively varied view-angles based on robot arm velocity. It was shown that the neural network controller was able to control the robot arm along a path composed of path segments unlike those with which it was trained. In addition it was shown that a multi-speed implementation with adaptive view angles improved system performance. © 1994 John Wiley & Sons, Inc.

HASLEARN: a highly autonomous system with learning behavior

Abstract: HASLEARN is a highly intelligent autonomous system that contains multistrategy learning capabilities which are integrated by a four-stages incremental learning processes. So HASLEARN can work like a human operator to learn operationalized reactive action rules, to recover and eliminate any accidents or anomalisms, and to learn prediction rules to warn the development of potential, abnormal states based on the current states when it is applied in a nuclear power plant. This paper describes the embedded learning capabilities in the planner component of HASLEARN. >

Optimal Control of Ordinary Differential Systems

Abstract: This chapter is devoted to optimal control problems governed by ordinary differential systems with main emphasis on first order conditions of optimality (the maximum principle). For reader’s convenience we did not formulate either the optimal control problem or the maximum principle into their most general form. However, we have chosen a sufficiently large framework in order to cover most of the application.