Dynamical systems in robot control architectures: A building block perspective (original) (raw)

A Hierarchical Hybrid Architecture for Mission-Oriented Robot Control

Advances in Intelligent Systems and Computing, 2014

In this work is presented a general architecture for a multi physical agent network system based on the coordination and the behaviour management. The system is organised in a hierarchical structure where are distinguished the individual agent actions and the collective ones linked to the whole agent network. Individual actions are also organised in a hybrid layered system that take advantages from reactive and deliberative control. Sensing system is involved as well in the behaviour architecture improving the information acquisition performance.

A universal sensor control architecture considering robot dynamics

International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2001

The paper presents a dynamical sensor control architecture that allows robot arms to perform tasks that with conventional feedback of sensor data fail because of delays or deviations due to the robot dynamics. The architecture distinguishes between robot positional control and refinement of desired positions using vision and/or other sensors. Each of these aspects is designed without the knowledge of

Hierarchical control systems for autonomous space robots

Guidance, Navigation and Control Conference

This paper presents a design procedure for hierarchical robot controllers which are representable by a class of automata known as locally finite topological automata. At the heart of the design procedure is an algorithm known as the factorization algorithm which allows the decomposition of the controller into hierarchies that are abstractions of the control specifications for the robot controller. The paper also discusses some possible implementation architectures for the controller.

Handbook of Robotics Chapter 8 : Robotic Systems Architectures and Programming

2012

Robot software systems tend to be complex. This complexity is due, in large part, to the need to control diverse sensors and actuators in real time, in the face of significant uncertainty and noise. Robot systems must work to achieve tasks while monitoring for, and reacting to, unexpected situations. Doing all this concurrently and asynchronously adds immensely to system complexity. The use of a well-conceived architecture, together with programming tools that support the architecture, can often help to manage that complexity. Currently, there is no single architecture that is best for all applications-different architectures have different advantages and disadvantages. It is important to understand those strengths and weaknesses when choosing an architectural approach for a given application. This chapter presents various approaches to architecting robotic systems. It starts by defining terms and setting the context, including a recounting of the historical developments in the area of robot architectures. The chapter then discusses in more depth the major types of architectural components in use today-behavioral control (see Ch. 38), executives, and task planners (see Ch. 9)-along with commonly used techniques for interconnecting those components. Throughout, emphasis will be placed on programming tools and environments that support these architectures. A case study is then presented, followed by a brief discussion of further reading.

Properties and structure of dynamic robot models for control engineering applications

Mechanism and Machine Theory, 1985

Ab~ract--Controiler design for robotic manipulators requires a fundamental physical understanding of the properties and structure of dynamic robot models. This paper focuses on the Lagrangian formulation which is attractive from both the dynamic modeling and control engineering points-of-view. Physical and mathematical properties and structural characteristics of the complete dynamic robot model are demonstrated. Implications of the model for control system analysis and design are then indicated. Physical interpretation leads naturally to the decomposition of the model into the positioning arm and end-effector subsystems and motivates the application of decentralized control to robotic manipulators. The authors then propose the application of control engineering to control the positioning arm and artificial intelligence and intelligent sensors to control the end-effector.

Dynamic Modell Adequate for Robot Control

The dynamic model of the robot can be obtained from the known physical laws: the d Alembert principle, the Lagrangian formulation or the Newton-Euler equations. These motion equations are equivalent to each other in the sense that they describe the dynamic behaviour of the same physical robot. However the structure of these equations may differ. In this paper the computational effort for a point on the trajectory in each of the three formulations is presented. It is shown that for an n DOF robot, the computational time effort is of different time orders. Also the control model suitable for robots which move at slow speeds is presented.

Dynamical neural networks for planning and low-level robot control

Systems, Man and Cybernetics, …, 2003

We use dynamical neural networks based on the neural field formalism for the control of a mobile robot. The robot navigates in an open environment and is able to plan a path for reaching a particular goal. We will describe how this dynamical approach may be used by a high level system (planning) for controlling a low level behavior (speed of the robot). We give also results about the control of the orientation of a camera and a robot body.

Variable structure robot control systems: The RAPP approach

Robotics and Autonomous Systems

This paper presents a method of designing variable structure control systems for robots. As the on-board robot computational resources are limited, but in some cases the demands imposed on the robot by the user are virtually limitless, the solution is to produce a variable structure system. The task dependent part has to be exchanged, however the task governs the activities of the robot. Thus not only exchange of some task-dependent modules is required, but also supervisory responsibilities have to be switched. Such control systems are necessary in the case of robot companions, where the owner of the robot may demand from it to