This paper deals with the modelling and control of balanced wheeled autonomous mobile robot. For the MBS dynamics modelling software tool Matlab-SimMechanics is used. The model derived automatically from geometric-topological description of MBS is used for the control purposes (local linearization for state space control, testing of nonlinear system controlled by LQR) and also as a reference during the analytical model formulation for global feedback linearization. The dual accelerometer is used as a tilt sensor and the proposed method for sensory processing is described in this paper. The approach is based on iterative solution of nonlinear equation. Control using the state space (LQR) and the feedback linearization is compared. Also, the influence of sensor noises and delays implemented into the model are discussed. Finally, the solution is verified on real physical model controlled by means of hardware ni the loop (HIL). and Obsahuje seznam literatury
A topologically oriented neural network is very efficient in real-time path planning of a mobile robot in dynamic environments. Using a dynamic recurrent neural network to solve the partial differential equation of a potential field in a discrete manner, the problem of obstacle avoidance and path planning of a moving robot can be efficiently solved. A dimensional network used to represent the topology of the robot's workspace, where each network node represents a state associated with a local workspace point. In this paper, two approaches associated with different boundary conditions are proposed, namely, Dirichlet and Neumann conditions. The first approach relies on a field of attraction distributed around the moving target, acting as a unique local extreme in the local network space. The steepest gradients of the network state variables will aim towards the source of the potential field. The second approach considers two attractive and repulsive potential sources associated with the start and destination points. A dynamic neural mesh is used to model the robot workspace. A simulation package has been built and extensive computer experiments were conducted to demonstrate and validate the reliability of the presented approach.
In this paper, the mobile robot localization problem is investigated under the stochastic communication protocol (SCP). In the mobile robot localization system, the measurement data including the distance and the azimuth are received by multiple sensors equipped on the robot. In order to relieve the network burden caused by network congestion, the SCP is introduced to schedule the transmission of the measurement data received by multiple sensors. The aim of this paper is to find a solution to the robot localization problem by designing a time-varying filter for the mobile robot such that the filtering error dynamics satisfies the H∞ performance requirement over a finite horizon. First, a Markov chain is introduced to model the transmission of measurement data. Then, by utilizing the stochastic analysis technique and completing square approach, the gain matrices of the desired filter are designed in term of a solution to two coupled backward recursive Riccati equations. Finally, the effectiveness of the proposed filter design scheme is shown in an experimental platform.
The paper gives detailed overview of the prototype of autonomous mobile robot Advee, developed for presentation purposes. Mechanical, electrical, sensor and software subsystems are described in the paper together with key algorithms such as localization and motion planning. Advee already performed over 600 hours of operation among common people, the experiences gained from the operation are also summarized in the paper. and Obsahuje seznam literatury
The mobile robot path planning involves finding the shortest and least difficult path from a start to a goal position in a given environment without collisions with known obstacles.
The main idea of case-based reasoning (CBR) is a presumption that similar tasks probably also have similar solutions. New tasks are solved by adapting old proved solutions of similar tasks to new conditions. Tasks and their solutions (cases) are stored in a case base.
The focal point of this paper is the proposition of a path planning method based on CBR combined with graph algorithms in the environment represented by a rectangular grid. On the basis of the experimental results obtained, it is possible to say that case-based reasoning can significantly save computation costs, particularly in large environments. and Obsahuje seznam literatury