A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constructing deterministic finite-state automata in recurrent neural networks
Journal of the ACM (JACM)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
View-based navigation using an omniview sequence in a corridor environment
Machine Vision and Applications - Special issue: Omnidirectional vision and its applications
Springer Handbook of Robotics
Stanley: The robot that won the DARPA Grand Challenge: Research Articles
Journal of Robotic Systems - Special Issue on the DARPA Grand Challenge, Part 2
Finite state automata and simple recurrent networks
Neural Computation
Autonomous driving in urban environments: Boss and the Urban Challenge
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part I
Visual Navigation for Mobile Robots: A Survey
Journal of Intelligent and Robotic Systems
The 2005 DARPA Grand Challenge: The Great Robot Race
The 2005 DARPA Grand Challenge: The Great Robot Race
Vision-Based Autonomous Navigation System Using ANN and FSM Control
LARS '10 Proceedings of the 2010 Latin American Robotics Symposium and Intelligent Robotics Meeting
Template-based autonomous navigation and obstacle avoidance in urban environments
ACM SIGAPP Applied Computing Review
Appearance-Guided Monocular Omnidirectional Visual Odometry for Outdoor Ground Vehicles
IEEE Transactions on Robotics
Autonomous Self-Localization of Mobile Robots through Reference Point Based Computer Vision
CBSEC '12 Proceedings of the 2012 Second Brazilian Conference on Critical Embedded Systems
Intelligent Robotic Car for Autonomous Navigation: Platform and System Architecture
CBSEC '12 Proceedings of the 2012 Second Brazilian Conference on Critical Embedded Systems
Multi-agent Autonomous Patrolling System Using ANN and FSM Control
CBSEC '12 Proceedings of the 2012 Second Brazilian Conference on Critical Embedded Systems
Hi-index | 0.00 |
In this paper we present an original approach applied to autonomous mobile robots navigation integrating localization and navigation using a topological map based on the proposed AFSM (adaptive finite state machine) technique. In this approach, the environment is mapped as a graph, and each possible path is represented by a sequence of states controlled by a FSM-finite state machine. An ANN (artificial neural network) is trained to recognize patterns on input data, where each pattern is associated to specific environment features or properties, consequently representing the present context/state of the FSM. When a new input pattern is recognized by the ANN (changing the current context), this allows the FSM to change to the next state and its associated action/behavior. The input features are related to specific local properties of the environment (obtained from sensors data), as for example, straight path, right and left turns, and intersections. This way, the FSM is integrated to a previously trained ANN, which acts as a key component recognizing and indicating the present state and the state changes, allowing the AFSM to select the current/correct action (local reactive behaviors) for each situation. The AFSM allows the mobile robot to autonomously follow a sequence of states/behaviors in order to reach a destination, first choosing an adequate local reactive behavior for each current state, and second detecting the changes in the current context/state, following a sequence of states/actions that codes the topological (global) path into the FSM (sequence of states/actions). The ANN is also a very important component of this system, since it can be trained/adapted to recognize a complex set of situations and state changes. In order to demonstrate the robustness of the proposed approach to different situations and sensors configurations, we evaluated the proposed approach for both indoor and outdoor environments, using a Pioneer P3-AT robot equipped with Kinect sensor for indoor environments, and an automated vehicle equipped with a standard RGB camera for urban roads environments. The proposed method was tested in different situations with success and demonstrated to be a promising approach to autonomous mobile robots control and navigation.