This thesis is concerned with the development, design and implementation of a novel hybrid multi-agent orientated control architecture for navigation of multiple autonomous mobile robots operating in an unknown and unstructured environment populated by static and/or dynamic obstacles. The proposed hybrid control architecture is modular and draws its design from competitive tasks architecture, production rules architecture, connectionist architecture, dynamic system architecture, multi-agent architecture and subsumption architecture. The reasoning of the control architecture is both deliberative and reactive. The proposed reactive behaviours are modelled using fuzzy logic, neural networks and hybrid behavioural encoding incorporating stateflow-fuzzy logic and stateflow-neural networks. The deliberative system is comprised of finite state machines. The processing is achieved in a centralised and/or decentralised manner using the proposed controller-agent concept from the field of multi-agent systems. The framework of the control architecture is suitable for adaptation in single and multiple robot navigation. The control architecture has been implemented in MATLAB/Simulink using the full non-linear model of the MIABOT V2 mobile robots. It is evaluated incrementally in order to verify its overall control performance and the performance of each subsystem. Results show that the control architecture's modularity, distribution, reactivity and behaviourbased structure provided the overall control system with robustness in all cases of navigation tasks utilising either single or multiple mobile robots. Furthermore the results obtained show the effectiveness of the control architecture in navigation tasks involving up to five mobile robots operating in unknown static and dynamic environments. The results demonstrate that the control strategy chosen for navigation of multiple mobile robots is efficient and also established the robustness of the control system architecture against the desired requirements, such as supervision, decision-making and co-ordination of internal control structures (subsystems). The autonomous mobile robots were exposed to a complex and highly dynamic environment and successfully achieved every control objective. Their trajectories were smooth despite the interaction between several behaviours and the presence of unexpected static and dynamic obstacles. The main contributions of this thesis are: development of a novel hybrid multi-agent based control architecture called CARDS; novel approach for identification of direction of moving obstacles (other robots) using finite state machines; novel approach for behavioural encoding using hybrid solutions such as stateflow-fuzzy and stateflow-neural for autonomous robot navigation; proposed a design methodology for developing integrated solutions for autonomous mobile robotic systems and classification of the main design methodology (properties) of control systems architectures for autonomous mobile robots. Less significance contributions are: literature survey on approaches/methods related to the development of intelligent control architectures for navigation of multiple autonomous mobile robots; modelling of MIABOT V2 mobile robots; comparison between PI, fuzzy and neural controllers and algorithmic methodology for discovery of fuzzy/neural local models from observation data; identification of the relationship of the most important requirements/properties of control architecture versus the main control architecture specifications using the Quality Function Deployment tool; modular approach for modelling and evaluation of three types of sensor and sensor sensitivity.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:722584 |
| Date | January 2002 |
| Creators | Mouzakitis, Alexandros |
| Publisher | University of South Wales |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://pure.southwales.ac.uk/en/studentthesis/hybrid-control-architecture-for-navigation-of-autonomous-mobile-robots(3b3f0e2d-42f9-4bdc-b55e-b5c4cbed937f).html |
Page generated in 0.0021 seconds