Contributions

The objective of the research carried out during the completion of this PhD thesis has been to accomplish the first milestone of the above mentioned project, that is, developing a navigation system for indoor unknown unstructured environments for a wheeled robot. More precisely, we have focused on the Navigation system and on the overall robot architecture. However, we have also had to implement simple versions of the Pilot and Vision systems in order to realize and test the Navigation system.

As it may have already been noticed, this work has two main research threads: the control architecture and the mapping and navigation method.

Regarding the control architecture, we have proposed a general coordination architecture that uses a bidding mechanism for coordinating a group of systems (and agents) that control a robot. This mechanism can be used at different levels of the control architecture. In our case, we have used it to coordinate two of the systems of the robot (Navigation and Pilot systems) and also to coordinate the agents that compose the Navigation system itself. Moreover, the multiagent view of the Navigation system could also be applied to other systems, having a multiagent Pilot or a multiagent Vision system. Using this bidding mechanism, the action actually being executed by the robot is the most urgent one at each point in time, and thus, if the agents bid rationally, the dynamics of the biddings would lead the robot to execute the necessary actions in order to reach a given target. An advantage of using such mechanism is that there is no need to create a hierarchy, such as in the subsumption architecture, but it is dynamically changing depending on the specific situation of the robot and the characteristics of the environment. A second advantage is that its modular view conforms an extensible architecture. To extend this architecture with a new capability we would just have to plug in a new system (or agent).

Regarding the mapping and navigation method, we have extended the work presented by Prescott [55], so that it can be used with fuzzy information about the locations of landmarks in the environment. This is of great importance when working with real robots, as it is impossible to avoid dealing with the imprecision of real world environments. Together with this extension, we have also developed methods that permit computing diverting targets, needed by the robot when there is no clear path to the goal.