FUZZY NAV A Vision Based Robot Navigation Architecture using Fuzzy Inference for Uncertainty Reasoning

We report here a new but robust vision based control architecture for indoor mobile robot navigation On one hand this architecture takes advantage of the high throughput of neural networks for the processing of camera images And on the other it employs fuzzy logic to deal with the uncertainty in the inferences drawn from the vision data In particular we will present in this paper the sixteen fuzzy terms that have proved su cient for the desired navigational behavior The reader should also note that the architecture we present in this paper allows our robot to simultaneously navigate and avoid obstacles both static and dynamic Introduction As the readers probably know our laboratory has previously produced two radically di erent control architectures for vision based navigation by indoor robots The FINALE system presented in allowed our robot to navigate at roughly meters minute using model based vision in which expectation maps constructed from a geometrical model of the hallways are compared with camera images to determine the location of the robot The FINALE system is heavily geometrical in the sense that it requires that a D model of the hallways be known Our second architecture the NEURO NAV system presented in was more human friendly in the sense that it used topological models of hallways As discussed in from the topological models of space the path planner in NEURO NAV outputs a sequence of navigational commands like straight to the second T junction turn right straight to the third door on the left In executing these commands the robot invokes an ensemble of neural networks for steering control For example the input to one of the neural networks the Hallway Follower consists of the Hough space of the camera image each output node of this network corresponds to a command to the robot to turn right or left by a certain number of degrees With the same hardware as for FINALE the NEURO NAV system is able to generate a steering command every three seconds Readers should refer to for other approaches The work reported in this paper represents an advance over NEURO NAV an advance that will allow our robot to ultimately go beyond just navigation and also engage in more complex tasks such as simultaneously nding an object while it is navigating The robot might for example look for a re extinguisher whose location is only approximately known in the hallway This enhanced level of intelligence is made possible by using fuzzy logic to couple the output of the various neural networks to a supervisory controller which decides what commands to issue to the actuators More speci cally while in the NEURO NAV system each steering command produced by one of the neural network was considered as a categorical command in our present work it is now treated as a command with a certain ambiguity associated with it a much more natural thing to do Similarly the NEURO NAV system treated each distance estimate produced by one of the neural networks categorically In our new system an ambiguity interval is associated with each such estimate Some references are helpful to understand how fuzzy inference takes place in real time applications In what follows in Section we will rst describe the overall architecture of our new system that we have named FUZZY NAV Section will then delve into the Fuzzy Supervisory Controller the heart of FUZZY NAV We will display some of the rules and talk about the linguistic variables and the associated fuzzy terms and their membership functions Finally in Section we will discuss the working of the entire system and show some results This work was supported by the O ce of Naval Research under Grant ONR N Architecture of FUZZY NAV