Abstract
In this paper, we present an autonomous navigation system based on a finite state machine (FSM) learned by an artificial neural network (ANN) in an indoor navigation task. This system uses a kinect as the only sensor. In the first step, the ANN is trained to recognize the different specific environment configurations, identifying the different environment situations (states) based on the kinect detections. Then, a specific sequence of states and actions is generated for any route defined by the user, configuring a path in a topological like map. So, the robot becomes able to autonomously navigate through this environment, reaching the destination after going through a sequence of specific environment places, each place being identified by its local properties, as for example, straight path, path turning to left, path turning to right, bifurcations and path intersections. The experiments were performed with a Pioneer P3-AT robot equipped with a kinect sensor in order to validate and evaluate this approach. The proposed method demonstrated to be a promising approach to autonomous mobile robots navigation.
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Sales, D., Correa, D., Osório, F.S., Wolf, D.F. (2012). 3D Vision-Based Autonomous Navigation System Using ANN and Kinect Sensor. In: Jayne, C., Yue, S., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2012. Communications in Computer and Information Science, vol 311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32909-8_31
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DOI: https://doi.org/10.1007/978-3-642-32909-8_31
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