In order to anticipate dangerous events, like a collision, an agent needs to make long-term predictions. However, those are challenging due to uncertainties in internal and external variables and environment dynamics. A sensorimotor model is acquired online by the mobile robot using a state-of-the-art method that learns the optical flow distribution in images, both in space and time. The learnt model is used to anticipate the optical flow up to a given time horizon and to predict an imminent collision by using reinforcement learning. We demonstrate that multi-modal predictions reduce to simpler distributions once actions are taken into account.
In our work we are very interested in providing long-term predictions. One option is to learn a model based on a differential equation of how sensor values change @cite_1 . Then we can anticipate sensory states at arbitrary times by simulating such a system, although accuracy decreases quickly depending on model complexity. Unfortunately, this cannot be reused directly to predict collisions and cannot handle multi-modality unless using a mixture.
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This paper presents a predictive model of sensor readings for mobile robot. The model predicts sensor readings for given time horizon based on current sensor readings and velocities of wheels assumed for this horizon. Similar models for such anticipation have been proposed in the literature. The novelty of the model presented in the paper comes from the fact that its structure takes into account physical phenomena and is not just a black box, for example a neural network. From this point of view it may be regarded as a semi-phenomenological model. The model is developed for the Khepera robot, but after certain modifications, it may be applied for any robot with distance sensors such as infrared or ultrasonic sensors. Keywords—Mobile robot, sensors, prediction, anticipation. I. INTRODUCTION ODELS of mobile robots usually takes into account only their kinematics and dynamics. Then variables describing the state of the mobile robot are: location coordinates, direction (azimuth) and sometimes velocity. In such models usually readings from the distance sensors are not taken into account. Such measurements, describing the distance from obstacles, are utilized only during generation of the control signals (velocities of the wheels). Moreover, only present measurements are used without any anticipation of measurements. Such anticipation may improve the control quality. If, for example, during the so called behavioral control the "avoid obstacles" rule is activated if the sensory reading exceed assumed threshold, then the control signals might be improved based on the anticipation of the sensory readings. In the literature several attempts of anticipation of sensory readings are reported but they are not serve during the control of the robot but for selection of landmarks (3). For example in articles (2) and (5) an artificial feedforward neural network was proposed as a predictor of further measurements of sensors. The proposed predictor was very simple - it contained only one layer and took as inputs only past readings from neighbouring sensors and did not use wheel velocities of the mobile robot. Such simplified model was good enough for the landmark selection based on the difference between the predicted and real readings but for the control purposes a