Audio Localization for Robots Using Parallel Cerebellar Models

© 2016 IEEE. A robot audio localization system is presented that combines the outputs of multiple adaptive filter models of the Cerebellum to calibrate a robot's audio map for various acoustic environments. The system is inspired by the MOdular Selection for Identification and Control (MOSAIC) framework. This study extends our previous work that used multiple cerebellar models to determine the acoustic environment in which a robot is operating. Here, the system selects a set of models and combines their outputs in proportion to the likelihood that each is responsible for calibrating the audio map as a robot moves between different acoustic environments or contexts. The system was able to select an appropriate set of models, achieving a performance better than that of a single model trained in all contexts, including novel contexts, as well as a baseline generalized cross correlation with phase transform sound source localization algorithm. The main contribution of this letter is the combination of multiple calibrators to allow a robot operating in the field to adapt to a range of different acoustic environments. The best performances were observed where the presence of a Responsibility Predictor was simulated

Self-adaptive context aware audio localization for robots using parallel cerebellar models

An audio sensor system is presented that uses multiple cerebellar models to determine the acoustic environment in which a robot is operating, allowing the robot to select appropriate models to calibrate its audio-motor map for the detected environment. The use of the adaptive filter model of the cerebellum in a variety of robotics applications has demonstrated the utility of the so-called cerebellar chip. This paper combines the notion of cerebellar calibration of a distorted audio-motor map with the use of multiple parallel models to predict the context (acoustic environment) within which the robot is operating. The system was able to correctly predict seven different acoustic contexts in almost 70% of cases tested

A Surveillance Mobile Robot based on Low-Cost Embedded Computers

Robot technology is everywhere from health to retail. It is very rare to see the use of robots in developing countries. It is also a big task for companies to hire staffs for the purpose of monitoring as it requires to work especially in the night. It is also dangers for human. It is a good idea to have some robots for the purpose of monitoring. This paper focuses on the development of surveillance mobile robot using an Arduino board, an ESP32 board and a Raspberry PI sys-tem. The robot is a proof of concept of possible robotic application in warehouses where monitoring technologies need to be embedded in the system. In this con-text, the device is equipped with a servo motor and an on-board camera. The end-user can monitor the robot remotely by wirelessly controlling the position of the servo (i.e. the camera). The camera module is controlled by the Raspberry PI and incorporate a motion detection system which provides an alarm and capture the image frame as soon as the motion is detected. Finally, the image is sent to the end-user. A set of preliminary test of the system has been performed in order to proof the reliability of the proposed prototype

Feed-forward selection of cerebellar models for calibration of robot sound source localization

We present a responsibility predictor, based on the adaptive filter model of the cerebellum, to provide feed-forward selection of cerebellar calibration models for robot Sound Source Localization (SSL), based on audio features extracted from the received audio stream. In previous work we described a system that selects the models based on sensory feedback, however, a drawback of that system is that it is only able to select a set of calibrators a-posteriori, after action (e.g. orienting a camera toward the sound source after a position estimate is made). The responsibility predictor improved the system performance compared to that without responsibility prediction. We show that a trained responsibility predictor is able to use contextual signals in the absence of ground truth to successfully select models with a performance approaching that of a system with full access to the ground truth through sensory feedback

Calibration of sound source localisation for robots using multiple adaptive filter models of the cerebellum

The aim of this research was to investigate the calibration of Sound Source Localisation (SSL) for robots using the adaptive filter model of the cerebellum and how this could be automatically adapted for multiple acoustic environments. The role of the cerebellum has mainly been identified in the context of motor control, and only in recent years has it been recognised that it has a wider role to play in the senses and cognition. The adaptive filter model of the cerebellum has been successfully applied to a number of robotics applications but so far none involving auditory sense. Multiple models frameworks such as MOdular Selection And Identification for Control (MOSAIC) have also been developed in the context of motor control, and this has been the inspiration for adaptation of audio calibration in multiple acoustic environments; again, application of this approach in the area of auditory sense is completely new. The thesis showed that it was possible to calibrate the output of an SSL algorithm using the adaptive filter model of the cerebellum, improving the performance compared to the uncalibrated SSL. Using an adaptation of the MOSAIC framework, and specifically using responsibility estimation, a system was developed that was able to select an appropriate set of cerebellar calibration models and to combine their outputs in proportion to how well each was able to calibrate, to improve the SSL estimate in multiple acoustic contexts, including novel contexts. The thesis also developed a responsibility predictor, also part of the MOSAIC framework, and this improved the robustness of the system to abrupt changes in context which could otherwise have resulted in a large performance error. Responsibility prediction also improved robustness to missing ground truth, which could occur in challenging environments where sensory feedback of ground truth may become impaired, which has not been addressed in the MOSAIC literature, adding to the novelty of the thesis. The utility of the so-called cerebellar chip has been further demonstrated through the development of a responsibility predictor that is based on the adaptive filter model of the cerebellum, rather than the more conventional function fitting neural network used in the literature. Lastly, it was demonstrated that the multiple cerebellar calibration architecture is capable of limited self-organising from a de-novo state, with a predetermined number of models. It was also demonstrated that the responsibility predictor could learn against its model after self-organisation, and to a limited extent, during self-organisation. The thesis addresses an important question of how a robot could improve its ability to listen in multiple, challenging acoustic environments, and recommends future work to develop this ability