A review of sensor technology and sensor fusion methods for map-based localization of service robot

Service robot is currently gaining traction, particularly in hospitality, geriatric care and healthcare industries. The navigation of service robots requires high adaptability, flexibility and reliability. Hence, map-based navigation is suitable for service robot because of the ease in updating changes in environment and the flexibility in determining a new optimal path. For map-based navigation to be robust, an accurate and precise localization method is necessary. Localization problem can be defined as recognizing the robot’s own position in a given environment and is a crucial step in any navigational process. Major difficulties of localization include dynamic changes of the real world, uncertainties and limited sensor information. This paper presents a comparative review of sensor technology and sensor fusion methods suitable for map-based localization, focusing on service robot applications

Neural-Network Based Adaptive Proxemics-Costmap for Human-Aware Autonomous Robot Navigation

In the revolution of Industry 4.0, autonomous robot navigation plays a vital role in ensuring intelligent cooperation with human workers to increase manufacturing efficiency. Human prefers to maintain a proxemic distance with other subjects for safety and comfort purposes, where the human personal-space can be represented by a costmap. Current proxemic costmaps perform well in defining the proxemic boundary to maintain the human-robot proxemic distance. However, these approaches generate static costmaps that are not adaptive towards different human states (linear position, angular position and velocity). This problem impacts the robot navigation efficiency, reduces human safety and comfort as the autonomous robot failed to prioritize avoiding certain humans over the other. To overcome this drawback, this paper proposed a neural-network based adaptive proxemic-costmap, named as NNPC, that can generate different sized personal-spaces at different human state encounters. The proposed proxemic-costmap was developed by learning a neural-network model using real human state data. A total of three human scenarios were used for data collection. The data were collected by tracking the humans in video recordings. After the model was trained, the proposed NNPC costmap was evaluated against two other state-of-art proxemic costmaps in five simulated human scenarios with various human states. Results show that NNPC outperformed the compared costmaps by ensuring human-aware robot manoeuvres that have higher robot efficiency and increased human safety and comfort. &nbsp

Adaptive Phototransistor Sensor for Line Finding

AbstractLine finding is used by wheeled mobile robot for localization. A phototransistor array was designed to detect the line position relative to the robot. This sensor is composed of six phototransistors to detect the position of line on the floor relative to the wheeled mobile robot. Because the ambience may change with time and the floor colour may be different from one location to another, an adaptive scheme has been designed to find the line on the floor. This proposed scheme consists of three parts; modulation and demodulation, threshold recognition with k-means clustering, and line finding with fuzzy logic. Modulation and demodulation technique is used to tackle the problem of different ambience in the surrounding. K-means clustering is used to recognize the contrast in the colour of line and floor while fuzzy logic is used to find the line relative to the sensor. Experiments were conducted in a microcontroller and it was found out that this scheme can find the line on the floor with minimum error

Performance evaluation of various 2-d laser scanners for mobile robot map building and localization

A study has been carried out to investigate the performance of various 2 - D laser scanners , which influ ence the map building quality and localization performance for a mobile robot. Laser scanners are increasingly used in auto mation and robotic applications. T hey are widely used as sensing devices for map building and localization in navigation of mobile ro bot. Laser scanners are commercially available, but there is very little published information on the performance comparison of various laser scanners on the mobile robot map building and localization. Hence, this work studies the performance by comparing four laser scanners which are Hokuyo URG04LX - UG01, Hokuyo UTM30LX, SICK TIM551 and Pepperl Fuchs ODM30M. The results, which are verified by comparison with the reference experimental data, indicated that the angle resolution and sensing range of laser scan ner are key factors affecting the map building quality and position estimation for localization. From the experiment, laser scanner with 0.25° angle resolution is optimum enough for building a map of sufficient quality for good localization performance . W i th 30meter of sensing range, a laser scanner can also result in better localization performance , especially in big environment