Ultrasound-based Navigation for Mobile Robots

This thesis presents an implementation of a positioning and navigation system for a mobile robot using ultrasonic pulses and passive sensors that are part of a sensor network. The system uses the Telos Tmote Sky sensor-boards running Contiki. In addition to the Tmote Sky the mobile robot consists of a number of processors and is equipped with position encoders for the wheels in order to be able to accurately estimate the position using dead-reckoning. It is also equipped with an ultrasound transmitter. The sensor nodes are equipped with ultrasound receivers

Tyre Pressure Monitoring using Sensors

This thesis presents an implementation of a positioning and navigation system for a mobile robot using ultrasonic pulses and passive sensors that are part of a sensor network. The system uses the Telos Tmote Sky sensor-boards running Contiki. In addition to the Tmote Sky the mobile robot consists of a number of processors and is equipped with position encoders for the wheels in order to be able to accurately estimate the position using dead-reckoning. It is also equipped with an ultrasound transmitter. The sensor nodes are equipped with ultrasound receivers

A Generalized Neural Network Approach to Mobile Robot Navigation and Obstacle Avoidance

In this thesis, we tackle the problem of extending neural network navigation algorithms for various types of mobile robots and 2-dimensional range sensors. We propose a general method to interpret the data from various types of 2-dimensional range sensors and a neural network algorithm to perform the navigation task. Our approach can yield a global navigation algorithm which can be applied to various types of range sensors and mobile robot platforms. Moreover, this method allows the neural networks to be trained using only one type of 2-dimensional range sensor, which contributes positively to reducing the time required for training the networks. Experimental results carried out in simulation environments demonstrate the effectiveness of our approach in mobile robot navigation for different kinds of robots and sensors. Therefore, the successful implementation of our method provides a solution to apply mobile robot navigation algorithms to various robot platforms

A New Classification Technique in Mobile Robot Navigation

This paper presents a novel pattern recognition algorithm that use weightless neural network (WNNs) technique.This technique plays a role of situation classifier to judge the situation around the mobile robot environment and makes control decision in mobile robot navigation. The WNNs technique is choosen due to significant advantages over conventional neural network, such as they can be easily implemented in hardware using standard RAM, faster in training phase and work with small resources. Using a simple classification algorithm, the similar data will be grouped with each other and it will be possible to attach similar data classes to specific local areas in the mobile robot environment. This strategy is demonstrated in simple mobile robot powered by low cost microcontrollers with 512 bytes of RAM and low cost sensors. Experimental result shows, when number of neuron increases the average environmental recognition ratehas risen from 87.6% to 98.5%.The WNNs technique allows the mobile robot to recognize many and different environmental patterns and avoid obstacles in real time. Moreover, by using proposed WNNstechnique mobile robot has successfully reached the goal in dynamic environment compare to fuzzy logic technique and logic function, capable of dealing with uncertainty in sensor reading, achieving good performance in performing control actions with 0.56% error rate in mobile robot speed

A Novel Artificial Organic Controller with Hermite Optical Flow Feedback for Mobile Robot Navigation

This chapter describes a novel nature-inspired and intelligent control system for mobile robot navigation using a fuzzy-molecular inference (FMI) system as the control strategy and a single vision-based sensor device, that is, image acquisition system, as feedback. In particular, FMI system is proposed as a hybrid fuzzy inference system with an artificial hydrocarbon network structure as defuzzifier that deals with uncertainty in motion feedback, improving robot navigation in dynamic environments. Additionally, the robotics system uses processed information from an image acquisition device using a real-time Hermite optical flow approach. This organic and nature-inspired control strategy was compared with a conventional controller and validated in an educational robot platform, providing excellent results when navigating in dynamic environments with a single-constrained perception device

A mosaic of eyes

Autonomous navigation is a traditional research topic in intelligent robotics and vehicles, which requires a robot to perceive its environment through onboard sensors such as cameras or laser scanners, to enable it to drive to its goal. Most research to date has focused on the development of a large and smart brain to gain autonomous capability for robots. There are three fundamental questions to be answered by an autonomous mobile robot: 1) Where am I going? 2) Where am I? and 3) How do I get there? To answer these basic questions, a robot requires a massive spatial memory and considerable computational resources to accomplish perception, localization, path planning, and control. It is not yet possible to deliver the centralized intelligence required for our real-life applications, such as autonomous ground vehicles and wheelchairs in care centers. In fact, most autonomous robots try to mimic how humans navigate, interpreting images taken by cameras and then taking decisions accordingly. They may encounter the following difficulties

Development of water surface mobile garbage collector robot

This paper presents a prototype of Water Surface Mobile Garbage Collector Robot built in motivation to educate the people to love and monitor the health of our rivers by collecting the trash themselves using mobile robot. The garbage collector is designed aimed for the cleaning of small-scale lakes, narrow rivers, and drains in Malaysia. The navigation of the robot is controlled using wireless Bluetooth communication from a smartphone application. The performance of the water garbage collector in terms of manoeuvring control efficiency and garbage collection load capacity was tested and evaluated. Based on the experimental results from a swimming pool, it can operate within a 4-metre range and collect 192 grams of small to medium sized recyclable garbage such as food packages, water bottles, and plastics in 10 seconds. It managed to float and navigate on the Panchor River within Bluetooth network range. A strong, lightweight and waterproof material is recommended for use for this water garbage collector. A proximity sensor or image processing technique for detecting garbage on the water surface may be studied and included in the future to enable a fully autonomous manoeuvring control system

Design of a multiple bloom filter for distributed navigation routing

Unmanned navigation of vehicles and mobile robots can be greatly simplified by providing environmental intelligence with dispersed wireless sensors. The wireless sensors can work as active landmarks for vehicle localization and routing. However, wireless sensors are often resource scarce and require a resource-saving design. In this paper, a multiple Bloom-filter scheme is proposed to compress a global routing table for a wireless sensor. It is used as a lookup table for routing a vehicle to any destination but requires significantly less memory space and search effort. An error-expectation-based design for a multiple Bloom filter is proposed as an improvement to the conventional false-positive-rate-based design. The new design is shown to provide an equal relative error expectation for all branched paths, which ensures a better network load balance and uses less memory space. The scheme is implemented in a project for wheelchair navigation using wireless camera motes. © 2013 IEEE

Enhancing smart environments with mobile robots

Sensor networks are becoming popular nowadays in the development of smart environments. Heavily relying on static sensor and actuators, though, such environments usually lacks of versatility regarding the provided services and interaction capabilities. Here we present a framework for smart environments where a service robot is included within the sensor network acting as a mobile sensor and/or actuator. Our framework integrates on-the-shelf technologies to ensure its adaptability to a variety of sensor technologies and robotic software. Two pilot cases are presented as evaluation of our proposal.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech