High precision hybrid RF and ultrasonic chirp-based ranging for low-power IoT nodes

Hybrid acoustic-RF systems offer excellent ranging accuracy, yet they typically come at a power consumption that is too high to meet the energy constraints of mobile IoT nodes. We combine pulse compression and synchronized wake-ups to achieve a ranging solution that limits the active time of the nodes to 1 ms. Hence, an ultra low-power consumption of 9.015 µW for a single measurement is achieved. The operation time is estimated on 8.5 years on a CR2032 coin cell battery at a 1 Hz update rate, which is over 250 times larger than state-of-the-art RF-based positioning systems. Measurements based on a proof-of-concept hardware platform show median distance error values below 10 cm. Both simulations and measurements demonstrate that the accuracy is reduced at low signal-to-noise ratios and when reflections occur. We introduce three methods that enhance the distance measurements at a low extra processing power cost. Hence, we validate in realistic environments that the centimeter accuracy can be obtained within the energy budget of mobile devices and IoT nodes. The proposed hybrid signal ranging system can be extended to perform accurate, low-power indoor positioning

Developing a low-cost beer dispensing robotic system for the service industry

As the prices of commercially available electronic and mechanical components decrease, manufacturers such as Devantech and Revolution Education have made encoded motor controller systems and microcontrollers very accessible to engineers and designers. This has made it possible to design sophisticated robotic and mechatronic systems very rapidly and at relatively low cost. A recent project in the Autonomous Systems Lab at Middlesex University, UK was to design and build a small, automated, robotic bartender based around the 5 litre Heineken 'Draughtkeg' system, which is capable of patrolling a bar and dispensing beer when signalled to by a customer. Because the system was designed as a commercial product, design constraints focused on keeping the build cost down, and so electronic components were sourced from outside companies and interfaced with a bespoke chassis and custom mechanical parts designed and manufactured on site at the University. All the programming was conducted using the proprietary BASIC language, which is freely available from the PicAXE supplier at no cost. This paper will discuss the restrictions involved in building a robot chassis around 'off-theshelf' components, and the issues arising from making the human-machine interaction intuitive whilst only using low-cost ultrasonic sensors. Programming issues will also be discussed, such as the control of accuracy when interfacing a PicAXE microcontroller with a Devantech MD25 Motor Controller board. Public live testing of the system was conducted at the Kinetica Art Fair 2010 event in London and has since been picked up by websites such as Engadget.com and many others. Feedback on the system will be described, as well as the refinements made as a result of these test

Neural Sensor Fusion for Spatial Visualization on a Mobile Robot

An ARTMAP neural network is used to integrate visual information and ultrasonic sensory information on a B 14 mobile robot. Training samples for the neural network are acquired without human intervention. Sensory snapshots are retrospectively associated with the distance to the wall, provided by on~ board odomctry as the robot travels in a straight line. The goal is to produce a more accurate measure of distance than is provided by the raw sensors. The neural network effectively combines sensory sources both within and between modalities. The improved distance percept is used to produce occupancy grid visualizations of the robot's environment. The maps produced point to specific problems of raw sensory information processing and demonstrate the benefits of using a neural network system for sensor fusion.Office of Naval Research and Naval Research Laboratory (00014-96-1-0772, 00014-95-1-0409, 00014-95-0657