Swarm robotics is the study of developing and controlling large groups of robots. Collectives of robots possess advantages over single robots such as being robust to mission failures due to single-robot errors. Experimental research in swarm robotics is currently limited by swarm robotic technology. Current swarm robotic systems are either small groups of sophisticated robots or large groups of simple robots due to manufacturing overhead, functionality-cost dependencies, and their need to avoid collisions, amongst others. It is therefore useful to develop a swarm robotic system that is easy to manufacture, that utilises its sensors beyond standard usage, and that allows for physical interactions. In this work, I introduce a new type of low-friction locomotion and show its first implementation in the HoverBot system. The HoverBot system consists of an air-levitation and magnet table, and a HoverBot agent. HoverBots are levitating circuit boards which are equipped with an array of planar coils and a Hall-effect sensor. HoverBot uses its coils to pull itself towards magnetic anchors that are embedded into a levitation table. These robots consist of a Printed Circuit Board (PCB), surface mount components, and a battery. HoverBots are easily manufacturable, robots can be ordered populated; the assembly consists of plugging in a battery to a robot. I demonstrate how HoverBot's low-cost hardware can be used beyond its standard functionality. HoverBot's magnetic field readouts from its Hall-effect sensor can be associated with successful movement, robot rotation and collision measurands. I build a time series classifier based on these magnetic field readouts, I modify and apply signal processing techniques to enable the online classification of the time-variant magnetic field measurements on HoverBot's low-cost microcontroller. This method allows HoverBot to detect rotations, successful movements, and collisions by utilising readouts from its single Hall-effect sensor. I discuss how this classification method could be applied to other sensors and demonstrate how HoverBots can utilise their classifier to create an occupancy grid map. HoverBots use their multi-functional sensing capabilities to determine whether they moved successfully or collided with a static object to map their environment. HoverBots execute an "explore-and-return-to-nest" strategy to deal with their sensor and locomotion noise. Each robot is assigned to a nest (landmark); robots leave their nests, move n steps, return and share their observations. Over time, a group of four HoverBots collectively builds a probabilistic belief over its environment. In summary, I build manufacturable swarm robots that detect collisions through a time series classifier and map their environment by colliding with their surroundings. My work on swarm robotic technology pushes swarm robotics research towards studies on collision-dependent behaviours, a research niche that has been barely studied. Collision events occur more often in dense areas and/or large groups, circumstances that swarm robots experience. Large groups of robots with collision-dependent behaviours could become a research tool to help invent and test novel distributed algorithms, to understand the dependencies between local to global (emergent) behaviours and more generally the science of complex systems. Such studies could become tremendously useful for the execution of large-scale swarm applications such as the search and rescue of survivors after a natural disaster.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:763978 |
| Date | January 2018 |
| Creators | Nemitz, Markus P. |
| Contributors | Stokes, Adam ; Underwood, Ian |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/33160 |
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