SwisTrack: A Tracking Tool for Multi-Unit Robotic and Biological Systems

Tracking of miniature robotic platforms involves major challenges in image recognition and data association. We present our 3-year effort into developing the platform-independent, easy-to-use, and robust tracking software SwisTrack, which is tailored to research in swarm robotics and behavioral biology. We demonstrate the software and algorithms abilities using two case studies, tracking of a swarm of cockroaches, and a swarm-robotic inspection task, while outlining hard problems in tracking and data-association of marker-less objects. Tracking accuracy of a moving robot with respect to camera noise and the calibration model are calculated experimentally. Its open, platform-independent architecture, and easy-to-use interfaces (MatlabTM, JavaTM, and C++), allowing for (distributed) post-processing of trajectory data online, make the software highly adaptive to particular research projects without changes to the source code. SwisTrack is publicly available on Sourceforge.net under the OSI Adaptive License and contributions from the robotics and biology community are encouraged.

Energy-Efficient Indoor Search by Swarms of Flying Robots without Global Information

Timothy Stirling

Swarms of robots can quickly search large environments through parallelisation, are robust due to redundancy, and can simplify complex tasks like navigation compared to a single robot. Flying swarms can rapidly cover rough terrain and have elevated sensing, aiding tasks such as search or surveillance. However, flying robots have severe limitations in sensing, processing and energy, which raise three open challenges to realise indoor aerial swarms. The first is goal-directed autonomous flight, which normally requires absolute positioning from GPS or external tracking systems, unavailable in unknown indoor environments. On-board sensing approaches using illumination-dependent cameras or laser scanners that can fail in homogeneous environments require processing on ground stations using long-range communication due to high computational demands. Although optic-flow based approaches can be less computationally demanding, they require significant illumination and contrast, and need additional sensors for goal-directed flight. The second challenge is cooperative search and navigation, which typically requires either absolute positioning, localisation using a priori environment maps or the computationally expensive online creation of maps. Thirdly, collective systems frequently suffer inefficiencies due to interference and unnecessary locomotion, resulting in wasted energy. Prior research to improve coordination usually required maps, centralised processing or GPS and considered only ground robots, but flying robots have significantly different energy characteristics and demands. Alternative strategies are therefore required that do not rely on absolute positioning or localisation, environment maps, long-range communication or centralised processing, referred to as Global Information. This thesis introduces a holistic approach to achieve autonomous flight and navigation by embedding a network of robots in the environment with local sensing, processing and communication. A new methodology is presented for goal-directed autonomous flight using relative positioning in reference to nearby static robots. This allows flying robots to directly perceive their motion and fly across the network through the environment. Comprehensive validation tests demonstrated a quadrotor successfully exploring a confined indoor environment. Efficient navigation is achieved using communication messages propagated across the robot network to retrieve the shortest paths. Search is accomplished with a systematic strategy to dynamically redeploy the robot network to unexplored areas. Extensive realistic simulation analysis showed that swarms of 20–30 robots could search a variety of large indoor environments. To mitigate the short flight endurance, different swarm deployment strategies are systematically compared that reduce robot interference and decrease unnecessary locomotion by exploiting gained environment information. The strategies either control the dispatching rate, robot activation or exploit the network to recruit robots when and where necessary. These comparisons facilitate the selection of the best strategy according to the robot capabilities and application requirements. In summary, this thesis resolves three major challenges in aerial swarm robotics and contributes new methodologies to significantly progress the realisation of energy-efficient indoor search by swarms of flying robots. This research should enable many applications such as locating toxic gas leaks, crowd monitoring, and searching for suspicious objects or injured victims.

EPFL2011

Evolution of cooperation in artificial ants

Markus Waibel

The evolution of cooperation is a fundamental and enduring puzzle in biology and the social sciences. Hundreds of theoretical models have been proposed, but empirical research has been hindered by the generation time of social organisms and by the difficulties of quantifying costs and benefits of cooperation. The significant increase in computational power in the last decade has made artificial evolution of simple social robots a promising alternative. This thesis is concerned with the artificial evolution of groups of cooperating robots. It argues that artificial evolution of robotic agents is a powerful tool to address open questions in evolutionary biology, and shows how insights gained from the study of artificial and biological multi-agent systems can be mutually beneficial for both biology and robotics. The work presented in this thesis contributes to biology by showing how artificial evolution can be used to quantify key factors in the evolution of cooperation in biological systems and by providing an empirical test of a central part of biological theory. In addition, it reveals the importance of the genetic architecture for the evolution of efficient cooperation in groups of organisms. The work also contributes to robotics by identifying three different classes of multi-robot tasks depending on the amount of cooperation required between team members and by suggesting guidelines for the evolution of efficient robot teams. Furthermore it shows how simulations can be used to successfully evolve controllers for physical robot teams.

EPFL2007

Tales from a Robotic World. How Intelligent Machines Will Shape Our Future

Dario Floreano, Nicola Nosengo

Stories from the future of intelligent machines—from rescue drones to robot spouses—and accounts of cutting-edge research that could make it all possible. Tech prognosticators promised us robots—autonomous humanoids that could carry out any number of tasks. Instead, we have robot vacuum cleaners. But, as Dario Floreano and Nicola Nosengo report, advances in robotics could bring those rosy predictions closer to reality. A new generation of robots, directly inspired by the intelligence and bodies of living organisms, will be able not only to process data but to interact physically with humans and the environment. In this book, Floreano, a roboticist, and Nosengo, a science writer, bring us tales from the future of intelligent machines—from rescue drones to robot spouses—along with accounts of the cutting-edge research that could make it all possible. These stories from the not-so-distant future show us robots that can be used for mitigating effects of climate change, providing healthcare, working with humans on the factory floor, and more. Floreano and Nosengo tell us how an application of swarm robotics could protect Venice from flooding, how drones could reduce traffic on the congested streets of mega-cities like Hong Kong, and how a “long-term relationship model” robot could supply sex, love, and companionship. After each fictional scenario, they explain the technologies that underlie it, describing advances in such areas as soft robotics, swarm robotics, aerial and mobile robotics, humanoid robots, wearable robots, and even biohybrid robots based on living cells. Robotics technology is no silver bullet for all the world's problems—but it can help us tackle some of the most pressing challenges we face.

MIT Press2022

Energy-Efficient Indoor Search by Swarms of Flying Robots without Global Information

Timothy Stirling

EPFL2011