Electroencephalography as implicit communication channel for proximal interaction between humans and robot swarms

Search and rescue, autonomous construction, and many other semi-autonomous multi-robot applications can benefit from proximal interactions between an operator and a swarm of robots. Most research on proximal interaction is based on explicit communication techniques such as gesture and speech. This study proposes a new implicit proximal communication technique to approach the problem of robot selection. We use electroencephalography (EEG) signals to select the robot at which the operator is looking. This is achieved using steady-state visually evoked potential (SSVEP), a repeatable neural response to a regularly blinking visual stimulus that varies predictively based on the blinking frequency. In our experiments, each robot was equipped with LEDs blinking at a different frequency, and the operator’s SSVEP neural response was extracted from the EEG signal to detect and select the robot without requiring any conscious action by the user. This study systematically investigates several parameters affecting the SSVEP neural response: blinking frequency of the LED, distance between the robot and the operator, and color of the LED. Based on these parameters, we study two signal processing approaches and critically analyze their performance on 10 subjects controlling a set of physical robots. Our results show that despite numerous artifacts, it is possible to achieve a recognition rate higher than 85% on some subjects, while the average over the ten subjects was 75%.

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

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

Bipedal walking and push recovery with a stepping strategy based on time-projection control

Salman Faraji, Auke Ijspeert, Seyed Hamed Razavi

In this paper, we present a simple control framework for online push recovery on biped robots with dynamic stepping properties. Owing to relatively heavy legs in our humanoid robot COMAN, we use a linear model called 3LP, which is composed of three pendulums to take swing and torso dynamics into account. Based on 3LP equations, we formulate discrete linear quadratic regulator (LQR) controllers and use a particular time-projection method to adjust footstep locations during the motion continuously. This process, which is based on pelvis and swing foot tracking errors, naturally considers swing dynamics and leads to leg-retraction properties. Suggested adjustments are added to the Cartesian 3LP gaits and converted into joint-space trajectories through inverse kinematics. Fixed and adaptive foot lift strategies are also used to ensure enough ground clearance in perturbed walking conditions. The proposed control architecture is robust, yet uses very simple state estimation and basic position tracking. We rely on series elastic actuators to absorb impacts while introducing simple laws to compensate for spring compressions. Extensive experiments on COMAN (real) and Atlas (simulated) robots demonstrate the functionality of different control blocks and prove the effectiveness of time-projection in extreme push recovery scenarios. We also show self-produced and emergent walking gaits when the robot is subject to continuous dragging forces. These gaits feature dynamic walking robustness with minimal reliance on the ankles and avoiding any active zero moment point (ZMP) control. The proposed architecture is therefore generic, computationally very fast and yet with no critical parameter to tune.

SAGE PUBLICATIONS LTD2019

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

Timothy Stirling

EPFL2011