Nithin Mathews

ARGoS: A modular, multi-engine simulator for heterogeneous swarm robotics

We present ARGoS, a novel open source multi-robot simulator. The main design focus of ARGoS is the real-time simulation of large heterogeneous swarms of robots. Existing robot simulators obtain scalability by imposing limitations on their extensibility and on the accuracy of the robot models. By contrast, in ARGoS we pursue a deeply modular approach that allows the user both to easily add custom features and to allocate computational resources where needed by the experiment. A unique feature of ARGoS is the possibility to use multiple physics engines of different types and to assign them to different parts of the environment. Robots can migrate from one engine to another transparently. This feature enables entirely novel classes of optimizations to improve scalability and paves the way for a new approach to parallelism in robotics simulation. Results show that ARGoS can simulate about 10,000 simple wheeled robots 40% faster than real-time.

Flocking in stationary and non-stationary environments: a novel communication strategy for heading alignment

We propose a novel communication strategy inspired by explicit signaling mechanisms seen in vertebrates, in order to improve performance of self-organized flocking for a swarm of mobile robots. The communication strategy is used to make the robots match each others headings. The task of the robots is to coordinately move towards a common goal direction, which might stay fixed or change over time. We perform simulation-based experiments in which we evaluate the accuracy of flocking with respect to a given goal direction. In our settings, only some of the robots are informed about the goal direction. Experiments are conducted in stationary and non-stationary environments. In the stationary environment, the goal direction and the informed robots do not change during the experiment. In the non-stationary environment, the goal direction and the informed robots are changed over time. In both environments, the proposed strategy scales well with respect to the swarm size and is robust with respect to noise.

Spatially targeted communication and self-assembly

We introduce spatially targeted communication - a communication method for multirobot systems. This method allows an individual message sending robot to isolate selected message recipient robots based on their spatial location. The recipient robots can then be sent information targeted solely at them, even if the sending robot uses a broadcast communication modality. We demonstrate spatially targeted communication using a heterogeneous multirobot system composed of flying robots and ground-based self-assembling robots. Flying robots use their privileged view of the environment to determine and communicate information to groups of ground-based robots on what morphologies to form to carry out upcoming tasks.

Cooperation in a heterogeneous robot swarm through spatially targeted communication

We consider a heterogeneous swarm robotic system composed of wheeled and aerial robots called foot-bots and eye-bots, respectively. The foot-bots are able to physically connect to one another autonomously and thus form collective robotic entities. Eye-bots have a privileged overview of the environment since they can fly and attach to metal ceilings. In this paper, we show how the heterogeneous swarm can benefit from cooperation. By using so-called spatially targeted communication, the eye-bot is able to communicate with selected groups of foot-bots and instruct them on how to overcome obstacles in their path by forming morphologies appropriate to the obstacle encountered. We conduct experiments in simulation to quantify separately the benefits of cooperation and of spatially targeted communication.

Opinion dynamics for decentralized decision-making in a robot swarm

In this paper, we study how an opinion dynamics model can be the core of a collective decision-making mechanism for swarm robotics. Our main result is that when opinions represent action choices, the opinion associated with the action that is the fastest to execute spreads in the population. Moreover, the spread of the best choice happens even when only a minority is initially advocating for it. The key elements involved in this process are consensus building and positive feedback. A foraging task that involves collective transport is used to illustrate the potential of the proposed approach.

Enhanced directional self-assembly based on active recruitment and guidance

We introduce enhanced directional self-assembly (EDSA) - a novel mechanism for morphology growth through the creation of directed connections in a self-assembling multirobot system. In our approach, a robot inviting a physical connection actively recruits the best located neighboring robot and guides the recruit to the location on its chassis where the connection is required. The proposed mechanism relies on local, high-speed communication between connection inviting robots and their recruits. Communication is based on a hybrid technology that combines radio and infrared to provide local relative positioning information when messages are transmitted between adjacent robots. Experiments with real robotic hardware show that EDSA is precise (misalignment of only 1.2° on average), robust (100% success rate for the experiments in this study) and fast (16.1 seconds on average from a distance of 80 cm). We show how the speed and precision of the new approach enable adaptive recruitment and connection in dynamic environments, a high degree of parallelism, and growth of a moving morphology.

Investigating the effect of increasing robot group sizes on the human psychophysiological state in the context of human–swarm interaction

We study the psychophysiological state of humans when exposed to robot groups of varying sizes. In our experiments, 24 participants are exposed sequentially to groups of robots made up of 1, 3 and 24 robots. We measure both objective physiological metrics (skin conductance level and heart rate), and subjective self-reported metrics (from a psychological questionnaire). These measures allow us to analyse the psychophysiological state (stress, anxiety, happiness) of our participants. Our results show that the number of robots to which a human is exposed has a significant impact on the psychophysiological state of the human and that higher numbers of robots provoke a stronger response.

Spatially targeted communication in decentralized multirobot systems

Spatially targeted communication (STC) allows a message sender to choose message recipients based on their location in space. Currently, STC in multirobot systems is limited to centralized systems. In this paper, we propose a novel communication protocol that enables STC in decentralized multirobot systems. The proposed protocol dispenses with the many aspects that underpin previous approaches, including external tracking infrastructure, a priori knowledge, global information, dedicated communication devices or unique robot IDs. We show how off-the-shelf hardware components such as cameras and LEDs can be used to establish ad-hoc STC links between robots. We present a Markov chain model for each of the two constituent parts of our proposed protocol and we show, using both model-based analysis and experimentation, that the proposed protocol is highly scalable. We also present the results of extensive experiments carried out on an autonomous, heterogeneous multirobot system composed of one aerial robot and numerous ground-based robots. Finally, two real world application scenarios are presented in which we show how spatial coordination can be achieved in a decentralized multirobot system through STC.

Mergeable nervous systems for robots

Robots have the potential to display a higher degree of lifetime morphological adaptation than natural organisms. By adopting a modular approach, robots with different capabilities, shapes, and sizes could, in theory, construct and reconfigure themselves as required. However, current modular robots have only been able to display a limited range of hardwired behaviors because they rely solely on distributed control. Here, we present robots whose bodies and control systems can merge to form entirely new robots that retain full sensorimotor control. Our control paradigm enables robots to exhibit properties that go beyond those of any existing machine or of any biological organism: the robots we present can merge to form larger bodies with a single centralized controller, split into separate bodies with independent controllers, and self-heal by removing or replacing malfunctioning body parts. This work takes us closer to robots that can autonomously change their size, form and function.Robots that can self-assemble into different morphologies are desired to perform tasks that require different physical capabilities. Mathews et al. design robots whose bodies and control systems can merge and split to form new robots that retain full sensorimotor control and act as a single entity.

Publisher Correction: Mergeable nervous systems for robots

The original version of this Article contained an error in the author contributions section, whereby credit for design of the experiments was not attributed to N.M. This error has now been corrected in both the PDF and HTML versions of the Article.Robots have the potential to display a higher degree of lifetime morphological adaptation than natural organisms. By adopting a modular approach, robots with different capabilities, shapes, and sizes could, in theory, construct and reconfigure themselves as required. However, current modular robots have only been able to display a limited range of hardwired behaviors because they rely solely on distributed control. Here, we present robots whose bodies and control systems can merge to form entirely new robots that retain full sensorimotor control. Our control paradigm enables robots to exhibit properties that go beyond those of any existing machine or of any biological organism: the robots we present can merge to form larger bodies with a single centralized controller, split into separate bodies with independent controllers, and self-heal by removing or replacing malfunctioning body parts. This work takes us closer to robots that can autonomously change their size, form and function.