Rehan O'Grady

From Fireflies to Fault-Tolerant Swarms of Robots

One of the essential benefits of swarm robotic systems is redundancy. In case one robot breaks down, another robot can take steps to repair the failed robot or take over the failed robots task. Although fault tolerance and robustness to individual failures have often been central arguments in favor of swarm robotic systems, few studies have been dedicated to the subject. In this paper, we take inspiration from the synchronized flashing behavior observed in some species of fireflies. We derive a completely decentralized algorithm to detect non-operational robots in a swarm robotic system. Each robot flashes by lighting up its on-board light-emitting diodes (LEDs), and neighboring robots are driven to flash in synchrony. Since robots that are suffering catastrophic failures do not flash periodically, they can be detected by operational robots. We explore the performance of the proposed algorithm both on a real-world swarm robotic system and in simulation. We show that failed robots are detected correctly and in a timely manner, and we show that a system composed of robots with simulated self-repair capabilities can survive relatively high failure rates.

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.

Fault detection in autonomous robots based on fault injection and learning

Abstract In this paper, we study a new approach to fault detection for autonomous robots. Our hypothesis is that hardware faults change the flow of sensory data and the actions performed by the control program. By detecting these changes, the presence of faults can be inferred. In order to test our hypothesis, we collect data from three different tasks performed by real robots. During a number of training runs, we record sensory data from the robots while they are operating normally and after a fault has been injected. We use back-propagation neural networks to synthesize fault detection components based on the data collected in the training runs. We evaluate the performance of the trained fault detectors in terms of number of false positives and time it takes to detect a fault. The results show that good fault detectors can be obtained. We extend the set of possible faults and go on to show that a single fault detector can be trained to detect several faults in both a robot’s sensors and actuators. We show that fault detectors can be synthesized that are robust to variations in the task, and we show how a fault detector can be trained to allow one robot to detect faults that occur in another robot.

SWARMORPH: Multirobot Morphogenesis Using Directional Self-Assembly

In this paper, we propose SWARMORPH: a distributed morphology generation mechanism for autonomous self-assembling mobile robots. Self-organized growth of global morphological structures emerges through the repeated application of local morphology extension rules. We present details of the directional self-assembly mechanism that provides control over the orientation of interrobot connections. We conduct real-world experiments to validate the low-level directional self-assembly mechanism and the growth of global morphologies. We demonstrate the scalability of the approach with large numbers of robots in simulation-based experiments.

Morphology control in a multirobot system

In this article, we propose a distributed control mechanism for a self-propelled, self-assembling robotic system that allows robots to form specific, connected morphologies. Global morphologies are grown using only local visual perception. Robots that are part of the connected entity indicate where new robots should attach to grow the local structure appropriately. We demonstrate the efficacy of the mechanism by letting groups of seven real robots self-assemble into four different morphologies: line, star, arrow, and rectangle.

Cooperative navigation in robotic swarms

We study cooperative navigation for robotic swarms in the context of a general event-servicing scenario. In the scenario, one or more events need to be serviced at specific locations by robots with the required skills. We focus on the question of how the swarm can inform its members about events, and guide robots to event locations. We propose a solution based on delay-tolerant wireless communications: by forwarding navigation information between them, robots cooperatively guide each other towards event locations. Such a collaborative approach leverages on the swarm’s intrinsic redundancy, distribution, and mobility. At the same time, the forwarding of navigation messages is the only form of cooperation that is required. This means that the robots are free in terms of their movement and location, and they can be involved in other tasks, unrelated to the navigation of the searching robot. This gives the system a high level of flexibility in terms of application scenarios, and a high degree of robustness with respect to robot failures or unexpected events. We study the algorithm in two different scenarios, both in simulation and on real robots. In the first scenario, a single searching robot needs to find a single target, while all other robots are involved in tasks of their own. In the second scenario, we study collective navigation: all robots of the swarm navigate back and forth between two targets, which is a typical scenario in swarm robotics. We show that in this case, the proposed algorithm gives rise to synergies in robot navigation, and it lets the swarm self-organize into a robust dynamic structure. The emergence of this structure improves navigation efficiency and lets the swarm find shortest paths.

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.

Automatic Synthesis of Fault Detection Modules for Mobile Robots

In this paper, we present a new approach for automatic synthesis of fault detection modules for autonomous mobile robots. The method relies on the fact that hardware faults typically change the flow of sensory perceptions received by the robot and the subsequent behavior of the control program. We collect data from three experiments with real robots. In each experiment, we record all sensory inputs from the robots while they are operating normally and after software-simulated faults have been injected. We use back- propagation neural networks to synthesize task-dependent fault detection modules. The performance of the modules is evaluated in terms of false positives and latency.

Autonomous Reconfiguration in a Self-assembling Multi-robot System

Self-assembling multi-robot systems can, in theory, overcome the physical limitations of individual robots by connecting to each other to form particular physical structures (morphologies) relevant to specific tasks. Here, we show for the first time how robots in a real-world multi-robot system can autonomously self-assemble into and reconfigure between arbitrary morphologies. We use a distributed control paradigm. The robots are individually autonomous and homogeneous - they all independently execute the same control program. Inter-robot communication is visual and strictly local. We demonstrate our technique on real robots.