Reynolds flocking in reality with fixed-wing robots: communication range vs. maximum turning rate

The success of swarm behaviors often depends on the range at which robots can communicate and the speed at which they change their behavior. Challenges arise when the communication range is too small with respect to the dynamics of the robot, preventing interactions from lasting long enough to achieve coherent swarming. To alleviate this dependency, most swarm experiments done in laboratory environments rely on communication hardware that is relatively long range and wheeled robotic platforms that have omnidirectional motion. Instead, we focus on deploying a swarm of small fixed-wing flying robots. Such platforms have limited payload, resulting in the use of short-range communication hardware. Furthermore, they are required to maintain forward motion to avoid stalling and typically adopt low turn rates because of physical or energy constraints. The tradeoff between communication range and flight dynamics is exhaustively studied in simulation in the scope of Reynolds flocking and demonstrated with up to 10 robots in outdoor experiments.

Evolutionary Synthesis of Communication-Based Aerial Swarms

Sabine Hauert

Aerial swarms have the potential to search for forest fires, chemical plumes or victims and serve as communication and sensor networks in the sky. Flying robots are interesting for such applications because they are fast, can easily overcome difficult terrain and provide line-of-sight communication or aerial perspectives. However, swarms of flying robots have so far only been demonstrated in simulation or in few examples in reality. Current simulators typically rely on unrealistic assumptions concerning robot sensing capabilities and motion. To bridge this reality gap we propose to address two key challenges. The first challenge consists in discovering swarm controllers that do not use position information. Swarm controllers in the literature rely on global or relative position information which can be obtained using GPS, cameras or range and bearing sensors. However, position requirements typically entail robots that are complex, heavy and expensive or that rely on specific environmental conditions to function. Lifting the need for position could instead lead to swarm controllers that can be deployed in a variety of environments and using very simple robots. The second challenge addressed consists in developing swarm controllers that can accommodate motion constraints of flying robots. In particular, we consider fixed-wing robots that can not stop or turn on the spot like ground robots or rotor crafts. Instead they must fly at relatively high speeds to avoid stalling. Therefore, making the robots advance slowly in average can only be done by having them turn. To address these challenges, we consider robot controllers that continuously react to wireless communication with neighboring robots or people by changing their turn rate (communication-based behaviors). Using communication as a sensor for flying robots is interesting because most robots are equipped with off-the-shelf radio modules that are low-cost, light-weight and relatively long-range. The design of robot controllers that can lead to desired swarm behaviors is done following a systematic approach. First, artificial evolution is used to automatically discover simple and unthought-of controllers for swarms of flying robots. Evolved controllers are then reverse engineered to obtain basic behaviors that can be modeled and used in a variety of swarm applications. In order to discover a range of basic behaviors, we consider a challenging swarm application that requires robots to direct themselves in their environment, move in groups, cover an area, and maintain a communication relay. Discovered behaviors are then extended to scenarios with wind. Overall, this thesis presents the evolutionary synthesis of communication-based behaviors for swarms of fixed-wing flying robots.

EPFL2010

Coordination schemes for distributed boundary coverage with a swarm of miniature robots

Nicolaus Correll

We provide a comparison of a series of original coordination mechanisms for the distributed boundary coverage problem with a swarm of miniature robots. Our analysis is based on real robot experimentation and models at different levels of abstraction. Distributed boundary coverage is an instance of the distributed coverage problem and has applications such as inspection of structures, de-mining, cleaning, and painting. Coverage is a particularly good example for the benefits of a multi-robot approach due to the potential for parallel task execution and additional robustness out of redundancy. The constraints imposed by a potential application, the autonomous inspection of a jet turbine engine, were our motivation for the algorithms considered in this thesis. Thus, there is particular emphasis on how algorithms perform under the influence of sensor and actuator noise, limited computational and communication capabilities, as well as on the policies about how to cope with such problems. The algorithms developed in this dissertation can be classified into reactive and deliberative algorithms, as well as non-collaborative and collaborative algorithms. The performance of these algorithms ranges from very low to very high, corresponding to highly redundant coverage to near-optimal partitioning of the environments, respectively. At the same time, requirements and assumptions on the robotic platform and the environment (from no communication to global communication, and from no localization to global localization) are incrementally raised. All the algorithms are robust to sensor and actuator noise and gracefully decay to the performance of a randomized algorithm as a function of an increased noise level and/or additional hardware constraints. Although the deliberative algorithms are fully deterministic, the actual performance is probabilistic due to inevitable sensor and actuator noise. For this reason, probabilistic models are used for predicting time to complete coverage and take into account sensor and actuator noise calibrated by using real hardware. For reactive systems with limited memory, the performance is captured using a compact representation based on rate equations that track the expected number of robots in a certain state. As the number of states explode for the deliberative algorithms that require a substantial use of memory, this approach becomes less tractable with the amount of deliberation performed, and we use Discrete Event System (DES) simulation in these cases. Our contribution to the domain of multi-robot systems is three-fold. First, we provide a methodology for system identification and optimal control of a robot swarm using probabilistic models. Second, we develop a series of algorithms for distributed coverage by a team of miniature robots that gracefully decay from a near-optimal performance to the performance of a randomized approach under the influence of sensor and actuator noise. Third, we design an implement a miniature inspection platform based on the miniature robot Alice with ZigBee ready communication capabilities and color vision on a foot-print smaller than 2 × 2 × 3 cm3.

EPFL2007

Evolutionary Synthesis of Communication-Based Aerial Swarms

Sabine Hauert

EPFL2010

Coordination schemes for distributed boundary coverage with a swarm of miniature robots

Nicolaus Correll

EPFL2007