A Graph-Based Formation Algorithm for Odor Plume Tracing

Odor plume tracing is a challenging robotics application, made difficult by the combination of the patchy characteristics of odor distribution and the slow response of the available sensors. This work proposes a graph-based formation control algorithm to coordinate a group of small robots equipped with odor sensors, with the goal of tracing an odor plume to its source. This approach makes it possible to organize the robots in arbitrary and evolving formation shapes, with the aim of improving tracking performance. The algorithm was evaluated in a high-fidelity submicroscopic simulator, using different formations and achieving quick convergence and negligible distance overhead in laminar wind flows.

Jorge Miguel Dias Almeida Rodrigues Soares, Alcherio Martinoli

Odor plume tracing is a challenging robotics application, made difficult by the combination of the patchy characteristics of odor distribution and the slow response of the available sensors. This work proposes a graph-based formation control algorithm to coordinate a group of small robots equipped with odor sensors, with the goal of tracing an odor plume to its source. This approach makes it possible to organize the robots in arbitrary and evolving formation shapes with the aim of improving tracing performance. The algorithm was evaluated in a high-fidelity submicroscopic simulator, using different formations and achieving quick convergence and negligible distance overhead in laminar wind flows.

Springer Japan2016

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

A Holistic Approach to Energy Harvesting for Indoor Robots

Florian Christopher Vaussard

Service robotics is a fast expanding market. Inside households, domestic robots can now accomplish numerous tasks, such as floor cleaning, surveillance, or remote presence. Their sales have considerably increased over the past years. Whereas 1.05 million domestic service robots were reportedly sold in 2009, at least 2.7 million units were sold in 2013. Consequently, this growth gives rise to an increase of the energy needs to power such a large and growing fleet of robots. However, the unique properties of mobile robots open some new fields of research. We must find technologies that are suitable for decreasing the energy requirements and thus further advance towards a sustainable development. This thesis tackles two fundamental goals based on a holistic approach of the global problem. The first goal is to reduce the energy needs by identifying key technologies in making energy-efficient robots. The second goal is to leverage innovative indoor energy sources to increase the ratio of renewable energies scavenged from the environment. To achieve our first goal, new energy-wise metrics are applied to real robotic hardware. This gives us the means to assess the impact of some technologies on the overall energy balance. First, we analysed seven robotic vacuum cleaners from a representative sample of the market that encompasses a wide variety of technologies. Simultaneous Localisation and Mapping (SLAM) was identified as a key technology to reduce energy needs when carrying out such tasks. Even if the instantaneous power is slightly increased, the completion time of the task is greatly reduced. We also analysed the needed sensors to achieve SLAM, as they are largely diversified. This work tested three sensors using three different technologies. We identified several important metrics. As of our second goal, potential energy sources are compared to the needs of an indoor robot. The sunshine coming through a building's apertures is identified as a promising source of renewable power. Numerical simulations showed how a mobile robot is mandatory to take full advantage of this previously unseen situation, as well as the influence of the geometric parameters on the yearly energy income under ideal sunny conditions. When considering a real system, the major difficulty to overcome is the tracking of the sunbeam along the day. The proposed algorithm uses a hybrid method. A high-level cognitive approach is responsible for the initial placement. Following realignments during the day are performed by a low-level reactive behaviour. A solar harvesting module was developed for our research robot. The tests conducted inside a controlled environment demonstrate the feasibility of this concept and the good performances of the aforementioned algorithm. Based on a realistic scenario and weather conditions, we computed that between 1 and 14 days of recharge could be necessary for a single cleaning task. In the future, our innovative technology could greatly lower the energy needs of service robots. However, it is not completely possible to abandon the recharge station due to occasional bad weather. The acceptance of this technology inside the user's home ecosystem remains to be studied.