Abstract-We propose two contrasting approaches to the scalable distributed control of a swarm of self-assembling miniaturized robots, specifically the formation of chains of a desired length: (1) a deterministic controller in which robots communicate with each other in order to directly limit the size of each chain, and (2) a probabilistic controller where the average chain size is controlled by the probability a robot will choose to leave its chain. We demonstrate the feasibility of both approaches by implementing them on a real swarm of Alice robots. Using Webots, a realistic simulator for mobile robotics, and macroscopic models based on the Chemical Reaction Network (CRN) framework, we investigate the limitations of the deterministic controller and demonstrate the existence of optimal parameters for the probabilistic controller where exploration and exploitation are well balanced, thus favoring the formation of larger chains.

Abstract-The remarkable accessibility of modern flying robots makes them an attractive platform for environmental sensing. However, low cost and ease of use are currently incompatible with large payloads, severely limiting the choice of sensor and ultimately modality. This paper describes the design of a system for using a small infrared thermometer to estimate the surface temperature over an area that is large compared to the area measured by the sensor, by mounting it on a flying robot. We leverage a priori knowledge about the spatial statistics of the phenomena under measure in order to plan an informative sampling path, fusing observations by Gaussian process regression. Our approach is designed to be evaluated in an indoor testbed, in which a quadrotor, in cooperation with simulated static sensing nodes, estimates the spatial distribution of surface temperature over a controlled thermal gradient. We perform extensive systematic experimentation both in simulation and our real-world testbed environment, with our algorithm estimating surface temperature to an accuracy of up to 2.1 C over a 16 m 2 area ranging in value from 25-65 C.

Model-based synthesis of distributed controllers for multi-robot systems is commonly approached in either a top-down or bottom-up fashion. In this paper, we investigate the experimental challenges of both approaches, with a special emphasis on resource-constrained miniature robots. We make our comparison through a case study in which a group of 2-cm-sized mobile robots screen the environment for undesirable features, and destroy or neutralize them. First, we solve this problem using a top-down approach that relies on a graph-based representation of the system, allowing for direct optimization using numerical techniques (e.g., linear and non-linear convex optimization) under very unrealistic assumptions (e.g., infinite number of robots, perfect localization, global communication, etc.). We show how one can relax these assumptions in the context of resource-constrained robots, and explain the resulting impact on system performance. Second, we solve the same problem using a bottom-up approach, i.e., we build up computationally efficient and accurate models at multiple abstraction levels, and use them to optimize the robots' controller using evolutionary algorithms. Finally, we outline the differences between the top-down and bottom-up approaches, and experimentally compare their performance.

3~7 ~t)~~-t7). 1 visible C0 2 Gas Killing Trees at Mammoth Mountain, California S ince 1980, scientists have monitored geologic unrest in Long Valley Caldera and at adjacent Mammoth Mountain, California. After a persistent swarm of earthquakes beneath Mammoth Mountain in 1989, earth scientists discovered that large volumes of carbon dioxide (C0 2) gas were seeping from beneath this volcano. This gas is killing trees on the mountain and also can be a danger to people. The USGS continues to study the C0 2 emissions to help protect the public from this invisible potential hazard. Mammoth Mountain is a young volcano on the southwestern rim of Long Valley Caldera, a large volcanic depression in eastern California. The Long Valley area, well known for its superb skiing, hiking, and camping, has been volcanically active for about 4 million years. The most recent EXPLANATION Earthquakes magnitude 1.5 and higher (1989-95)-Fault iJ Locations of tree kill areas as of 1995 Mammoth Mountain, a young volcano in eastern California, rises above the floor of a large volcanic depression known as Long Valley Caldera. The scenic Long Valley area, popular with skiers, hikers, and campers, has been volcanically active for about 4 million years. High concentrations of C0 2 gas have been detected in the soil on Mammoth Mountain. This invisible gas, seeping from beneath the volcano, is killing trees on the sides of the mountain.