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We address the problem of designing a distributed algorithm for two robots that sketches the boundary of an unknown shape. Critically, we assume a certain amount of delay in how quickly our robots can react to external feedback. In particular, when a robot moves, it commits to move along path of length at least 𝜆, or turn an amount of radians at least 𝜆 for some positive 𝜆 ≤ 1∕26, that is normalized based on a unit diameter shape. Then, our algorithm outputs a polygon that is √ an 𝜖-sketch, for 𝜖 =8 𝜆, in the sense that every point on the shape boundary is within distance 𝜖 of the output polygon. Moreover, our costs are asymptotically optimal in two key criteria for the robots: total distance traveled and total amount of rotation. Additionally, we implement our algorithm, and illustrate its output on some specific shapes. 

This article addresses the quadrotors’ safety-critical landing control problem with external uncertainties and collision avoidance. A geometrically robust hierarchical control strategy is proposed for an underactuated quadrotor, which consists of a slow outer loop controlling the position and a fast inner loop regulating the attitude. First, an estimation error quantified (EEQ) observer is developed to identify and compensate for the target’s linear acceleration and the translational disturbances, whose estimation error has a nonnegative upper bound. Furthermore, an outer-loop controller is designed by embedding the EEQ observer and control barrier functions (CBFs), in which the negative effects of external uncertainties, collision avoidance, and input saturation are thoroughly considered and effectively attenuated. For the inner-loop subsystem, a geometric controller with a robust integral of the sign of the error (RISE) control structure is developed to achieve disturbances rejection and asymptotic attitude tracking. Based on Lyapunov techniques and the theory of cascade systems, it is rigorously proven that the closed-loop system is uniformly ultimately bounded. Finally, the effectiveness of the proposed control strategy is demonstrated through numerical simulations and hardware experiments. 

We report CO2 emission rates and plume δ13C during the July 2023 eruption at Litli Hrútur in the Fagradalsfjall region of the Reykjanes Peninsula. The CO2 emission rates were measured by UAV utilizing a new method of data extrapolation that enables obtaining rapid flux results of dynamic eruption plumes. The δ13C values are consistent with degassing-induced isotopic fractionation of the magma during and after the eruption. Our results show that rapid, real-time CO2 flux measurements coupled with isotopic values of samples collected at the same time provide key insights into the dynamics of volcanic eruptions and have the potential of forecasting the termination of activity. 

Abstract. We report in-plume carbon dioxide (CO2) concentrations and carbon isotope ratios during the 2021 eruption of Tajogaite volcano, island of La Palma, Spain. CO2 measurements inform our understanding of volcanic contributions to the global climate carbon cycle and the role of CO2 in eruptions. Traditional ground-based methods of CO2 collection are difficult and dangerous, and as a result only about 5 % of volcanoes have been directly surveyed. We demonstrate that unpiloted aerial system (UAS) surveys allow for fast and relatively safe measurements. Using CO2 concentration profiles we estimate the total flux during several measurements in November 2021 to be 1.76±0.20×103 to 2.23±0.26×104 t d−1. Carbon isotope ratios of plume CO2 indicate a deep magmatic source, consistent with the intensity of the eruption. Our work demonstrates the feasibility of UASs for CO2 surveys during active volcanic eruptions, particularly for deriving rapid emission estimates. 

We address the problem of designing a distributed algorithm for two robots that sketches the boundary of an unknown shape. Critically, we assume a certain amount of delay in how quickly our robots can react to external feedback. In particular, when a robot moves, it commits to move along path of length at least λ, or turn an amount of radians at least λ for some positive λ ≤ (1/2)^6, that is normalized based on a unit diameter shape. Then, our algorithm outputs a polygon that is an ϵ-sketch, for ϵ = 8λ^(1/2), in the sense that every point on the shape boundary is within distance ϵ of the output polygon. Moreover, our costs are asymptotically optimal in two key criteria for the robots: total distance travelled and total amount of rotation. Additionally, we implement our algorithm, and illustrate its output on some specific shapes. 

With the rapid development of technology and the proliferation of uncrewed aerial systems (UAS), there is an immediate need for security solutions. Toward this end, we propose the use of a multi-robot system for autonomous and cooperative counter-UAS missions. In this paper, we present the design of the hardware and software components of different complementary robotic platforms: a mobile uncrewed ground vehicle (UGV) equipped with a LiDAR sensor, an uncrewed aerial vehicle (UAV) with a gimbal-mounted stereo camera for air-to-air inspections, and a UAV with a capture mechanism equipped with radars and camera. Our proposed system features 1) scalability to larger areas due to the distributed approach and online processing, 2) long-term cooperative missions, and 3) complementary multimodal perception for the detection of multirotor UAVs. In field experiments, we demonstrate the integration of all subsystems in accomplishing a counter-UAS task within an unstructured environment. The obtained results confirm the promising direction of using multi-robot and multi-modal systems for C-UAS. 

We present methods for autonomous collaborative surveying of volcanic CO 2 emissions using aerial robots. CO 2 is a useful predictor of volcanic eruptions and an influential greenhouse gas. However, current CO 2 mapping methods are hazardous and inefficient, as a result, only a small fraction of CO 2 emitting volcanoes have been surveyed. We develop algorithms and a platform to measure volcanic CO 2 emissions. The Dragonfly Unpiloted Aerial Vehicle (UAV) platform is capable of long-duration CO 2 collection flights in harsh environments. We implement two survey algorithms on teams of Dragonfly robots and demonstrate that they effectively map gas emissions and locate the highest gas concentrations. Our experiments culminate in a successful field test of collaborative rasterization and gradient descent algorithms in a challenging real-world environment at the edge of the Valles Caldera supervolcano. Both algorithms treat multiple flocking UAVs as a distributed flexible instrument. Simultaneous sensing in multiple UAVs gives scientists greater confidence in estimates of gas concentrations and the locations of sources of those emissions. These methods are also applicable to a range of other airborne concentration mapping tasks, such as pipeline leak detection and contaminant localization.