Unmanned aerial vehicles represent a new frontier in a wide range of monitoring and research applications. To fully leverage their potential, a key challenge is planning missions for efficient data acquisition in complex environments. To address this issue, this article introduces a general informative path planning framework for monitoring scenarios using an aerial robot, focusing on problems in which the value of sensor information is unevenly distributed in a target area and unknown a priori. The approach is capable of learning and focusing on regions of interest via adaptation to map either discrete or continuous variables on the terrain using variable-resolution data received from probabilistic sensors. During a mission, the terrain maps built online are used to plan information-rich trajectories in continuous 3-D space by optimizing initial solutions obtained by a coarse grid search. Extensive simulations show that our approach is more efficient than existing methods. We also demonstrate its real-time application on a photorealistic mapping scenario using a publicly available dataset and a proof of concept for an agricultural monitoring task.

Solar-powered aircraft promise significantly increased flight endurance over conventional aircraft. While this makes them promising candidates for large-scale aerial inspection missions, their structural fragility necessitates that adverse weather is avoided using appropriate path planning methods. This paper therefore presents MetPASS, the Meteorology-aware Path Planning and Analysis Software for Solar-powered UAVs. MetPASS is the first path planning framework in the literature that considers all aspects that influence the safety or performance of solar-powered flight: It avoids environmental risks (thunderstorms, rain, wind, wind gusts and humidity) and exploits advantageous regions (high sun radiation or tailwind). It also avoids system risks such as low battery state of charge and returns safe paths through cluttered terrain. MetPASS imports weather data from global meteorological models, propagates the aircraft state through an energetic system model, and then combines both into a cost function. A combination of dynamic programming techniques and an A*-search-algorithm with a custom heuristic is leveraged to plan globally optimal paths in station-keeping, point-to-point or multi-goal aerial inspection missions with coverage guarantees. A full software implementation including a GUI is provided. The planning methods are verified using three missions of ETH Zurich's AtlantikSolar UAV: An 81-hour continuous solar-powered station-keeping flight, a 4000km Atlantic crossing from Newfoundland to Portugal, and two multi-glacier aerial inspection missions above the Arctic Ocean performed near Greenland in summer 2017.

Large-scale aerial sensing missions can greatly benefit from the perpetual endurance capability provided by high-performance low-altitude solar-powered unmanned aerial vehicles (UAVs). However, today these UAVs suffer from small payload capacity, low energetic margins, and high operational complexity. To tackle these problems, this paper presents four individual technical contributions and integrates them into an existing solar-powered UAV system: First, a lightweight and power-efficient day/night-capable sensing system is discussed. Second, means to optimize the UAV platform to the specific payload and to thereby achieve sufficient energetic margins for day/night flight with payload are presented. Third, existing autonomous launch and landing functionality is extended for solar-powered UAVs. Fourth, as a main contribution an extended Kalman filter (EKF)-based autonomous thermal updraft tracking framework is developed. Its novelty is that it allows the end-to-end integration of the thermal-induced roll moment into the estimation process. It is assessed against unscented Kalman filter and particle filter methods in simulation and implemented on the aircraft's low-power autopilot. The complete system is verified during a 26h search-and-rescue aerial sensing mock-up mission that represents the first-ever fully autonomous perpetual endurance flight of a small solar-powered UAV with a day/night-capable sensing payload. It also represents the first time that solar-electric propulsion and autonomous thermal updraft tracking are combined in flight. In contrast to previous work that has focused on the energetic feasibility of perpetual flight, the individual technical contributions of this paper are considered core functionality to guarantee ease-of-use, effectivity, and reliability in future multiday aerial sensing operations with small solar-powered UAVs.

Many robotic applications, such as search-and-rescue, require multiple agents to search for and perform actions on targets. However, such missions present several challenges, including cooperative exploration, task selection and allocation, time limitations, and computational complexity. To address this, we propose a decentralized multi-agent decision-making framework for the search and action problem with time constraints. The main idea is to treat time as an allocated budget in a setting where each agent action incurs a time cost and yields a certain reward. Our approach leverages probabilistic reasoning to make near-optimal decisions leading to maximized reward. We evaluate our method in the search, pick, and place scenario of the Mohamed Bin Zayed International Robotics Challenge (MBZIRC), by using a probability density map and reward prediction function to assess actions. Extensive simulations show that our algorithm outperforms benchmark strategies, and we demonstrate system integration in a Gazebo-based environment, validating the framework's readiness for field application.

In this paper, we introduce an informative path planning (IPP) framework for active classification using unmanned aerial vehicles (UAVs). Our algorithm uses a combination of global viewpoint selection and evolutionary optimization to refine the planned trajectory in continuous 3D space while satisfying dynamic constraints. Our approach is evaluated on the application of weed detection for precision agriculture. We model the presence of weeds on farmland using an occupancy grid and generate adaptive plans according to informationtheoretic objectives, enabling the UAV to gather data efficiently. We validate our approach in simulation by comparing against existing methods, and study the effects of different planning strategies. Our results show that the proposed algorithm builds maps with over 50% lower entropy compared to traditional “lawnmower” coverage in the same amount of time. We demonstrate the planning scheme on a multirotor platform with different artificial farmland set-ups.

Unmanned aerial vehicles (UAVs) can offer timely and cost-effective delivery of high-quality sensing data. How- ever, deciding when and where to take measurements in complex environments remains an open challenge. To address this issue, we introduce a new multiresolution mapping approach for informative path planning in terrain monitoring using UAVs. Our strategy exploits the spatial correlation encoded in a Gaussian Process model as a prior for Bayesian data fusion with probabilistic sensors. This allows us to incorporate altitude-dependent sensor models for aerial imaging and perform constant-time measurement updates. The resulting maps are used to plan information-rich trajectories in continuous 3-D space through a combination of grid search and evolutionary optimization. We evaluate our framework on the application of agricultural biomass monitoring. Extensive simulations show that our planner performs better than existing methods, with mean error reductions of up to 45% compared to traditional "lawnmower" coverage. We demonstrate proof of concept using a multirotor to map color in different environments.

Autonomous mobile robots are increasingly employed to take measurements for environmental monitoring, but planning informative, measurement‐rich paths through large three‐dimensional environments is still challenging. Designing such paths, known as the informative path planning (IPP) problem, has been shown to be NP‐hard. Existing algorithms focus on providing guarantees on suboptimal solutions, but do not scale well to large problems. In this paper, we introduce a novel IPP algorithm that uses an evolutionary strategy to optimize a parameterized path in continuous space, which is subject to various constraints regarding path budgets and motion capabilities of an autonomous mobile robot. Moreover, we introduce a replanning scheme to adapt the planned paths according to the measurements taken in situ during data collection. When compared to two state‐of‐the‐art solutions, our method provides competitive results at significantly lower computation times and memory requirements. The proposed replanning scheme enables to build models with up to 25% lower uncertainty within an initially unknown area of interest. Besides presenting theoretical results, we tailored the proposed algorithms for data collection using an autonomous surface vessel for an ecological study, during which the method was validated through three field deployments on Lake Zurich, Switzerland. Spatiotemporal variations are shown over a period of three months and in an area of 350 m × 350 m × 13 m. Whereas our theoretical solution can be applied to multiple applications, our field results specifically highlight the effectiveness of our planner for monitoring toxic microorganisms in a pre‐alpine lake, and for identifying hot‐spots within their distribution.

Although many applications of small Autonomous Surface Vessels rely on two-dimensional state estimation, inspection tasks based on long-range sensors require more accurate attitude estimates. In the context of shoreline monitoring relying on a nodding laser scanner, we evaluate three different extended Kalman filter approaches with respect to an accurate ground truth in the range of millimeters. Our experimental setup allowed us to track the impact of sensors noise, including GPS non-Gaussian error, a phenomenon often underestimated. Extensive field experiments demonstrate that the use of a complementary filter in combination with a model-based extended Kalman filter performed best and reduced velocity errors by 73% compared to GPS. Finally, following our state estimation observations, we present a long-term shore monitoring result highlighting changes in the environment over a period of 6 months.

With the recent increase of toxic algal blooms in lakes, biologists are looking for new sampling methods to analyze their spatiotemporal dynamics. In this paper, we present an Autonomous Surface Vessel (ASV) capable of achieving longterm sampling missions covering up to now a transect plane (1.5 km wide and 20 m deep). The ASV is equipped with a probe which can be lowered via a winch to measure critical environmental parameters allowing more sophisticated prediction models of toxic algal blooms. So far, 21 km of autonomous sampling confirmed the system stability and produced enough basin-wide biological data to gain biologists’ interest. We intend to use this ASV as basis to further explore autonomous navigation on inland waterbodies.

Autonomous Surface Vessels (ASVs) are increasingly being proposed as tools to automate environmental data collection, bathymetric mapping and shoreline monitoring. For many applications it can be assumed that the boat operates on a 2D plane. However, with the involvement of exteroceptive sensors like cameras or laser rangefinders, knowing the 3D pose of the boat becomes critical. In this paper, we formulate three different algorithms based on 3D extended Kalman filter state estimation for ASV localization. We compare them using field testing results with ground truth measurements, and demonstrate that the best performance is achieved with a model-based solution in combination with a complementary filter for attitude estimation. Furthermore, we present a parameter identification methodology and show that it also yields accurate results when used with inexpensive sensors. Finally, we present a long-term series (i.e. over a full year) of shoreline monitoring data sets and discuss the need for map maintenance routines based on a variant of the Iterative Closest Point algorithm.

Robotic sensors are promising instruments for monitoring spatial phenomena. Oftentimes, rather than aiming to achieve low prediction error everywhere, one is interested in determining whether the phenomenon exhibits certain critical behavior. In this paper, we consider the problem of focusing autonomous sampling to determine whether and where the sensed spatial field exceeds a given threshold value. We introduce a receding horizon path planner, LSE-DP, which plans efficient paths for sensing in order to reduce our uncertainty specifically around the threshold value. We report fully autonomous field experiments with an Autonomous Surface Vessel (ASV) in an aquatic monitoring setting, which demonstrate the effectiveness of the proposed method. LSE-DP is able to reduce the uncertainty around the threshold value of interest to 68% when compared to non-adaptive methods.

This paper presents a novel underwater robot based on biological locomotion principle. A robotic platform imitating sea-turtle fin propulsion is described and tested. As fin locomotion is a novel and complex research area, basic control concepts are analyzed and implemented. Based on a simulation, a fin-trajectory morphing control strategy is developed in order to control the robots roll, pitch and yaw rates, thus allowing the robot to follow a given vector. Absolute position control or depth control, however, is not yet implemented. The paper concludes with the presentation of a working system that demonstrated motion capabilities in air as well as the first dive test in a swimming pool.

Short‐term changes in temporal and spatial distributions of the toxic cyanobacterium Planktothrix rubescens in Lake Zurich were investigated using high‐resolution data sets acquired with an autonomous surface vessel (ASV). Data profiles were recorded with a multi‐parameter probe while the ASV navigated along 1.5 km toward a waypoint located on the other side of the lake by using a global positioning system. We deployed the ASV seven times on five consecutive days during the stratification period (July 2011) to generate cross‐sectional views of temperature, light, oxygen, and phycoerythrin and chlorophyll fluorescence from surface to 18 m. The data were also used to compute daily photosynthetic rates. Data showed a daily reshaping of the P. rubescens distribution in the metalimnion on both horizontal and vertical axes, from patches to a shore‐to‐shore spreading. A thermocline depression observed after 16 h of sustained winds forced the accumulation of P. rubescens on the downwind shore. The compression of the metalimnion and its downward shift by 6 m within 24 h suggested the modulation of a longitudinal seiche following the wind event. This passive transport of the metalimnetic P. rubescens population resulted in a 90% light reduction, and a decrease of the averaged daily photosynthetic rate from +21 mmol O2 m−2 d−1 to −10 mmol O2 m−2 d−1. Negative photosynthetic rates were computed on 2 d out of 5 d, meaning that the transport of P. rubescens by seiches significantly affected the balance between oxygen production and utilization in Lake Zurich, especially because it is the dominant primary producer.

Many information gathering problems require determining the set of points, for which an unknown function takes value above or below some given threshold level. We formalize this task as a classification problem with sequential measurements, where the unknown function is modeled as a sample from a Gaussian process (GP). We propose LSE, an algorithm that guides both sampling and classification based on GP-derived confidence bounds, and provide theoretical guarantees about its sample complexity. Furthermore, we extend LSE and its theory to two more natural settings: (1) where the threshold level is implicitly defined as a percentage of the (unknown) maximum of the target function and (2) where samples are selected in batches. We evaluate the effectiveness of our proposed methods on two problems of practical interest, namely autonomous monitoring of algal populations in a lake environment and geolocating network latency.

We demonstrate a scenario in which a mobile robot, according to a plan, builds a structure that it can then enter. The robot interacts with the construction using local sensing. This synthesis of planning and stigmergy opens the way to new construction techniques using mobile robots.

This article presents a novel Autonomous Surface Vessel (ASV) that was designed and manufactured specifically for the monitoring of water resources, resources that are not only constantly drained, but also face the growing threat of mass proliferation (bloom) of noxious cyanobacteria. On one hand, the distribution of these blooms in a given water body requires a surveillance of biological data at high spatial resolution, on both vertical and horizontal axes. On the other hand, the understanding of the temporal evolution of the cyanobacteria necessitates repeated sampling at the same location. Our ASV was therefore designed to combine the ability to take measurements within a range of depths, with its custom-made winch, and the accuracy of GPS localization, without the need for static installations. This paper first describes the ASV conception and then, results of extended field tests on the waypoint navigation mode are discussed. Finally, the first results of a sampling campaign for monitoring algal blooms in Lake Zurich are presented. This work constitutes advances in the deployment of mobile measurement platforms for environmental monitoring in lacustrine environments. Furthermore, it investigates the application of a single ASV in order to capture both spatial and temporal dynamics of harmful cyanobacterial blooms in lakes. Combining surface mobility with depth measurements within a single robot allows fast deployments in remote location, which is cost-efficient for lake sampling. This reduces the need for fixed installations, which can be impossible in recreational areas for example. The highresolution sampling of lakes will contribute to understand and predict the occurrence of harmful cyanobacterial blooms for a better management of water resources.