Gregory Hitz

Autonomous Inland Water Monitoring: Design and Application of a Surface Vessel

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, whereas on the other hand, the understanding of the temporal evolution of the cyanobacteria necessitates repeated sampling at the same location. Therefore, our ASV was designed to combine the ability to take measurements within a range of depths, with its custom-made winch, and accurate localization provided by the global positioning system (GPS), without the need for static installations. This article first describes the ASV conception, and then the 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 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. The high-resolution sampling of lakes will contribute to understand and predict the occurrence of harmful cyanobacterial blooms for a better management of water resources.

Autonomous construction of a roofed structure: Synthesizing planning and stigmergy on a mobile robot

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.

Multiresolution mapping and informative path planning for UAV-based terrain monitoring

Unmanned aerial vehicles (UAVs) can offer timely and cost-effective delivery of high-quality sensing data. However, 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.

State Estimation for Shore Monitoring Using an Autonomous Surface Vessel

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.

Online informative path planning for active classification using UAVs

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 information-theoretic 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.

Robotic technologies for solar-powered UAVs: Fully autonomous updraft-aware aerial sensing for multiday search-and-rescue missions

Large-scale aerial sensing missions can greatly benefit from the perpetual endurance capability provided by high-performance low-altitude solar-powered 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-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 26-hour search-and-rescue aerial sensing mockup 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 multi-day aerial sensing operations with small solar-powered UAVs.

Relaxing the planar assumption

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.