Author name cluster

Gregory Hitz

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

7 papers

2 author rows

ICRA Conference 2018 Conference Paper

Multi-Agent Time-Based Decision-Making for the Search and Action Problem

Takahiro Miki
Marija Popovic
Abel Gawel
Gregory Hitz
Roland Siegwart

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.

Details

IROS Conference 2017 Conference Paper

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

Marija Popovic
Teresa A. Vidal-Calleja
Gregory Hitz
Inkyu Sa
Roland Siegwart
Juan I. Nieto 0001

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.

Details

ICRA Conference 2017 Conference Paper

Online informative path planning for active classification using UAVs

Marija Popovic
Gregory Hitz
Juan I. Nieto 0001
Inkyu Sa
Roland Siegwart
Enric Galceran

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.

Details

ICRA Conference 2014 Conference Paper

Fully autonomous focused exploration for robotic environmental monitoring

Gregory Hitz
Alkis Gotovos
François Pomerleau
Marie-Eve Garneau
Cédric Pradalier
Andreas Krause 0001
Roland Siegwart

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.

Details

IJCAI Conference 2013 Conference Paper

Active Learning for Level Set Estimation

Alkis Gotovos
Nathalie Casati
Gregory Hitz
Andreas Krause

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.

PDF Details DOI

IROS Conference 2013 Conference Paper

System integration and fin trajectory Design for a robotic sea-turtle

Cédric Siegenthaler
Cédric Pradalier
Fabian Günther
Gregory Hitz
Roland Siegwart

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.

Details

IROS Conference 2012 Conference Paper

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

Stefan Wismer
Gregory Hitz
Michael Bonani
Alexey Gribovskiy
Stéphane Magnenat

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.

Details