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Jérôme Guzzi

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14 papers
2 author rows

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14

IROS Conference 2024 Conference Paper

Resource-Aware Collaborative Monte Carlo Localization with Distribution Compression

  • Nicky Zimmerman
  • Alessandro Giusti
  • Jérôme Guzzi

Global localization is essential in enabling robot autonomy, and collaborative localization is key for multi-robot systems, allowing for more efficient planning and execution of tasks. In this paper, we address the task of collaborative global localization under computational and communication constraints. We propose a method which reduces the amount of information exchanged and the computational cost. We also analyze, implement and open-source seminal approaches, which we believe to be a valuable contribution to the community. We exploit techniques for distribution compression in near-linear time, with error guarantees. We evaluate our approach and the implemented baselines on multiple challenging scenarios, simulated and real-world. Our approach can run online on an onboard computer. We release an open-source C++/ROS2 implementation of our approach, as well as the baselines. 1

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ICRA Conference 2020 Conference Paper

Intuitive 3D Control of a Quadrotor in User Proximity with Pointing Gestures

  • Boris Gromov
  • Jérôme Guzzi
  • Luca Maria Gambardella
  • Alessandro Giusti

We present an approach for controlling the position of a quadrotor in 3D space using pointing gestures; the task is difficult because it is in general ambiguous to infer where, along the pointing ray, the robot should go. We propose and validate a pragmatic solution based on a push button acting as a simple additional input device which switches between different virtual workspace surfaces. Results of a study involving ten subjects show that the approach performs well on a challenging 3D piloting task, where it compares favorably with joystick control.

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ICRA Conference 2019 Conference Paper

On the Impact of Uncertainty for Path Planning

  • Jérôme Guzzi
  • Ricardo Omar Chávez García
  • Luca Maria Gambardella
  • Alessandro Giusti

We consider the problem of planning paths on graphs with some edges whose traversability is uncertain; for each uncertain edge, we are given a probability of being traversable (e. g. , by a learned classifier). We categorize different interpretations of the problem that are meaningful for mobile robots navigating partially-known environments, each of which yields a different formalization; we then focus on the case in which the true traversability of an edge is revealed only when the agent visits one of its endpoints (Canadian Traveller Problem). In this context, we design a large simulation campaign on synthetic and real-world maps to study the impact of two different factors: the planning strategy, and the amount of uncertainty (which could depend on the quality of the classifier producing traversability estimates).

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AAAI Conference 2019 System Paper

Realtime Generation of Audible Textures Inspired by a Video Stream

  • Simone Mellace
  • Jérôme Guzzi
  • Alessandro Giusti
  • Luca M. Gambardella

We showcase a model to generate a soundscape from a camera stream in real time. The approach relies on a training video with an associated meaningful audio track; a granular synthesizer generates a novel sound by randomly sampling and mixing audio data from such video, favoring timestamps whose frame is similar to the current camera frame; the semantic similarity between frames is computed by a pretrained neural network. The demo is interactive: a user points a mobile phone to different objects and hears how the generated sound changes.

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ICRA Conference 2019 Conference Paper

Vision-based Control of a Quadrotor in User Proximity: Mediated vs End-to-End Learning Approaches

  • Dario Mantegazza
  • Jérôme Guzzi
  • Luca Maria Gambardella
  • Alessandro Giusti

We consider the task of controlling a quadrotor to hover in front of a freely moving user, using input data from an onboard camera. On this specific task we compare two widespread learning paradigms: a mediated approach, which learns a high-level state from the input and then uses it for deriving control signals; and an end-to-end approach, which skips high-level state estimation altogether. We show that despite their fundamental difference, both approaches yield equivalent performance on this task. We finally qualitatively analyze the behavior of a quadrotor implementing such approaches.

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AAAI Conference 2018 System Paper

Learning an Image-based Obstacle Detector With Automatic Acquisition of Training Data

  • Stefano Toniolo
  • Jérôme Guzzi
  • Luca Gambardella
  • Alessandro Giusti

We detect and localize obstacles in front of a mobile robot by means of a deep neural network that maps images acquired from a forward-looking camera to the outputs of five proximity sensors. The robot autonomously acquires training data in multiple environments; once trained, the network can detect obstacles and their position also in unseen scenarios, and can be used on different robots, not equipped with proximity sensors. We demonstrate both the training and deployment phases on a small modified Thymio robot.

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AAAI Conference 2018 Conference Paper

Mighty Thymio for University-Level Educational Robotics

  • Jérôme Guzzi
  • Alessandro Giusti
  • Gianni Di Caro
  • Luca Gambardella

Thymio is a small, inexpensive, mass-produced mobile robot with widespread use in primary and secondary education. In order to make it more versatile and effectively use it in later educational stages, including university levels, we have expanded Thymio’s capabilities by adding off-the-shelf hardware and open software components. The resulting robot, that we call Mighty Thymio, provides additional sensing functionalities, increased computing power, networking, and full ROS integration. We present the architecture of Mighty Thymio and show its application in advanced educational activities.

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IROS Conference 2016 Conference Paper

From indoor GIS maps to path planning for autonomous wheelchairs

  • Jérôme Guzzi
  • Gianni A. Di Caro

This work focuses on how to compute trajectories for an autonomous wheelchair based on indoor GIS maps, in particular on IndoorGML maps, which set the standard in this context. Good wheelchair trajectories are safe and comfortable for the user and the people sharing the space with him, turn gently, are high legible, and smooth (at least G 2 continuos). We derive a navigation graph from a given IndoorGML map. We define and solve an optimization problem to find the desired path: given a succession of cells to traverse, the path corresponds to the best composite Bézier trajectory for the wheelchair. We discuss a related multi-objective path planning problem. Experimental results and an implementation on real robots show the planner performance.

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ICRA Conference 2015 Conference Paper

Fair Multi-Target Tracking in Cooperative Multi-Robot systems

  • Jacopo Banfi
  • Jérôme Guzzi
  • Alessandro Giusti
  • Luca Maria Gambardella
  • Gianni A. Di Caro

Cooperative Multi-Robot Observation of Multiple Moving Targets (CMOMMT) denotes a class of problems in which a set of autonomous mobile robots equipped with limited-range sensors are used to keep under observation a (possibly larger) set of mobile targets. Robots cooperatively plan their motion in order to maximize the time during which each target lies within the sensing range of at least one robot.

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IROS Conference 2014 Conference Paper

Interactive Augmented Reality for understanding and analyzing multi-robot systems

  • Fabrizio Ghiringhelli
  • Jérôme Guzzi
  • Gianni A. Di Caro
  • Vincenzo Caglioti
  • Luca Maria Gambardella
  • Alessandro Giusti

Once a multi-robot system is implemented on real hardware and tested in the real world, analyzing its evolution and debugging unexpected behaviors is often a very difficult task. We present a tool for aiding this activity, by visualizing an Augmented Reality overlay on a live video feed acquired by a fixed camera overlooking the robot environment. Such overlay displays live information exposed by each robot, which may be textual (state messages), symbolic (graphs, charts), or, most importantly, spatially-situated; spatially-situated information is related to the environment surrounding the robot itself, such as for example the perceived position of neighboring robots, the perceived extent of obstacles, the path the robot plans to follow. We show that, by directly representing such information on the environment it refers to, our proposal removes a layer of indirection and significantly eases the process of understanding complex multi-robot systems. We describe how the system is implemented, discuss application examples in different scenarios, and provide supplementary material including demonstration videos and a functional implementation.

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IROS Conference 2014 Conference Paper

Kinect-based people detection and tracking from small-footprint ground robots

  • Armando Pesenti Gritti
  • Oscar Tarabini
  • Jérôme Guzzi
  • Gianni A. Di Caro
  • Vincenzo Caglioti
  • Luca Maria Gambardella
  • Alessandro Giusti

Small-footprint mobile ground robots, such as the popular Turtlebot and Kobuki platforms, are by necessity equipped with sensors which lie close to the ground. Reliably detecting and tracking people from this viewpoint is a challenging problem, whose solution is a key requirement for many applications involving sharing of common spaces and close human-robot interaction. We present a robust solution for cluttered indoor environments, using an inexpensive RGB-D sensor such as the Microsoft Kinect or Asus Xtion. Even in challenging scenarios with multiple people in view at once and occluding each other, our system solves the person detection problem significantly better than alternative approaches, reaching a precision, recall and F1-score of 0. 85, 0. 81 and 0. 83, respectively. Evaluation datasets, a real-time ROS-enabled implementation and demonstration videos are provided as supplementary material.

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ICRA Conference 2013 Conference Paper

Human-friendly robot navigation in dynamic environments

  • Jérôme Guzzi
  • Alessandro Giusti
  • Luca Maria Gambardella
  • Guy Theraulaz
  • Gianni A. Di Caro

The vision-based mechanisms that pedestrians in social groups use to navigate in dynamic environments, avoiding obstacles and each others, have been subject to a large amount of research in social anthropology and biological sciences. We build on recent results in these fields to develop a novel fully-distributed algorithm for robot local navigation, which implements the same heuristics for mutual avoidance adopted by humans. The resulting trajectories are human-friendly, because they can intuitively be predicted and interpreted by humans, making the algorithm suitable for the use on robots sharing navigation spaces with humans. The algorithm is computationally light and simple to implement. We study its efficiency and safety in presence of sensing uncertainty, and demonstrate its implementation on real robots. Through extensive quantitative simulations we explore various parameters of the system and demonstrate its good properties in scenarios of different complexity. When the algorithm is implemented on robot swarms, we could observe emergent collective behaviors similar to those observed in human crowds.

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IROS Conference 2013 Conference Paper

Local reactive robot navigation: A comparison between reciprocal velocity obstacle variants and human-like behavior

  • Jérôme Guzzi
  • Alessandro Giusti
  • Luca Maria Gambardella
  • Gianni A. Di Caro

Most local robot navigation algorithms are based on the concept of velocity obstacle, a mechanistic approach to the navigation problem in which a solution is engineered from scratch. Over the years, a number of different velocity obstacle variants have been developed to effectively handle multi-robot systems. In parallel, an alternative, human-inspired approach for robot navigation has been recently proposed, which derives from the observation and modeling of crowds of pedestrians. We discuss similarities and differences among two broadly used obstacle-velocity variants, namely Hybrid Reciprocal Velocity Obstacle and Optimal Reciprocal Collision Avoidance, and the human-inspired approach. How do these differences (which are often subtle) impact performance, and why? We answer these questions through extensive simulation experiments, wherein we evaluate the the algorithms for safety, trajectory efficiency, and emergence of collective behaviors, in different challenging multi-robot scenarios using both ideal and realistic models for robots and sensing.

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