Giovanni Beltrame

PhD - Polytechnique Montréal

Co-supervisor :

haechan-mark.bong@mila.quebec

Website

Google Scholar

Yann Bouteiller

Collaborating researcher - Polytechnique Montréal Montreal

Co-supervisor :

yann.bouteiller@mila.quebec

PhD - Polytechnique Montréal

Co-supervisor :

maeva.guerrier@mila.quebec

Github

Simon Roy

Master's Research - Université de Montréal

Co-supervisor :

simon.roy@mila.quebec

Website

Github

Soma Soma

PhD - Polytechnique Montréal

Co-supervisor :

karthik.soma@mila.quebec

Publications

A Blockchain Framework for Equitable and Secure Task Allocation in Robot Swarms

Hanqing Zhao

Alexandre Pacheco

Xue Liu

Marco Dorigo

Gregory Dudek

Recent studies demonstrate the potential of blockchain to enable robots in a swarm to achieve secure consensus about the environment, partic… (see more)ularly when robots are homogeneous and perform identical tasks. Typically, robots receive rewards for their contributions to consensus achievement, but no studies have yet targeted heterogeneous swarms, in which the robots have distinct physical capabilities suited to different tasks. We present a novel framework that leverages domain knowledge to decompose the swarm mission into a hierarchy of tasks within smart contracts. This allows the robots to reach a consensus about both the environment and the action plan, allocating tasks among robots with diverse capabilities to improve their performance while maintaining security against faults and malicious behaviors. We refer to this concept as equitable and secure task allocation. Validated in Simultaneous Localization and Mapping missions, our approach not only achieves equitable task allocation among robots with varying capabilities, improving mapping accuracy and efficiency, but also shows resilience against malicious attacks.

2024-12-31

IEEE Robotics and Automation Letters (unknown)

Multi-Robot Decentralized Collaborative SLAM in Planetary Analogue Environments: Dataset, Challenges, and Lessons Learned

Pierre-Yves Lajoie

Karthik Soma

Alice Lemieux-Bourque

Rongge Zhang

Vivek Shankar Varadharajan

Decentralized collaborative simultaneous localization and mapping (C-SLAM) is essential to enable multirobot missions in unknown environment… (see more)s without relying on preexisting localization and communication infrastructure. This technology is anticipated to play a key role in the exploration of the Moon, Mars, and other planets. In this article, we share insights and lessons learned from C-SLAM experiments involving three robots operating on a Mars analogue terrain and communicating over an ad hoc network. We examine the impact of limited and intermittent communication on C-SLAM performance, as well as the unique localization challenges posed by planetary-like environments. Additionally, we introduce a novel dataset collected during our experiments, which includes real-time peer-to-peer inter-robot throughput and latency measurements. This dataset aims to support future research on communication-constrained, decentralized multirobot operations.

2024-12-31

IEEE Transactions on Field Robotics (published)

BlabberSeg: Real-Time Embedded Open-Vocabulary Aerial Segmentation

Ricardo de Azambuja

Real-time aerial image segmentation plays an important role in the environmental perception of Uncrewed Aerial Vehicles (UAVs). We introduce… (see more) BlabberSeg, an optimized Vision-Language Model built on CLIPSeg for on-board, real-time processing of aerial images by UAVs. BlabberSeg improves the efficiency of CLIPSeg by reusing prompt and model features, reducing computational overhead while achieving real-time open-vocabulary aerial segmentation. We validated BlabberSeg in a safe landing scenario using the Dynamic Open-Vocabulary Enhanced SafE-Landing with Intelligence (DOVESEI) framework, which uses visual servoing and open-vocabulary segmentation. BlabberSeg reduces computational costs significantly, with a speed increase of 927.41% (16.78 Hz) on a NVIDIA Jetson Orin AGX (64GB) compared with the original CLIPSeg (1.81Hz), achieving real-time aerial segmentation with negligible loss in accuracy (2.1% as the ratio of the correctly segmented area with respect to CLIPSeg). BlabberSeg's source code is open and available online.

2024-10-15

ArXiv (preprint)

Active Semantic Mapping and Pose Graph Spectral Analysis for Robot Exploration

Rongge Zhang

2024-10-13

2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (published)

Canadarm, Canadarm2, and Canadarm3: The Evolution of Canada's Iconic Robotic System and Its Impacts from Space Down to Earth

Yianni Hudon-Castillo

Jean-Christophe Lamanque

Marion Thénault

Katherine Zamudio-Turcotte

Sri Venkata Vathsala Musunuri

Auriane Thilloy

Olivier Leclair

Mohamed Amine Elforaici

Rafael Daigneault

Rachad Chazbek

2024-10-13

58th IAA History of Astronautics Symposium (published)

Physical Simulation for Multi-agent Multi-machine Tending

Abdalwhab Abdalwhab

David St-Onge

2024-10-10

ArXiv (preprint)

Beyond the lab: Feasibility of cognitive neuroscience data collection during a speleological expedition

Anita Paas

Hugo R. Jourde

Arnaud Brignol

Marie-Anick Savard

Zseyvfin Eyqvelle

Samuel Bassetto

Emily B.J. Coffey

2024-10-04

Journal of Environmental Psychology (published)

Multi-Objective Risk Assessment Framework for Exploration Planning Using Terrain and Traversability Analysis

Riana Gagnon Souleiman

Vivek Shankar Vardharajan

2024-10-03

ArXiv (preprint)

Frequency-based View Selection in Gaussian Splatting Reconstruction

Monica Li

Pierre-Yves Lajoie

Three-dimensional reconstruction is a fundamental problem in robotics perception. We examine the problem of active view selection to perform… (see more) 3D Gaussian Splatting reconstructions with as few input images as possible. Although 3D Gaussian Splatting has made significant progress in image rendering and 3D reconstruction, the quality of the reconstruction is strongly impacted by the selection of 2D images and the estimation of camera poses through Structure-from-Motion (SfM) algorithms. Current methods to select views that rely on uncertainties from occlusions, depth ambiguities, or neural network predictions directly are insufficient to handle the issue and struggle to generalize to new scenes. By ranking the potential views in the frequency domain, we are able to effectively estimate the potential information gain of new viewpoints without ground truth data. By overcoming current constraints on model architecture and efficacy, our method achieves state-of-the-art results in view selection, demonstrating its potential for efficient image-based 3D reconstruction.

2024-09-23

ArXiv (preprint)

Swarming Out of the Lab: Comparing Relative Localization Methods for Collective Behavior

Rafael Gomes Braga

Vivek Shankar Vardharajan

David St-Onge

2024-09-14

Lecture Notes in Computer Science (published)

Concurrent product layout design optimization and dependency management using a modified NSGA-III approach

Yann-Seing Law-Kam Cio

Aurelian Vadean

Abolfazl Mohebbi

Sofiane Achiche

The complexity of mechatronic systems has increased with the significant advancements of technology in their components which makes their de… (see more)sign more challenging. This is due to the need for incorporating expertise from different domains as well as the increased number and complexity of components integrated into the product. To alleviate the burden of designing such products, many industries and researchers are attracted to the concept of modularization which is to identify a subset of system components that can form a module. To achieve this, a novel product-related dependency management approach is proposed in this paper with the support of an augmented design structure matrix. This approach makes it possible to model positive and negative dependencies and to compute the combination potency between components to form modules. This approach is then integrated into a modified non-dominated sorting genetic algorithm III to concurrently optimize the design and identify the modules. The methodology is exemplified through the case study of a layout design of an automatic greenhouse. By applying the proposed methodology to the case study, it was possible to generate concepts that decreased the number of modules from 9 down to 4 while ensuring the optimization of the design performance.

2024-08-20

Frontiers in Mechanical Engineering (published)

LiDAR-based Real-Time Object Detection and Tracking in Dynamic Environments

Wenqiang Du

In dynamic environments, the ability to detect and track moving objects in real-time is crucial for autonomous robots to navigate safely and… (see more) effectively. Traditional methods for dynamic object detection rely on high accuracy odometry and maps to detect and track moving objects. However, these methods are not suitable for long-term operation in dynamic environments where the surrounding environment is constantly changing. In order to solve this problem, we propose a novel system for detecting and tracking dynamic objects in real-time using only LiDAR data. By emphasizing the extraction of low-frequency components from LiDAR data as feature points for foreground objects, our method significantly reduces the time required for object clustering and movement analysis. Additionally, we have developed a tracking approach that employs intensity-based ego-motion estimation along with a sliding window technique to assess object movements. This enables the precise identification of moving objects and enhances the system's resilience to odometry drift. Our experiments show that this system can detect and track dynamic objects in real-time with an average detection accuracy of 88.7\% and a recall rate of 89.1\%. Furthermore, our system demonstrates resilience against the prolonged drift typically associated with front-end only LiDAR odometry. All of the source code, labeled dataset, and the annotation tool are available at: https://github.com/MISTLab/lidar_dynamic_objects_detection.git

2024-07-03

ArXiv (preprint)