Giovanni Beltrame

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)

Overcoming Boundaries: Interdisciplinary Challenges and Opportunities in Cognitive Neuroscience

Arnaud Brignol

Anita Paas

Luis Sotelo-Castro

David St-Onge

Emily B.J. Coffey

2024-06-30

Neuropsychologia (published)

Variable Time Step Reinforcement Learning for Robotic Applications

Yong Wang

Traditional reinforcement learning (RL) generates discrete control policies, assigning one action per cycle. These policies are usually impl… (see more)emented as in a fixed-frequency control loop. This rigidity presents challenges as optimal control frequency is task-dependent; suboptimal frequencies increase computational demands and reduce exploration efficiency. Variable Time Step Reinforcement Learning (VTS-RL) addresses these issues with adaptive control frequencies, executing actions only when necessary, thus reducing computational load and extending the action space to include action durations. In this paper we introduce the Multi-Objective Soft Elastic Actor-Critic (MOSEAC) method to perform VTS-RL, validating it through theoretical analysis and experimentation in simulation and on real robots. Results show faster convergence, better training results, and reduced energy consumption with respect to other variable- or fixed-frequency approaches.

2024-06-28

ArXiv (preprint)

MOSEAC: Streamlined Variable Time Step Reinforcement Learning

Yong Wang

2024-06-02

ArXiv (preprint)

Hierarchies define the scalability of robot swarms

Vivek Shankar Vardharajan

Karthik Soma

Sepand Dyanatkar

Pierre-Yves Lajoie

The emerging behaviors of swarms have fascinated scientists and gathered significant interest in the field of robotics. Traditionally, swarm… (see more)s are viewed as egalitarian, with robots sharing identical roles and capabilities. However, recent findings highlight the importance of hierarchy for deploying robot swarms more effectively in diverse scenarios. Despite nature's preference for hierarchies, the robotics field has clung to the egalitarian model, partly due to a lack of empirical evidence for the conditions favoring hierarchies. Our research demonstrates that while egalitarian swarms excel in environments proportionate to their collective sensing abilities, they struggle in larger or more complex settings. Hierarchical swarms, conversely, extend their sensing reach efficiently, proving successful in larger, more unstructured environments with fewer resources. We validated these concepts through simulations and physical robot experiments, using a complex radiation cleanup task. This study paves the way for developing adaptable, hierarchical swarm systems applicable in areas like planetary exploration and autonomous vehicles. Moreover, these insights could deepen our understanding of hierarchical structures in biological organisms.

2024-05-02

ArXiv (preprint)