A Factor Graph Model of Trust for a Collaborative Multi-Agent System

In the field of Multi-Agent Systems (MAS), known for their openness, dynamism, and cooperative nature, the ability to trust the resources and services of other agents is crucial. Trust, in this setting, is the reliance and confidence an agent has in the information, behaviors, intentions, truthfulness, and capabilities of others within the system. Our paper introduces a new graphical approach that utilizes factor graphs to represent the interdependent behaviors and trustworthiness among agents. This includes modeling the behavior of robots as a trajectory of actions using a Gaussian process factor graph, which accounts for smoothness, obstacle avoidance, and trust-related factors. Our method for evaluating trust is decentralized and considers key interdependent sub-factors such as proximity safety, consistency, and cooperation. The overall system comprises a network of factor graphs that interact through trust-related factors and employs a Bayesian inference method to dynamically assess trust-based decisions with informed consent. The effectiveness of this method is validated via simulations and empirical tests with autonomous robots navigating unsignalized intersections.

GPT-4 and Safety Case Generation: An Exploratory Analysis

In the ever-evolving landscape of software engineering, the emergence of large language models (LLMs) and conversational interfaces, exemplified by ChatGPT, is nothing short of revolutionary. While their potential is undeniable across various domains, this paper sets out on a captivating expedition to investigate their uncharted territory, the exploration of generating safety cases. In this paper, our primary objective is to delve into the existing knowledge base of GPT-4, focusing specifically on its understanding of the Goal Structuring Notation (GSN), a well-established notation allowing to visually represent safety cases. Subsequently, we perform four distinct experiments with GPT-4. These experiments are designed to assess its capacity for generating safety cases within a defined system and application domain. To measure the performance of GPT-4 in this context, we compare the results it generates with ground-truth safety cases created for an X-ray system system and a Machine-Learning (ML)-enabled component for tire noise recognition (TNR) in a vehicle. This allowed us to gain valuable insights into the model's generative capabilities. Our findings indicate that GPT-4 demonstrates the capacity to produce safety arguments that are moderately accurate and reasonable. Furthermore, it exhibits the capability to generate safety cases that closely align with the semantic content of the reference safety cases used as ground-truths in our experiments.

Fiducial Marker Detection in Multi-Viewpoint Point Cloud

The existing LiDAR fiducial marker systems have usage restrictions. Especially, LiDARTag requires a specific marker placement and Intensity Image-based LiDAR Fiducial Marker demands that the point cloud is sampled from one viewpoint. As a result, with point clouds sampled from multiple viewpoints, fiducial marker detection remains an unsolved problem. In this letter, we develop a novel algorithm to detect the fiducial markers in the multi-viewpoint point cloud. The proposed algorithm includes two stages. First, Regions of Interest (ROIs) detection finds point clusters that could contain fiducial markers. Specifically, a method extracting the ROIs from the intensity perspective is introduced on account of the fact that from the spatial perspective, the markers, which are sheets of paper or thin boards, are non-distinguishable from the planes to which they are attached. Second, marker detection verifies if the candidate ROIs contain fiducial markers and outputs the ID numbers and vertices locations of the markers in the valid ROIs. In particular, the ROIs are transmitted to a predefined intermediate plane for the purpose of adopting a spherical projection to generate the intensity image, and then, marker detection is completed through the intensity image. Qualitative and quantitative experimental results are provided to validate the proposed algorithm. The codes and results are available at: https://github.com/York-SDCNLab/Marker?Detection-General

Intensity Image-based LiDAR Fiducial Marker System

The fiducial marker system for LiDAR is crucial for the robotic application but it is still rare to date. In this paper, an Intensity Image-based LiDAR Fiducial Marker (IILFM) system is developed. This system only requires an unstructured point cloud with intensity as the input and it has no restriction on marker placement and shape. A marker detection method that locates the predefined 3D fiducials in the point cloud through the intensity image is introduced. Then, an approach that utilizes the detected 3D fiducials to estimate the LiDAR 6-DOF pose that describes the transmission from the world coordinate system to the LiDAR coordinate system is developed. Moreover, all these processes run in real-time (approx 40 Hz on Livox Mid-40 and approx 143 Hz on VLP-16). Qualitative and quantitative experiments are conducted to demonstrate that the proposed system has similar convenience and accuracy as the conventional visual fiducial marker system. The codes and results are available at: https://github.com/York-SDCNLab/IILFM.

Autonomous Vision-based UAV Landing with Collision Avoidance using Deep Learning

There is a risk of collision when multiple UAVs land simultaneously without communication on the same platform. This work accomplishes vision-based autonomous landing and uses a deep-learning-based method to realize collision avoidance during the landing process.

Ghost-DeblurGAN and Its Application to Fiducial Marker System

Motion blur can impede marker detection and marker-based pose estimation, which is common in real-world robotic applications involving fiducial markers. To solve this problem, we propose a novel lightweight generative adversarial network (GAN), Ghost-DeblurGAN, for real-time motion deblurring. Furthermore, a new large-scale dataset, YorkTag, provides pairs of sharp/blurred images containing fiducial markers and is proposed to train and qualitatively and quantitatively evaluate our model. Experimental results demonstrate that when applied along with fudicual marker systems to motion-blurred images, Ghost-DeblurGAN improves the marker detection significantly and mitigates the rotational ambiguity problem in marker-based pose estimation.

Navigation of a Self-Driving Vehicle Using One Fiducial Marker

Navigation using only one marker, which contains four artificial features, is a challenging task since camera pose estimation using only four coplanar points suffers from the rotational ambiguity problem in a real-world application. This paper presents a framework of vision-based navigation for a self-driving vehicle equipped with multiple cameras and a wheel odometer. A multiple camera setup is presented for the camera cluster which has 360-degree vision such that our framework solely requires one planar marker. A Kalman-Filter-based fusion method is introduced for the multiple-camera and wheel odometry. Furthermore, an algorithm is proposed to resolve the rotational ambiguity problem using the prediction of the Kalman Filter as additional information. Finally, the lateral and longitudinal controllers are provided. Experiments are conducted to illustrate the effectiveness of the theory.