9 research outputs found
AI Security Threats against Pervasive Robotic Systems: A Course for Next Generation Cybersecurity Workforce
Robotics, automation, and related Artificial Intelligence (AI) systems have
become pervasive bringing in concerns related to security, safety, accuracy,
and trust. With growing dependency on physical robots that work in close
proximity to humans, the security of these systems is becoming increasingly
important to prevent cyber-attacks that could lead to privacy invasion,
critical operations sabotage, and bodily harm. The current shortfall of
professionals who can defend such systems demands development and integration
of such a curriculum. This course description includes details about seven
self-contained and adaptive modules on "AI security threats against pervasive
robotic systems". Topics include: 1) Introduction, examples of attacks, and
motivation; 2) - Robotic AI attack surfaces and penetration testing; 3) -
Attack patterns and security strategies for input sensors; 4) - Training
attacks and associated security strategies; 5) - Inference attacks and
associated security strategies; 6) - Actuator attacks and associated security
strategies; and 7) - Ethics of AI, robotics, and cybersecurity
Analysis of LiDAR Configurations on Off-road Semantic Segmentation Performance
This paper investigates the impact of LiDAR configuration shifts on the
performance of 3D LiDAR point cloud semantic segmentation models, a topic not
extensively studied before. We explore the effect of using different LiDAR
channels when training and testing a 3D LiDAR point cloud semantic segmentation
model, utilizing Cylinder3D for the experiments. A Cylinder3D model is trained
and tested on simulated 3D LiDAR point cloud datasets created using the
Mississippi State University Autonomous Vehicle Simulator (MAVS) and 32, 64
channel 3D LiDAR point clouds of the RELLIS-3D dataset collected in a
real-world off-road environment. Our experimental results demonstrate that
sensor and spatial domain shifts significantly impact the performance of
LiDAR-based semantic segmentation models. In the absence of spatial domain
changes between training and testing, models trained and tested on the same
sensor type generally exhibited better performance. Moreover, higher-resolution
sensors showed improved performance compared to those with lower-resolution
ones. However, results varied when spatial domain changes were present. In some
cases, the advantage of a sensor's higher resolution led to better performance
both with and without sensor domain shifts. In other instances, the higher
resolution resulted in overfitting within a specific domain, causing a lack of
generalization capability and decreased performance when tested on data with
different sensor configurations
URA*: Uncertainty-aware Path Planning using Image-based Aerial-to-Ground Traversability Estimation for Off-road Environments
A major challenge with off-road autonomous navigation is the lack of maps or
road markings that can be used to plan a path for autonomous robots. Classical
path planning methods mostly assume a perfectly known environment without
accounting for the inherent perception and sensing uncertainty from detecting
terrain and obstacles in off-road environments. Recent work in computer vision
and deep neural networks has advanced the capability of terrain traversability
segmentation from raw images; however, the feasibility of using these noisy
segmentation maps for navigation and path planning has not been adequately
explored. To address this problem, this research proposes an uncertainty-aware
path planning method, URA* using aerial images for autonomous navigation in
off-road environments. An ensemble convolutional neural network (CNN) model is
first used to perform pixel-level traversability estimation from aerial images
of the region of interest. The traversability predictions are represented as a
grid of traversal probability values. An uncertainty-aware planner is then
applied to compute the best path from a start point to a goal point given these
noisy traversal probability estimates. The proposed planner also incorporates
replanning techniques to allow rapid replanning during online robot operation.
The proposed method is evaluated on the Massachusetts Road Dataset, the
DeepGlobe dataset, as well as a dataset of aerial images from off-road proving
grounds at Mississippi State University. Results show that the proposed image
segmentation and planning methods outperform conventional planning algorithms
in terms of the quality and feasibility of the initial path, as well as the
quality of replanned paths
Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities
Robotics and Artificial Intelligence (AI) have been inextricably intertwined
since their inception. Today, AI-Robotics systems have become an integral part
of our daily lives, from robotic vacuum cleaners to semi-autonomous cars. These
systems are built upon three fundamental architectural elements: perception,
navigation and planning, and control. However, while the integration of
AI-Robotics systems has enhanced the quality our lives, it has also presented a
serious problem - these systems are vulnerable to security attacks. The
physical components, algorithms, and data that make up AI-Robotics systems can
be exploited by malicious actors, potentially leading to dire consequences.
Motivated by the need to address the security concerns in AI-Robotics systems,
this paper presents a comprehensive survey and taxonomy across three
dimensions: attack surfaces, ethical and legal concerns, and Human-Robot
Interaction (HRI) security. Our goal is to provide users, developers and other
stakeholders with a holistic understanding of these areas to enhance the
overall AI-Robotics system security. We begin by surveying potential attack
surfaces and provide mitigating defensive strategies. We then delve into
ethical issues, such as dependency and psychological impact, as well as the
legal concerns regarding accountability for these systems. Besides, emerging
trends such as HRI are discussed, considering privacy, integrity, safety,
trustworthiness, and explainability concerns. Finally, we present our vision
for future research directions in this dynamic and promising field
The design and protocol of acupuncture for migraine prophylaxis: A multicenter randomized controlled trial
Background: Many studies have already reported encouraging results in the prophylactic therapy of migraine by acupuncture, but there seems to be a lack of high quality randomized controlled trials from China. We design and perform a randomized controlled clinical trial to evaluate the efficacy of acupuncture compared with flunarizine in the prophylactic therapy of patients with migraine without aura in China. Methods: This trial is a multicenter, prospective, randomized controlled clinical trial. The 140 migraine patients are randomly allocated to two different groups. The acupuncture groups (n = 70) is treated with acupuncture and placebo medicine; while the control group (n = 70) is treated with sham acupuncture and medicine (Flunarizine). Both Flunarizine and placebo are taken 10 mg once per night for the first 2 weeks and then 5 mg once per night for the next 2 weeks. Patients in both groups receive 12 sessions of verum/sham acupuncture in 4 weeks. Discussion: The study design and the long term clinical practice of acupuncturists guarantee a high external validity for the results. The results of our trial will be helpful to supply the evidence on the efficacy of acupuncture for migraine prophylaxis in China. Trial Registration: The trial is registered at Controlled Clinical Trials: ISRCTN49839714.Medicine, Research & ExperimentalSCI(E)0ARTICLEnull1
Point Cloud Scene Completion of Obstructed Building Facades with Generative Adversarial Inpainting
Collecting 3D point cloud data of buildings is important for many applications such as urban mapping, renovation, preservation, and energy simulation. However, laser-scanned point clouds are often difficult to analyze, visualize, and interpret due to incompletely scanned building facades caused by numerous sources of defects such as noise, occlusions, and moving objects. Several point cloud scene completion algorithms have been proposed in the literature, but they have been mostly applied to individual objects or small-scale indoor environments and not on large-scale scans of building facades. This paper introduces a method of performing point cloud scene completion of building facades using orthographic projection and generative adversarial inpainting methods. The point cloud is first converted into the 2D structured representation of depth and color images using an orthographic projection approach. Then, a data-driven 2D inpainting approach is used to predict the complete version of the scene, given the incomplete scene in the image domain. The 2D inpainting process is fully automated and uses a customized generative-adversarial network based on Pix2Pix that is trainable end-to-end. The inpainted 2D image is finally converted back into a 3D point cloud using depth remapping. The proposed method is compared against several baseline methods, including geometric methods such as Poisson reconstruction and hole-filling, as well as learning-based methods such as the point completion network (PCN) and TopNet. Performance evaluation is carried out based on the task of reconstructing real-world building facades from partial laser-scanned point clouds. Experimental results using the performance metrics of voxel precision, voxel recall, position error, and color error showed that the proposed method has the best performance overall
Nuclear Power Plant Disaster Site Simulation Using Rigid Body Physics
Nuclear power plant disasters in the past century have led to the major economic loss, negative environmental impact, and danger to human lives. Disaster relief operations such as search and rescue, hazard detection, and damage assessment face significant challenges due to the difficulty in categorizing and analyzing damaged building structures. This study proposes an efficient method to generate a large number of 3D geometric models of a post-disaster nuclear power plant site using the Bullet physics engine. Two scenarios of nuclear power plant disasters, which are explosions due to reactor failure as well as earthquakes, are considered in this study. Different degrees of virtual forces are applied to the nuclear power plant site, and the resulting geometry of damaged structures are recorded. The generated database of damaged structures has many potential applications in object recognition, risk classification, and hazard analysis.N
Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities
Robotics and Artificial Intelligence (AI) have been inextricably intertwined since their inception. Today, AI-Robotics systems have become an integral part of our daily lives, from robotic vacuum cleaners to semi-autonomous cars. These systems are built upon three fundamental architectural elements: perception, navigation and planning, and control. However, while the integration of AI in Robotics systems has enhanced the quality of our lives, it has also presented a serious problem - these systems are vulnerable to security attacks. The physical components, algorithms, and data that makeup AI-Robotics systems can be exploited by malicious actors, potentially leading to dire consequences. Motivated by the need to address the security concerns in AI-Robotics systems, this paper presents a comprehensive survey and taxonomy across three dimensions: attack surfaces, ethical and legal concerns, and Human-Robot Interaction (HRI) security. Our goal is to provide readers, developers and other stakeholders with a holistic understanding of these areas to enhance the overall AI-Robotics system security. We begin by identifying potential attack surfaces and provide mitigating defensive strategies. We then delve into ethical issues, such as dependency and psychological impact, as well as the legal concerns regarding accountability for these systems. Besides, emerging trends such as HRI are discussed, considering privacy, integrity, safety, trustworthiness, and explainability concerns. Finally, we present our vision for future research directions in this dynamic and promising field