Artificial Intelligence and Robotics Commons^™

Research On Dual-Layer Path Planning Method For Lunar Rover Based On Slip Prediction, Zhang Xingyu, Baolei Wu, Jun Wang, Miaoying Hong, Jiahui Wang, Yongqiang Qi

Journal of System Simulation

Abstract: In response to the challenges faced by lunar rovers in the process of path planning, such as safe obstacle avoidance and target deviation caused by complex terrain, a dual-layer path planning based on slip prediction is proposed. In this approach, flat terrain is adaptively selected to reduce the wheel slip of the lunar rover. The overall complexity of the terrain is calculated using digital elevation information, and a Q-learning algorithm with a three-level reward mechanism is designed to navigate around highslip areas, achieving global path planning. A depth camera is used to perceive obstacles, a dynamic window method based …

Design Of Distributed Multi-Functional Integrated Signal-Level Confrontation Simulation System In Local Area, Weiqian Li, Tianyu Yang, Zongyang Li, Jianjun Chen

Journal of System Simulation

Abstract: In order to study the resources management and self-organized collaborative application method of multiple multi-functional integrated electronic equipment in the region, we build a signal-level digital simulation system that supports multiple distributed multi-functional integrated electronic equipment within a region to carry out cooperation or confrontation. A joint time advancing mechanism named "variable-step time advancing method based on frame scheduling" and "independent event driven time advancing method " is proposed. It can not only ensure the integrity of each frame of radar simulation data for each equipment, but also enable multiple equipment in the simulation system to advance simultaneously and …

Uav Path Planning Based On Improved Deep Deterministic Policy Gradients, Sen Zhang, Qiangqiang Dai

Journal of System Simulation

Abstract: Aiming at the problems of poor convergence and invalid exploration when UAVs perform path planning in complex environments, an improved deep deterministic policy gradient(DDPG) algorithm is proposed. Using a dual experience pooling mechanism to store success and failure experiences separately, the algorithm is able to use the success experience to strengthen the strategy optimization and learn from the failure experience to avoid the wrong path; an APF method is introduced to add a bootstrap term to the planning, which is combined with the exploration of noisy actions in a randomized sampling process to dynamically integrate the selected actions; multi-objective …

Optimization Of Vehicle Routing For Cross-Infection Risk In The Epidemic, Xiaodong Shi, Yongcheng Guo, Mingqi Ma, Jiarui Pan

Journal of System Simulation

Abstract: In view of the safety risks associated with logistics distribution route optimization during public health emergencies, this paper investigates the vehicle routing problem by incorporating the risk of cross-infection, integrates the cross-infection risk caused by logistics activities in the epidemic area into the logistics distribution model, and establishes a logistics vehicle distribution model with the goal of cross-infection risk and cost. An improved genetic algorithm is designed for model optimization and solution. Based on the integration of chaos initialization population and adaptive crossover and mutation operations, a neighbor exclusion operator is further proposed to enhance the global search ability …

Remote Sensing Small Object Detection Based On Cross-Stage Two-Branch Feature Aggregation, Jie Li, Yang Liu, Liang Li, Bengan Su, Jialong Wei, Guangda Zhou, Yanmin Shi, Zhen Zhao

Journal of System Simulation

Abstract: Aiming at YOLOv8's leakage and false detection problems caused by target scale difference and complex background in remote sensing small target detection, this paper proposes a remote sensing image small target detection method based on cross-stage two-branch feature aggregation. The global shared weights in the convolution operator and the context-aware weights of specific tokens in the attention are fused to obtain high-frequency local information and low-frequency global information; the global remote dependencies are captured using a lightweight MLP, and the parallel cross-stage learnable vision center mechanism is designed to capture the information of the local corner regions of the …

A Radar Countermeasure Modeling Method Incorporating Cognitive Bias, Rui Wang, Xiangyang Li, Dong Wang, Hongguang Ma, Zhili Zhang

Journal of System Simulation

Abstract: Cognitive bias, stemming from electronic measurement error and variability in human perception, exists in cognitive electronic warfare and affects the outcomes of conflicts. In this paper, the dynamic game approach is employed to develop a model for cognitive bias induced by incomplete information and measurement errors in cognitive radar countermeasures. The payoffs for both parties are calculated using the radar's anti-jamming strategy matrix A and the jammer's jamming strategy matrix B. With perfect Bayesian equilibrium, a dynamic radar countermeasure model is established, and the impact of cognitive bias is analyzed. Drawing inspiration from the cognitive bias analysis method used …

Digital Twin Framework For The Generation And Optimization Of Security Policies For Tsn Industrial Control Systems, Huimai Zhang, Xiaoya Hu, Chunjie Zhou

Journal of System Simulation

Abstract: The characteristic of multi-service flow integration in TSN industrial control systems makes it very difficult to establish an accurate mathematical model. In order to ensure the coordination between the security policy and the real-time operation of the system, a four-layer double-closed-loop digital twin framework of "physical layer-data layer-twin layer-service layer" serving the generation and optimization of security policies is proposed. The optimal security policy generation is achieved through the internal closed loop composed of iterative optimization between the initial security policy generation at the service layer and the deployment verification at the twin layer. The deterministic communication process between …

A Transfer Learning-Based Hybrid Model For Pm2.5 Concentration Prediction, Xinbiao Lu, Chunlin Ye, Yisen Chen, Wen Wu, Yudan Chen

Journal of System Simulation

Abstract: In order to solve the problems of increased computational cost due to irrelevant features and decreased prediction accuracy due to the difference in probability distribution caused by the change of data distribution over time in PM2.5 concentration prediction, this paper constructs a hybrid deep learning model TraTCN-LSTM-BiGRU based on migration learning. The meteorological factors related to PM2.5 concentration are selected as the model input using the mean-value heat map algorithm features; the source domain data and target domain data are divided by KL scatter and an adaptive layer is introduced into the model to achieve inter-domain distribution adaptation; the …

Trajectory Planning Of Quadruped Robot Over Obstacle With Single Leg Based On Deep Reinforcement Learning, Min Li, Sen Zhang, Xiangguang Zeng, Gang Wang, Tongwei Zhang, Dijie Xie, Wenzhe Ren, Tao Zhang

Journal of System Simulation

Abstract: Aiming at the problems of joint vibration and high energy consumption of quadruped robot in the process of walking over obstacles, a foot trajectory planning method of quadruped robot based on deep reinforcement learning SAC algorithm is proposed. Based on robot kinematics and Monte Carlo method, the motion space of the single-legged foot of quadruped robot is analyzed. A compound seventhdegree polynomial trajectory of the quadruped robot is planned. The SAC algorithm is used to train and obtain the low energy consumption obstacle crossing strategy of four-legged robot under different obstacle environment. The simulation results show that the compound …

A Method For Road Extraction Using Masked Image Modeling And Contrastive Learning, Jiangjiang Wu, Zhenghong Li, Zhichao Sha, Hao Chen, Shuang Peng, Chun Du, Jun Li

Journal of System Simulation

Abstract: Aiming at the occlusion problem of road extraction from remote sensing images, a road extraction method combining MIM and CL is proposed, the model training process includes a masked pretraining stage and a contrast training stage. The masked pre-training stage mainly carries out mask image reconstruction, and trains the model to recover the whole image from some areas that are randomly occluded. The comparison training stage is mainly for the prediction error and low confidence regions to learn the comparison, to narrow the distance between the features of the same category and increase the distance between the features of …

An Event Ontology And Dataset Construction Method For Strategic Operations Analysis, Quanlin Chen, Jun Jia

Journal of System Simulation

Abstract: Aiming at the lack of professional datasets for information extraction technology research in the field of strategic operations research analysis, this paper proposes an event ontology and dataset construction method for strategic operations research analysis. The method proposes an event ontology model for strategic operations research analysis according to the needs of situation judgment in strategic operations research analysis, and uses the method of "a small amount of manual annotation + fine-tuned large language model annotation" to construct the event dataset EfSOA for strategic operations research analysis. The dataset construction method proposed in this paper and the constructed dataset …

Research On Economic Dispatching Strategy Of Chp Units Based On Srl, Xin Wang, Chenggang Cui, Xiangxiang Wang, Ping Zhu

Journal of System Simulation

Abstract: In addressing the challenge of the DRL algorithm in the optimization of combined heat and power (CHP) units, lacking safety and stability guarantees, a scheduling optimization method based on SRL is proposed. Utilizing Dymola platform, a district heating system model is constructed with the CHP unit as the heat source. A MDP model for the economic dispatching of CHP units is designed, incorporating control barrier functions (CBF) to guide safe exploration in DRL. Simulation results show that the CBF-DRL method, in complex and nonlinear district heating systems, not only accelerates the convergence of DRL algorithms but also efficiently utilizes …

Mobile Robot Path Planning Based On Search-Step Optimized A* Algorithm, Die Yu, Baizhong Bao, Yan Si, Jian Duan, Xiaobin Zhan, Tielin Shi

Journal of System Simulation

Abstract: A search-step optimized A* algorithm is proposed to address the issues with the traditional A* algorithm in robot path planning tasks, such as the high time consumption in large-scale high-resolution maps and the poor paths qualitys. Based on the cubic Hermite curve, a set of search steps (the path edges connecting the current node to its successors) is constructed, which can match the size of the robot and satisfy the dynamic constraints of the robot. More accurate cost functions are established based on the length and maximum absolute curvature value of the curve. Experimental results show that compared with …

Signal Timing Optimization Via Reinforcement Learning With Traffic Flow Prediction, Ming Xu, Jinye Li, Dongyu Zuo, Jing Zhang

Journal of System Simulation

Abstract: In response to the existing reinforcement learning-based traffic signal control methods that do not consider the changing trends in traffic flow, leading to congestion and inability to adapt to complex and variable road conditions, we propose a traffic signal timing optimization reinforcement learning method based on flow prediction. A phase timing amplitude control model is introduced. This model analyzes the spatiotemporal characteristics of historical traffic data to predict the flow for the next time slot and calculates a reasonable range for phase timing based on the prediction results. The H-PPO algorithm is employed to control the signal phase while …

An Intelligent Tracking Control Method For Unmanned Vehicles With Time-Varying Disturbances, Jie Huang, Jie Huang

Journal of System Simulation

Abstract: An intelligent policy iteration tracking control method is proposed for the tracking control problem with bounded time-varying disturbances. An adaptive disturbance compensator is designed to counteract the bounded disturbance and guarantee the validity of the Hamilton-Jacobi-Bellman (HJB) equation. An identifier network is proposed to estimate the unknown vehicle dynamics, and a new HJB equation is derived using the reconstructed identifier tracking error. An online optimal tracking control strategy for unmanned vehicles is obtained in the state of identifier estimation with the assistance of actor-critic network. Based on Lyapunov theory, it is demonstrated that the identifier tracking error, identifier approximation …

Capability Dependency Analysis Based On Kill Chain And Fdna, Yushuai Wang, Guangya Si

Journal of System Simulation

Abstract: To better support the operation SoS analysis, deeply analyze the impact of dependency relationship during mission accomplishment, and accurately grasp the deep logic of SoS capability generation, the capability dependency analysis method based on the kill chain and function dependency network analysis(FDNA) is proposed. Combined with the analysis of the characteristics of the capability dependency relationship, the kill chain closure and the kill web formation process are abstracted from the perspective of operational interaction, a capability dependency network modeling method for the SoS is proposed, and a specific process covering the identification of capability dependency, calculation of operability, solving …

Looking Good: The Math Behind Computer Vision*, Corbin Weiss

Campus Research Month

Exploring the mathematical foundations of a Multilayer Perceptron (MLP), a foundational approach to computer vision. Then expanding this understanding to create a visualization of the representation of reality in the MLP.

Control Of Industrial Robots Based On Artificial Intelligence, Bryan Lara Medrano

Open Access Theses & Dissertations

Industrial robots are vital in developing smart factories, creating the need for more efficient and modern control systems. As a result, investigators and scholars are dedicating great effort to advancing this field et al. [27]. Literature showcases significant progress in various areas, including the control of articulated arms and advancements in human-robot interfaces, self-decision-making, object recognition, decision-making, and routing planning. This manuscript describes a novel technique for predicting the movement of a robotic arm based on artificial neural networks. We have implemented an artificial intelligence method based on artificial neural networks to analyze the possible routing of a robotic arm …

Machine Learning Methods For Hypervelocity Fragment Flyout Characterization, Katharine Larsen

Doctoral Dissertations and Master's Theses

Resulting from breakup events, such as collisions and explosions, hypervelocity fragments create potential hazards for both terrestrial and on-orbit environments, such as terrestrial weapons explosions and satellite breakup events, respectively. To avoid unnecessary damage, an accurate understanding or characterization of hypervelocity fragmentation events is vital. Currently, publicly available two-line elements collected from on-orbit breakup events are limited, excluding pre-detonation parent body conditions, such as orientation, and information of smaller fragments. The uncertainty of these datasets varies between each collected set. Therefore, the overall goal of this work is to employ machine learning to estimate distribution characteristics of a space debris …

From Image Enhancement To Model Protection Integrating Generative Ai And Secure Learning In Computer Vision, Mohammad Shahab Uddin

Electrical & Computer Engineering Theses & Dissertations

This dissertation aims to address critical challenges in the field of computer vision and machine learning, focusing on three key areas: image translation, denoising, and model security. The research encompasses novel methodologies and models that significantly advance existing techniques. This dissertation will not only provide valuable contributions to the academic community but also hold significant potential for practical applications in domains ranging from surveillance to autonomous systems.

Consequently, this dissertation proposes three goals. First, we present new approaches for converting optical videos to infrared videos using deep learning. To apply powerful deep learning based algorithms for object detection and classification …

Enhancing Iot Security Using Lightweight Machine Learning Algorithms: A Comprehensive Approach Using Ensemble Learning, Feature Selection, And Federated Transfer Learning, Khawlah Harahsheh

Electrical & Computer Engineering Theses & Dissertations

The rapid expansion of the Internet of Things (IoT) has introduced significant security vulnerabilities due to the resource-constrained nature of IoT devices and their exposure to cyber threats. Traditional security solutions are often infeasible due to the high computational and storage demands they impose. This dissertation presents a lightweight, AI-driven security framework that enhances IoT network resilience by integrating feature selection, ensemble learning, and federated transfer learning while maintaining data privacy and minimizing computational overhead.

The proposed framework consists of three primary components: Feature Selection for Intrusion Detection, which optimizes performance by reducing redundant data and improving detection accuracy with …

On Generalization Across Environments In Multi-Objective Reinforcement Learning, Jayden Jing Xiang Teoh, Pradeep Varakantham, Peter Vamplew

Research Collection School Of Computing and Information Systems

No abstract provided.

Dual Operation Aggregation Graph Neural Networks For Solving Flexible Job-Shop Scheduling Problem With Reinforcement Learning, Peng Zhao, You Zhou, Di Wang, Zhiguang Cao, Yubin Xiao, Xuan Wu, Yuanshu Li, Hongjia Liu, Wei Du, Yuan Jiang, Liupu Wang

Research Collection School Of Computing and Information Systems

With the widespread adoption of Internet Protocol (IP) communication technology and web-based platforms, cloud manufacturing has become a significant hallmark of Industry 4.0. Integrating graph algorithms into these web-enabled environments is crucial as they facilitate the representation and analysis of complex relationships in manufacturing processes, enabling efficient decision-making and adaptability in dynamic environments. As a key scheduling problem in cloud manufacturing, the flexible job-shop scheduling problem (FJSP) finds extensive applications in real-world scenarios. However, traditional FJSP-solving methods struggle to meet the efficiency and adaptability demands of cloud manufacturing due to generalization issues and excessive computational time, while reinforcement learning-based methods …

Framework For Integrating Industry Knowledge Into A Large Language Model To Assist Construction Cost Estimation, Prashnna Ghimire

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

The construction industry generates a large amount of data across projects produced by digital devices, tools, and methods, and this volume is rapidly increasing. However, the industry lags behind in adopting data-driven technologies. On the other hand, the rapid advancement of generative AI (GenAI) in recent years, especially state-of-the-art large language models (LLMs), shows great potential and has been increasingly adopted in many industries; however, the construction industry is behind in adoption. While academic studies have proposed various machine learning applications for construction, industry implementation has lagged due to a disconnect between these proof-of-concept developments and practical industry needs. Also, …

Multimodal Ai-Driven Biomarker For Early Detection Of Cancer Cachexia, Sabeen Ahmed

USF Tampa Graduate Theses and Dissertations

Cancer cachexia is a metabolic syndrome characterized by substantial skeletal muscle loss, impacting cancer patients' survival and quality of life. Despite its clinical significance, early detection remains a challenge due to the lack of standardized diagnostic criteria and the reliance on indirect markers. This work presents an AI-driven approach to enhance cachexia detection and monitoring by integrating multiple deep learning methodologies. We explore transformer architectures for time-series analysis to model sequential medical data, enabling disease prediction and progression modeling. To ensure robust and reliable decision-making in clinical settings, we explore Bayesian deep neural networks for uncertainty estimation. Additionally, we introduce …

Robust Spacecraft Autonomy For Deep Space Exploration In Special Euclidean Group Se(3), Matthew Wittal

Doctoral Dissertations and Master's Theses

Over the past half-century, humanity has gained extensive experience conducting manned spaceflight near Earth. Arguably, "near Earth" could even include the Moon — the most distant destination humans have reached. However, "near" in this work primarily refers low Earth orbit (LEO). One could argue that we have not truly left Earth since the Apollo, as spacecraft in some LEOs remain subject to atmospheric drag thus emphasizing their continued connection to Earth's immediate environment. Reflecting on this, it becomes clear that humanity has largely remained bound to Earth’s immediate vicinity since the Apollo missions reached the Moon. However, that is set …

The Asset Management Optimization Engine: An Ai And Machine Learning Model Approach To Pavement Asset Management, Matt Versdahl

USF Tampa Graduate Theses and Dissertations

While state Departments of Transportation (DOT) face major funding challenges, the need to find optimal ways to preserve and maintain pavement assets remains. Asset management employs a lowest cost lifecycle method to analyze asset costs and determine the best investment strategies to preserve it throughout its lifecycle. As new technology emerges, so do opportunities to leverage it. DOTs collect a significant amount of performance data on pavement and use it to decide how to keep it in a state of good repair. The literature in this area focuses on engineering techniques applied to treatment strategies. This dissertation research focuses on …

Role Of Eye-Tracking Technology And Software Algorithms In Enhancing Adhd Detection And Diagnosis: A Systematic Literature Review, Lauren E. Perkins

Honors College Theses

This systematic literature review explores the role of eye-tracking technology and software algorithms in enhancing the detection and diagnosis of ADHD. ADHD, a neurodevelopmental disorder affecting both children and adults, is traditionally diagnosed through behavioral assessments, which may lack objectivity. Recent studies suggest that eye-tracking, specifically focusing on saccades, fixations, and blink rates, offers the potential for more accurate and objective measures of ADHD. The review examines clinical trials, observational studies, and machine learning research to assess the correlation between ADHD and eye movement patterns. Results indicate that individuals with ADHD exhibit distinct eye movement patterns, which can be quantified …

Enhancement Of Hardware-In-Loop Simulation Ability For Homing Guidance Through Adaptive Field-Of-View Method, Shizheng Wan, Yu Cheng, Xu Zhang, Xuwei Fan

Journal of System Simulation

Abstract: Considering homing guidance test in the hardware-in-loop simulation, commands of flight simulator and antenna array are likely to exceed their ranges when the target vehicle maneuvers with a large cross range. To solve this problem, the adaptive field-of-view method is proposed to enhance simulation ability in laboratory. Inflight aircraft attitudes and missile-target line-of-sight angles are chosen as state parameters, and the optimal performance function can be established with maximum servo angle of both flight simulator and antenna array. Gradient descent algorithm is applied to acquire the optimal bias angles between the laboratory coordinate system and the launch inertial coordinate …

Research On Robot Path Planning Based On Improved Harris Hawks Algorithm, Yuxin Bai, Zhenya Chen, Ruitao Shi, Weitao Su, Zhuoqiang Ma, Shangjin Yang

Journal of System Simulation

Abstract: In order to improve the convergence accuracy of the HHO algorithm, this paper proposes a GSHHO(gold sine harris hawks optimization) algorithm based on multi-strategies. An infinite iterative chaotic map is used to initialize the population, and an elite reverse learning strategy is used to improve population quality; A convergence factor adjustment strategy is used to recalculate prey energy, balancing the global exploration and local development capabilities of the algorithm; In the development phase of Harris Eagle, the golden sine strategy was introduced to replace the original position update method and improve the local development ability of the algorithm; Experiments …