Simon X. Yang

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Verified

Simon verified their affiliation via an institutional email.

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• Ph.D.
• Professor (Full) at University of Guelph

This paper presents a novel knowledge-based genetic algorithm to generate a collision-free path in complex environments. The proposed algorithm infuses specific domain knowledge into robot path planning through the development of five problem-specific operators that integrate a local search technique to improve efficiency. In addition, the proposed...

Fish schools present high-efficiency group behaviors through simple individual interactions to collective migration and dynamic escape from the predator. The school behavior of fish is usually a good inspiration to design control architecture for swarm robots. In this article, a novel fish-inspired self-adaptive approach is proposed for collective...

This paper investigated the distributed leader follower formation control problem for multiple differentially driven mobile robots. A distributed estimator is first introduced and it only requires the state information from each follower itself and its neighbors. Then, we propose a bioinspired neural dynamic based backstepping and sliding mode cont...

In the past decades, considerable attention has been paid to bio-inspired intelligence and its applications to robotics. This paper provides a comprehensive survey of bio-inspired intelligence, with a focus on neurodynamics approaches, to various robotic applications, particularly to path planning and control of autonomous robotic systems. Firstly,...

This article addresses distributed robust learning-based control for consensus formation tracking of multiple underwater vessels, in which the system parameters of the marine vessels are assumed to be entirely unknown and subject to the modeling mismatch, oceanic disturbances, and noises. Toward this end, graph theory is used to allow us to synthes...

Citation: Chen, S.; Feng, T.; Li, J.; Yang, S.X. Research on Intelligent Path Planning of Mobile Robot Based on Hybrid Symmetric Bio-Inspired Neural Network Algorithm in Complex Road Environments. Symmetry 2025, 17, 836. https:// Abstract: To address the intelligent path planning challenges faced by mobile robots operating in complex road environme...

Southwestern China has abundant hydropower networks, wherein neighboring cascade hydropower stations within the same river basin are typically connected to the power system in a chain-structured configuration. However, when such chain-structured cascade hydroelectric power plants (CC-HPPs) participate in automatic voltage control (AVC), problems su...

Traversable area detection is essential for autonomous robot navigation, enabling robots to identify safe areas, plan optimal paths, and avoid obstacles. In complex indoor environments, this task is particularly challenging due to various obstacles and dynamic changes. In this study, an improved traversable area detection model for indoor robot is...

Rapid population growth and climate change have created challenges for managing water quality. Protecting water sources and devising practical solutions are essential for restoring impaired inland rivers. Traditional water quality monitoring and forecasting methods rely on labor-intensive sampling and analysis, which are often costly. In recent yea...

Trust plays an important role and significantly influences human-robot collaborations (HRC). However, most previous research on trust only emphasizes the human attitude toward robots. There needs more understanding of human uncertainties that may also cause disruptions of trust in collaborations. This paper presents a novel mutual trust framework t...

This study addresses the dual challenges of complex road environments and diverse task-safety requirements in mobile-robot path planning by proposing an innovative method that integrates a dual-layer fuzzy control system with an improved genetic algorithm. Initially, an expert system-based dual-layer fuzzy control system is developed. The first lay...

Traditional pig weighing methods are costly, require driving pigs onto electronic scales, and cannot collect real-time data without interference. Pig weight estimation using deep learning often demands significant computational resources and lacks real-time capabilities, highlighting the need for a more efficient method. To overcome these challenge...

Accurate citrus disease identification is essential for targeted orchard pesticide application. Current models struggle with accuracy and efficiency due to diverse leaf lesion patterns and complex orchard environments. This study presents YOLOv8n-DE, an improved lightweight YOLOv8-based model for enhanced citrus disease detection. It introduces the...

Embodied artificial intelligence (AI) is reshaping the landscape of intelligent robotic systems, particularly by providing many realistic solutions to execute actions in complex and dynamic environments. However, Embodied AI requires a huge data generation for training and evaluation to ensure safe interaction with physical environments. Therefore,...

The application of handling robots in industrial environments has always been a research hotspot. This paper proposes a positioning scheme for handling robots based on improved adaptive Monte Carlo (AMCL) fusion of multiple sensors and QR code assistance, which can achieve high-precision positioning under low-cost conditions in industrial environme...

Industrial robots can cause servo system instability during operation due to friction between joints and changes in end loads, which results in jittering of the robotic arm. Therefore, this paper proposes a hybrid sparrow search algorithm (HSSA) method for PID parameter optimization. By studying the optimization characteristics of the genetic algor...

The damage identification of arch bridges plays a vital role in the structural condition assessment and maintenance decision-making. However, traditional deep learning-based methods confront the issue of insufficient accuracy and high requirements of training samples. To this end, this paper proposes an arch bridge damage identification method base...

Flocking control, as an essential approach for survivable navigation of multirobot systems, has been widely applied in fields, such as logistics, service delivery, and search and rescue. However, realistic environments are typically complex, dynamic, and even aggressive, posing considerable threats to the safety of flocking robots. In this article,...

Indoor layout estimation is a critical aspect of indoor scene understanding, aiming to recover and reconstruct the geometric structure information of indoor spaces by analyzing images or depth data. The indoor layout estimation is a challenging task, due to the complexity of the indoor environment, including the unstructured geometric construction...

Formation control of unmanned underwater vehicles (UUVs) is of great significance to the Internet of Underwater Things (IoUT). Currently, various methods are employed for UUV formation control. Among them, rigid graph-based approaches, which rely solely on relative information between UUVs, are particularly suitable for underwater environments. How...

The stable physiological structure and rich vascular network of pig ears contribute to distinct thermal characteristics, which can reflect temperature variations. While the temperature of the pig ear does not directly represent core body temperature due to the ear’s role in thermoregulation, thermal infrared imaging offers a feasible approach to an...

The stable physiological structure and rich vascular network of pig ears more easily form distinct thermal characteristics, representing pig body temperature. Using thermal infrared imaging to identify pig ears offers a feasible approach to analyzing individual pig health status. Based on this background, a dataset comprising 23,189 thermal infrare...

This review examines the integration of remote sensing technologies and machine learning models for efficient monitoring and management of lake water quality. It critically evaluates the performance of various satellite platforms, including Landsat, Sentinel-2, MODIS, RapidEye, and Hyperion, in assessing key water quality parameters including chlor...

Daily inspections of individual laying hens in large-scale egg farms are both labor-intensive and time-consuming, requiring farm staff to manually check each caged hen and promptly remove any deceased birds to prevent the spread of disease within the attery cages. To streamline this process, a specialized robot has been developed to enhance inspect...

Collaboration is one of the most important factors in multirobot systems. Considering certain real-world applications and to further promote its development, we propose a new benchmark to evaluate multirobot collaboration in target trapping environment (T2E). In T2E, two kinds of robots (called captor robot and target robot ) share the same spa...

Call for papers https://www.mdpi.com/topics/WV723OIDW6

The body size, shape, weight, and scoring of goats are crucial indicators for assessing their growth, health, and meat production. The application of computer vision technology to measure these parameters is becoming increasingly prevalent. However, in real farm environments, obstacles, such as fences, ground conditions, and dust, pose significant...

Observer-based control is the most commonly used method in the control of electro-hydraulic servo system (EHSS) with uncertainties, but it suffers from the drawback of low accuracy under the influence of large external load forces and disturbances. To address this problem, this paper proposes a novel compensation function observer-based backsteppin...

Path planning of multi-unmanned aerial vehicles (multi-UAVs) in complex urban environments is a challenging task, which suffers from low autonomous decision-making capability and execution efficiency. Traditional methods are struggling to cope with the increasing task demands. Deep reinforcement learning (DRL) offers a more flexible and efficient s...

Considering animal welfare, the free-range laying hen farming model is increasingly gaining attention. However, in some countries, large-scale farming still relies on the cage-rearing model, making the focus on the welfare of caged laying hens equally important. To evaluate the health status of caged laying hens, a dataset comprising visible light...

Space‐wall‐climbing robots face the challenge of stably attaching to and moving on spacecraft surfaces, which include smooth flat areas and rough intricate surfaces. Although adhesion‐based wall‐climbing robots demonstrate stable climbing on smooth surfaces in outer space, there is scarce research on their stable adhesion on rough surfaces within a...

The condition degradation prediction for network-level bridges is conducive to the decision-making of bridge maintenance. However, traditional prediction methods mainly belong to shallow neural networks and have difficulty in extracting the common degradation features of network-level bridges, thus obtaining a limited prediction accuracy. To this e...

Image stitching is the synthesis of multiple partial image segments into a complete and continuous panoramic image through effective image alignment and seamless fusion techniques. It can achieve a wider field of view and richer information for display and analysis. Most deep learning-based image stitching methods have significant advantages in imp...

Observer-based control is the most commonly used method in the control of electro-hydraulic servo system (EHSS) with uncertainties, but it suffers from the drawback of low accuracy under the influence of large external load forces and disturbances. To address this problem, this paper proposes a novel compensation function observer-based backsteppin...

Considering animal welfare, the free-range laying hen farming model is increasingly gaining attention. However, in some countries, large-scale farming still relies on the cage-rearing model, making the focus on the welfare of caged laying hens equally important. To evaluate the health status of caged laying hens, a dataset comprising visible light...

The paper presents an innovative approach (CBNNTAP) that addresses the complexities and challenges introduced by ocean currents when optimizing target assignment and motion planning for a multi-unmanned underwater vehicle (UUV) system. The core of the proposed algorithm involves the integration of several key components. Firstly, it incorporates a...

The research on automatic monitoring methods for greenhouse gases and hazardous gas emissions is currently a focal point in the fields of environmental science and climatology. Until 2023, the amount of greenhouse gases emitted by the livestock sector accounts for about 11–17% of total global emissions, with enteric fermentation in ruminants being...

Multi-robot cooperative navigation is an important task, which has been widely studied in many fields like logistics, transportation, and disaster rescue. However, most of the existing methods either require some strong assumptions or are validated in simple scenarios, which greatly hinders their implementation in the real world. In this paper, mor...

The sailfish possesses outstanding motion performance among marine species, while the robotic fish using the sailfish as a bionic object has received little attention. In this paper, the shape structure and motion characteristics of sailfish are observed, and a bionic sailfish robot is designed that can perform cooperative motion through the dorsal...

Considering the increased risk of urban flooding and drought due to global climate change and rapid urbanization, the imperative for more accurate methods for streamflow forecasting has intensified. This study introduces a pioneering approach leveraging the available network of real-time monitoring stations and advanced machine learning algorithms...

This article focuses on the trajectory tracking control for a perturbed wheeled mobile robot (WMR) with slipping and skidding. The external disturbances and uncertainties caused by the slipping and skidding compose the “total” disturbance. By analyzing the stable state of the controlled system, the WMR reference model driven by a favorable disturba...

Natural disasters and urban accidents drive the demand for rescue robots to provide safer, faster, and more efficient rescue trajectories. In this article, a feature learning-based bio-inspired neural network (FLBBINN) is proposed to quickly generate a heuristic rescue path in complex and dynamic environments, as traditional approaches usually cann...

Natural disasters and urban accidents drive the demand for rescue robots to provide safer, faster, and more efficient rescue trajectories. In this paper, a feature learning-based bio-inspired neural network (FLBBINN) is proposed to quickly generate a heuristic rescue path in complex and dynamic environments, as traditional approaches usually cannot...

Fish schools present high-efficiency group behaviors through simple individual interactions to collective migration and dynamic escape from the predator. The school behavior of fish is usually a good inspiration to design control architecture for swarm robots. In this paper, a novel fish-inspired self-adaptive approach is proposed for collective es...

In complex and dynamic environments, traditional pursuit–evasion studies may face challenges in offering effective solutions to sudden environmental changes. In this paper, a bio-inspired neural network (BINN) is proposed that approximates a pursuit–evasion game from a neurodynamic perspective instead of formulating the problem as a differential ga...

Dissolved oxygen (DO) concentration is a pivotal determinant of water quality in freshwater lake ecosystems. However, rapid population growth and discharge of polluted wastewater, urban stormwater runoff, and agricultural non-point source pollution runoff have triggered a significant decline in DO levels in Lake Erie and other freshwater lakes loca...

Human-computer interaction is a discipline that studies the interaction between computer systems and users. Nowadays, it has become an important research topic in the field of robotics, especially in the field of auxiliary robots. As gestures can express rich information, gesture recognition is widely used in machine control, smart home, etc. Visio...

Sequential recommendation systems in cold-start scenarios aim to provide recommendations as accurately as possible for users with sparse behavior, which is a challenging issue in this field. Recently, meta-learning algorithms have been introduced into the cold-start recommendations and have obtained some better results. However, most of these meta-...

This paper proposes a novel intelligent approach to swarm robotics, drawing inspiration from the collective foraging behavior exhibited by fish schools. A bio-inspired neural network (BINN) and a self-organizing map (SOM) algorithm are used to enable the swarm to emulate fish-like behaviors such as collision-free navigation and dynamic sub-group fo...

In complex environments, mobile robots performing tasks with different hazard levels need to consider different road factors, this paper proposes a functional model correlating task hazard levels with road factors, proposing an innovative Hybrid Adaptive Genetic Algorithm (HAGA). The HAGA integrates an optimized two-optimization (2-opt) operator* w...

This paper addresses the challenges of distributed formation control in multiple mobile robots, introducing a novel approach that enhances real-world practicability. We first introduce a distributed estimator using a variable structure and cascaded design technique, eliminating the need for derivative information to improve the real time performanc...

A remote and non-contact tissue regeneration tracking technique is of great significance in regenerative medicine for monitoring cell ingrowth, revascularization and remodeling of the implanted biological scaffold. The present study has explored the bimodal hyperspectral imaging system to study quantitatively the action of a scaffold-induced tissue...

Deep learning-based object detection methods have achieved significant results in UAV crop seedling image detection. However, when there are differences in the shape characteristics and sizes of seedlings within datasets, the performance of the detector tends to decrease. Existing methods typically rely on specific datasets, ignoring the problem of...

Knee osteoarthritis (KOA), a common musculoskeletal disorder, is typically diagnosed by assessing patients’ X-rays. This method may have potential implications for health, especially with long-term and repeated examinations. In this study, we propose a KOA severity prediction (SP) model that utilizes bi-modal data, thermal image combined with perso...

Unmanned surface vehicles (USVs) and unmanned aerial vehicles (UAVs) have been widely applied in maritime operations. However, challenges persist in the formation control and joint operations of such heterogeneous unmanned systems. This article presents a novel cooperative system design of USVs and UAVs aimed at synchronized cooperative tasks and p...

Multiple autonomous vehicles (MAVs) enhance efficiency and task execution compared to a single vehicle. Real-world applications necessitate MAVs to safely navigate in dynamic formation along planned trajectories, while sensing, mapping, and avoiding obstacles. Addressing the need for trajectory adaptation amidst real-world scenarios, a safety-aware...

Climate change and urbanization have increased the frequency of floods worldwide, resulting in substantial casualties and property loss. Accurate flood forecasting can offer governments early warnings about impending flood disasters, giving them a chance to evacuate and save lives. Deep learning is used in flood forecasting to improve the timelines...

Rays have shown superior ocean swimming performance, but the underwater robots that use rays as bionic objects still need to be improved in terms of forward speed. In this paper, we observe and investigate the structure and motion characteristics of rays, and design a bionic ray robot driven by two pairs of serial pectoral fins. In Addition, theore...

Intelligent escape is an interdisciplinary field that employs artificial intelligence (AI) techniques to enable robots with the capacity to intelligently react to potential dangers in dynamic, intricate, and unpredictable scenarios. As the emphasis on safety becomes increasingly paramount and advancements in robotic technologies continue to advance...

Plant diseases pose a significant threat to the economic viability of agriculture and the normal functioning of trees in forests. Accurate detection and identification of plant diseases are crucial for smart agricultural and forestry management. In recent years, the intersection of agriculture and artificial intelligence has become a popular resear...

In this article, we investigate the scheduling issue of diesel generators (DGs) in an Internet of Things (IoT)-Driven isolated microgrid (MG) by deep reinforcement learning (DRL). The renewable energy is fully exploited under the uncertainty of renewable generation and load demand. The DRL agent learns an optimal policy from history renewable and l...

Robust constrained formation tracking control of underactuated underwater vehicles (UUVs) fleet in 3-D space is a challenging but practical problem. To address this problem, this article develops a novel consensus-based optimal coordination protocol and a robust controller, which adopts a hierarchical architecture. On the top layer, the spherical c...

This paper addresses distributed robust learning-based control for consensus formation tracking of multiple underwater vessels, in which the system parameters of the marine vessels are assumed to be entirely unknown and subject to the modeling mismatch, oceanic disturbances, and noises. Towards this end, graph theory is used to allow us to synthesi...

Robust constrained formation tracking control of underactuated underwater vehicles (UUVs) fleet in three-dimensional space is a challenging but practical problem. To address this problem, this paper develops a novel consensus based optimal coordination protocol and a robust controller, which adopts a hierarchical architecture. On the top layer, the...

Collaboration is one of the most important factors in multi-robot systems. Considering certain real-world applications and to further promote its development, we propose a new benchmark to evaluate multi-robot collaboration in Target Trapping Environment (T2E). In T2E, two kinds of robots (called captor robot and target robot) share the same space....

Semantic segmentation is a hot research issue in the field of image processing. The introduction of depth images improves the effect of semantic segmentation. However, most existing methods do not take into account the differences between RGB and depth features, leading to poor segmentation accuracy. To fully utilize the RGB and depth features, an...

Buildings play an indispensable role in urban development [...]

Simultaneous localization and mapping (SLAM) is paramount for unmanned systems to achieve self-localization and navigation. It is challenging to perform SLAM in large environments, due to sensor limitations, complexity of the environment, and computational resources. We propose a novel approach for localization and mapping of autonomous vehicles us...

Quadrotor unmanned aerial vehicles have become the most commonly used flying robots with wide applications in recent years. This paper presents a bioinspired control strategy by integrating the backstepping sliding mode control technique and a bioinspired neural dynamics model. The effects of both disturbances and system and measurement noises on t...

Accurately predicting the magnitude and timing of floods is an extremely challenging problem for watershed management, as it aims to provide early warning and save lives. Artificial intelligence for forecasting has become an emerging research field over the past two decades, as computer technology and related areas have been developed in depth. In...

3D object detection has received extensive attention from researchers. RGB-D sensors are often used for the information complementary in 3D object detection tasks due to their easy acquisition of aligned point cloud and RGB image data, relatively reasonable prices, and reliable performance. However, how to effectively fuse point cloud data and RGB...

The OpenAI chatbot ChatGPT has achieved unprecedented success since its launch in November 2022. The Artificial Intelligence (AI) technologies behind ChatGPT are expected to have far-reaching effects on various technological fields beyond natural language processing. This editorial discusses the potential benefits and challenges that ChatGPT may br...

Simultaneous localization and mapping (SLAM) is paramount for unmanned systems to achieve self-localization and navigation. It is challenging to perform SLAM in large environments, due to sensor limitations, complexity of the environment, and computational resources. We propose a novel approach for localization and mapping of autonomous vehicles us...

This paper investigated the distributed leader follower formation control problem for multiple differentially driven mobile robots. A distributed estimator is first introduced and it only requires the state information from each follower itself and its neighbors. Then, we propose a bioinspired neural dynamic based backstepping and sliding mode cont...

In this paper, we investigate the scheduling issue of diesel generators (DGs) in an Internet of Things (IoT)-Driven isolated microgrid (MG) by deep reinforcement learning (DRL). The renewable energy is fully exploited under the uncertainty of renewable generation and load demand. The DRL agent learns an optimal policy from history renewable and loa...

This paper presents a comprehensive overview of recent developments in formation control of multiple autonomous underwater vehicles (AUVs). Several commonly used structures and approaches for formation coordination are listed, and the advantages and deficiencies of each method are discussed. The difficulties confronted in synthesis of a practical A...

This paper proposes an intelligent fault-tolerant control (FTC) strategy to tackle the trajectory tracking problem of an underwater vehicle (UV) under thruster damage (power loss) cases and meanwhile resolve the actuator saturation brought by the vehicle's physical constraints. In the proposed control strategy, the trajectory tracking component is...

The images acquired by a single visible light sensor are very susceptible to light conditions, weather changes, and other factors, while the images acquired by a single infrared light sensor generally have poor resolution, low contrast, low signal-to-noise ratio, and blurred visual effects. The fusion of visible and infrared light can avoid the dis...

This paper proposes an intelligent fault-tolerant control (FTC) strategy to tackle the trajectory tracking problem of an underwater vehicle (UV) under thruster damage (power loss) cases and meanwhile resolve the actuator saturation brought by the vehicle’s physical constraints. In the proposed control strategy, the trajectory tracking component is...

The indoor scene object detection technology is of important research significance, which is one of the popular research topics in the field of scene understanding for indoor robots. In recent years, the solutions based on deep learning have achieved good results in object detection. However, there are still some problems to be further studied in i...

Bionic soft robotic hand has been developed rapidly, as it can achieve considerable flexibility and mimic human hand to perform actions such as grasping. This study develops a novel bionic soft robotic hand, which consists of a palm and five fingers that can operate independently. Unlike other soft hands, the fingers of the developed soft robotic h...