Amirmehdi Yazdani

Efficient global localization of mobile robots in symmetrical indoor environments remains a formidable challenge, given the inherent complexities arising from uniform structures and a dearth of distinctive features. This review paper conducts an in-depth investigation into the nuances of global localization strategies, focusing on symmetrical environments, such as extended corridors, symmetrical rooms, tunnels, and industrial warehouses. The study comprehensively reviews and categorizes key techniques employed in this context, encompassing probabilistic-based approaches, learning-based approaches, Simultaneous Localization and Mapping (SLAM)-based approaches, and optimization-based approaches. The primary goal is to provide a contemporary and thorough literature review, offering insights into existing global localization solutions, followed by extant methods tailored for symmetrical indoor spaces. Also, the paper addresses practical challenges associated with implementing various global localization techniques, contributing to a holistic understanding of their real-world applicability. Comparative experimental results demonstrate that hybrid approaches achieve superior localization accuracy in symmetrical environments compared to any single method alone. These experiments, conducted in indoor settings with different symmetry levels, highlight the hybrid approach’s robustness and precision in resolving symmetry-induced ambiguities. This work signifies a significant step forward in mobile robot global localization, which addresses symmetrical environments’ complexities by leveraging the strengths of hybrid methodologies.

Reliable transfer of control policies from simulation to real-world robotic systems remains a central challenge in robotics, particularly for car-like mobile robots. Digital Twin (DT) technology provides a robust framework for high-fidelity replication of physical platforms and bi-directional synchronization between virtual and real environments. In this study, a DT-based testbed is developed to train and evaluate an imitation learning (IL) control framework in which a neural network policy learns to replicate the behavior of a hybrid Model Predictive Control (MPC)–Backstepping expert controller. The DT framework ensures consistent benchmarking between simulated and physical execution, supporting a structured and safe process for policy validation and deployment. Experimental analysis demonstrates that the learned policy effectively reproduces expert behavior, achieving bounded trajectory-tracking errors and stable performance across simulation and real-world tests. The results confirm that DT-enabled IL provides a viable pathway for Sim2Real transfer, accelerating controller development and deployment in autonomous mobile robotics.

This paper investigates the formation control of a disturbed air-ground multi-robot system consisting of unmanned aerial vehicles (UAVS) and unmanned ground vehicles (UGVs). An embedded collision-free formation control scheme, comprising two main components, is proposed. Firstly, a formation signal generator is developed based on distributed cooperative control approaches and artificial potential field-based methods. The generator, functioning as a virtual multi-agent system through a network of second-order integrator-chain dynamic nodes, is designed to “achieve” the formation control task while ensuring some specific constraints. Secondly, by embedding the corresponding node within each robot and taking its outputs as the tracking references, distinct constrained tracking controllers are designed for the UAVs and UGVs, respectively. Additionally, some finite-time disturbance observers are also employed to estimate and compensate for the lumped disturbances of UAVs and UGVs. With these two components, the proposed embedded formation control scheme completes the desired formation shape asymptotically for the air-ground UAVs-UGVs system under disturbances, while the collisions are successfully avoided between any two UAVs or two UGVs. Rigorous stability analysis and comparative simulations demonstrate the effectiveness of the proposed control scheme.

Accurate localization is a critical requirement for the successful deployment of mobile robots in indoor environments, particularly those characterized by symmetrical structures and features. Symmetrical indoor environments pose unique localization challenges due to the presence of repeated patterns and structures that can confound traditional localization methods. In such environments, accurately estimating the robot's pose relative to a global reference frame becomes increasingly challenging, leading to potential errors and inefficiencies in robot navigation and task execution. This paper presents an improved deep learning-based Monte Carlo localization (DMCL) framework for global localization of a mobile robot in symmetrical indoor environment using only 2D lidar. We first, converted 2D laser data to single channel 2D projected image and an occupancy grid. This 2D projected image is used to train the neural network to regress the 3DOF of robot. Finally, we integrated this trained neural network which estimate the robot pose in environment with MCL in the weight updating stage. The performance of both Monte Carlo Localization (MCL) and in DMCL methods in symmetrical indoor environment is investigated through extensive simulation studies. Verifying the effectiveness of the proposed method our network is able to obtain position accuracy of 0.15m and scene classification accuracy to 99% in simulation.

This paper proposes a novel method to tackle the growing problem of system instability in microgrids, which is brought on by the widespread adoption of renewable energy sources (RESs) and distributed generators (DGs). Connecting RESs and DGs to microgrids through power electronic interfaces leads to a decrease and fluctuation in the system's inertia. This reduction in inertia leads to increased uncertainty and instability in the system, necessitating either load shedding or the curtailment of renewable generation, which are undesirable for both utilities and customers. To address these challenges, this paper provides a novel approach for enhancing inertia in microgrids to avoid any instability or load shedding. This approach introduces a multi‐objective optimization algorithm based on the non‐dominated sorting genetic algorithm II (NSGA‐II), implemented in a co‐simulation platform combining MATLAB and DIgSILENT PowerFactory. The algorithm simultaneously determines the minimum required capacity of battery energy storage systems (BESSs) acting as virtual inertia and their optimum droop coefficients to stabilize the grid and prevent load shedding. The proposed approach is formulated within the context of a multi‐objective optimization algorithm, by utilizing the NSGA‐II in an integrated DIgSILENT and MATLAB framework. The simulation results show the positive results of the proposed approach in stabilizing the microgrid of Broome city and avoiding any load shedding. The method is validated under three critical scenarios: a 0.97 MW step load increase, a cloud event reducing PV output by 49%, and a synchronous generator outage. Results show that the optimized BESS configuration successfully maintains frequency stability and avoids any load shedding. The minimum sizing of BESSs and their optimum droop coefficient are obtained for different scenarios including step load change, cloud event, and synchronous generator outage. Compared to conventional approaches, the proposed method significantly reduces the required BESS capacity while ensuring compliance with frequency nadir and RoCoF constraints. This approach provides a cost‐effective and scalable decision‐making tool for microgrid operators to enhance system resilience and customer satisfaction. The outcome of this research is a critical decision‐making tool for the microgrid owner to cost‐effectively decide the virtual inertia sizing and their parameters for stabilization of microgrid and to improve customer satisfaction by avoiding load shedding. This contribution bridges the gap between academic research and field‐level implementation, offering a scalable and adaptable strategy for future renewable‐rich microgrids.

Multi-robot task allocation and coalition formation are critical challenges in robotics, essential for applications such as disaster response, search and rescue, environmental monitoring, exploration and mapping, surveillance and security, logistics, agriculture, military operations and healthcare. Therefore, it is essential to address these challenges and develop optimal solutions for implementing these concepts in real-world scenarios to effectively execute the previously mentioned applications. Hence, this paper presents a comprehensive survey and comparative analysis of different approaches for allocating tasks to multiple robots and forming coalitions to accomplish these tasks efficiently. The paper first provides a systematic categorization of the existing methods into four different groups namely behavior-based, market-based, optimization-based, and learning-based methods. Next, it analyzes the trade-off between different objectives, including minimizing task completion time, maximizing resource utilization, and balancing workload among robots. The paper also explores the impact of robot heterogeneity, task dependencies, and communication constraints on the performance of various algorithms. Furthermore, it discusses the challenges of dynamic task allocation and coalition formation in response to changes in the environment or robot failures.
Accordingly, the paper presents a comprehensive comparative study of the surveyed approaches, highlighting their substantial features including limitations and suitability for different application scenarios. As such, the paper identifies promising research directions, including the integration of machine learning techniques and the development of hybrid algorithms. Through this systematic analysis, the main aim is to provide researchers with a comprehensive understanding of the state-of-the-art in multi-robot task allocation and coalition formation, enabling them to select the most appropriate approach for their specific requirements.

This paper presents the optimal sizing of solar photovoltaic (PV) and battery energy storage systems (BESs) for grid‐connected houses with electric vehicles (EVs) by considering vehicle‐to‐home (V2H) operation. To minimise the cost for residential households, particle swarm optimisation (PSO) is utilised, and a novel rule‐based home energy management system (HEMS) is implemented. Stochastic functions are used to investigate the uncertainties regarding the availability of EVs based on arrival and departure times and initial state of charge (SOC). The impact of V2H integration, maximum daily energy export, and electricity costs are thoroughly examined through simulations and sensitivity analysis. The simulation results confirm that utilising V2H operation in a grid‐connected household can eliminate the need for a BES while still reducing electricity costs about 5% compared to a household equipped with PV, BES, and EV without V2H operation.

The Australian and global agricultural industries are experiencing economic challenges. These challenges to farming are caused by a group of related factors including lack of labour supply, low profit margins, and high operational costs. Of all the factors impacting upon the operation and profitability of farms, it is human labour and chemical pesticides which are most effectual. One potential solution which has been proposed to reduce the impact of said factors is the use of robotic labour on farms. However, the available robotic crop disease classification models are lacking in accuracy and efficiency for this solution to be commercially viable. Therefore, this research aims to further understand what progress needs to be made in order to increase the accuracy and efficiency of crop disease classification models, specifically for disease species classification and disease severity classification purposes.
As part of this research, previous literature will be examined to determine the current scope of automatic intelligent crop disease classification capabilities. Subsequently, three State-of-the-Art (SotA) image classification models (AlexNet, MobileNetv3, and MLP-Mixer) will be trained and tested to determine the current standard of performance for crop disease species classification. Subsequently, this research will determine, based upon model performance of currently existing models, which model architecture produces favourable results for crop disease severity classification.

This paper presents the development of an efficient and smooth path planning algorithm tailored for autonomous systems operating in static environments. A modified bi-directional and goal-oriented rapidly-exploring random tree (RRT) algorithm is proposed, generating an initial rough global path quickly. To further enhance the quality of this path, we introduce a two-step optimization process, involving down-sampling to reduce redundant waypoints and up-sampling to improve path resolution. A cubic curve smoothing technique is then applied to ensure the path maintains continuity and remains collision-free, even in dense, clustered environments. The algorithm is validated by simulations in environments that approximate real-world conditions using MATLAB. This work primarily focuses on improving computational efficiency and path smoothness, addressing key challenges in robotic path planning.

The paper analyses the Electromagnetic Transient (EMT) stability analysis of a modified Medium Voltage (MV). This Microgrid (MG) comprises a Diesel Generator (DG) and a Photovoltaic (PV) source under balanced load conditions. The system model was operated in a stable grid-connected mode with only DG's, but the authors proposed a new system with DG and PV in an islanded operation scenario. Islanded MG must operate in a stable mode where controllers can take prompt action in the fault event and make the system more reliable. In this regard, the droop control and PI control manage DG's active power and PV system's power operation, respectively. The Western Electricity Coordination Council (WECC) plant control is utilised as the benchmark model with PV only for a large-scale PV plant model. The control parameters are optimised for both machines to improve the system's dynamic response. The effectiveness and robustness of the MV islanded system are examined under different operating conditions through extensive simulation studies in the DigSILENT PowerFactory software.