News & Research

High levels of electric vehicle (EV) penetration and load uncertainty pose significant challenges to traditional voltage regulation in distribution systems (DS), particularly concerning voltage drops. This study investigates the potential of a dual-service mode (V2V/V2G) for mobile charging using multiple charging robots, cooperating with conventional DS voltage regulation methods to enhance performance. Specifically, we propose a multi-robot adaptive charging network (MRACN) embedded in a three-level voltage regulation framework to address voltage violations. At the low level, we introduce a voltage security rule-based dispatch strategy for MRACN to mitigate EV-induced voltage drops. At the mid-level, we optimize day-ahead scheduling of on-load tap changers and capacitor banks via optimal power flow for long-term voltage regulation. At the high level, we employ an unscented Kalman filter-based deep reinforcement learning (UKF-DRL) approach to integrate MRACN into DSs for real-time voltage control. Experimental results demonstrate that MRACN effectively reduces voltage violations and enhances the flexibility of conventional voltage control systems. Moreover, the UKF-DRL method accelerates DRL training by nearly an order of magnitude compared to full AC power-flow solutions, with the overwhelming majority of state estimates deviating by no more than 2%.

This paper presents an Agentic AI-based framework for automated safety analysis of humanoid and service robots using their CAD designs. The framework integrates multiple specialized AI agents and tools that collaboratively import and analyze CAD models to detect and segment hazardous features such as sharp edges, gaps, holes, pinch points, and exposed moving parts (e.g., limbs, wheels, gears, and motors). By combining vision-language models (VLMs), geometric analysis, and semantic segmentation, the system evaluates compliance with international safety standards. Identified semantic features such as gaps, holes, accessible areas, sharp edges, slots, and pinch points are correlated with ISO defined safety thresholds, enabling the AI agents to assess compliance, estimate the likelihood of injury or entrapment, and determine whether the robot's CAD-based form factors meet safety requirements. This work defines key Agentic AI use cases, outlines the design and workflow of collaborating agents and tools, and proposes an integrated design-support system for early-stage robot development, enabling proactive hazard analysis & risk assessment throughout product design life cycle.

This paper presents NAVBOT25, a labelled dataset aimed at strengthening the overall security of autonomous robots against network-based cyber threats within the Robot Operating System (ROS) platform. NAVBOT25 offers a comprehensive, labelled dataset that captures both normal operational behaviour and a variety of attack scenarios relevant to ROS-based systems. This dataset was generated by deploying a TurtleBot3 running ROS Noetic in controlled laboratory setting, where real-world attack vectors were executed —including SSH brute-force attempts, reverse shells, port scans, and ROS-specific attacks such as unauthorized publishing actions and topic flooding. Network traffic was captured using tcpdump, and 83 flow-level features were extracted using CICFlowMeter, resulting in a series of CSV files. Designed to support the development of AI-assisted intrusion detection systems, NAVBOT25 addresses existing gaps in robotic cybersecurity research by providing a richer and more diverse dataset for evaluating threat detection in networked robotic systems.

Diffusion-based reinforcement learning dem effectiveness of the proposed algorithm is validated through simulations on the MuJoCo platform and experiments on a physical robotic platform. The relevant code for this study is publicly available on GitHub: https://github.com/zhn-ya/diffusion-RL.

The rapid growth of robotics and embedded system technologies has increased the need for intelligent and userfriendly robotic systems. Conventional robot control methods such as remote controls, keyboards, and programmed instructions often make human-robot interaction difficult and less efficient. To overcome these limitations, this project proposes the design and development of a Voice Controlled Humanoid Robot that can understand human voice commands and perform corresponding actions.The proposed system uses speech recognition technology to convert voice commands into digital signals. These commands are transmitted through Bluetooth communication to a microcontroller such as Arduino or ESP32, which processes the commands and controls servo motors through a motor driver circuit. Based on the received instructions, the humanoid robot performs movements such as walking, turning, hand movements, and other predefined actions.The main objective of this project is to create a simple, low-cost, and efficient robotic system that improves natural communication between humans and robots. The system provides hands-free robot control, making it suitable for educational purposes, assistive technologies, automation systems, and future smart robotic applications. The project demonstrates the practical implementation of embedded systems, wireless communication, and voice recognition technologies in humanoid robotics.