Earthquakes pose critical challenges for search and rescue (SAR), as narrow operational time windows and hazardous terrain constrain human capabilities - highlighting the need for robotic systems. Biomimetic snake robot systems offer a promising solution with their adaptability and flexibility in navigating irregular surfaces. Prior research has largely focused on mechanical design, with limited quantitative, task-specific evaluation of gaits in post-earthquake settings. To address this gap, we compare lateral undulation and sidewinding gaits, hypothesizing that their differing biomechanics yield distinct advantages across terrains. Using the Bullet Physics engine, we model a snake robot and simulate two SAR-inspired locomotion challenges: (1) a stepped ramp with repeating incline-flat patterns simulating collapsed flooring, and (2) a randomized heightfield terrain resembling concrete rubble. Performance metrics include cost of transport, contact stability, load distribution, and either completion time (Task 1) or success rate (Task 2). Results indicate sidewinding is more energy-efficient across both tasks while lateral undulation provides markedly better contact stability and more consistent load distribution Sidewinding completed Task 1 faster and lateral undulation achieved a higher success rate in Task 2. Additional evaluations showed that directional control parameters held a critical role in improving balance and reducing slippage for both gaits. These findings support informed gait selection in real deployments and advance the development of robust SAR-oriented snake robotics.
The decontamination of legacy nuclear or other hazardous waste sites is a critical task that exposes human workers to significant risks. This paper presents a robotic system designed to mitigate human exposure by enabling remote, operator-supervised soil sampling using a retrofitted industrial walking excavator. Our integrated approach allows a single operator at a safe distance to supervise the entire soil sampling mission, from site mapping to task execution.The system’s architecture seamlessly combines (1) a cloud-based SLAM framework for generating consistent and accessible site maps; (2) a software stack for traversability estimation and navigation, enabling robust mobility in unstructured terrain; and (3) a shared autonomy manipulation framework where the operator designates a target and the robot autonomously executes the complex, safety-critical soil sampling motion.This paper outlines the system design and reports on the successful validation of its core components. In a local testbed designed to simulate hazardous waste sites, we validated several key systems on the semi-autonomous excavator: the cloud-based SLAM pipeline, local 3D reconstruction for real-time environment modeling, intuitive Cartesian control of the end-effector, the autonomous sampling workflow, and the navigation stack. Field experiments subsequently validated all necessary components for the complete sampling workflow, demonstrating the viability of our shared autonomy approach and establishing a promising foundation for future field deployment.
This paper presents a robotics framework to support emergency entities, integrating a large language model-based multi-agent system (LLM-MAS) to manage the movements of uncrewed ground vehicles (UGVs) in search and rescue (SAR) operations. The system allows operators without robotics expertise to issue natural language voice commands to autonomous UGVs, ensuring their safe and context-aware navigation based on real-time mission data. The proposed ROS 2-based communication architecture integrates UGVs into existing SAR command hierarchies, featuring robust methods for message acknowledgment, prioritization, and operational window synchronization that enable effective coordination between human teams and autonomous robots. During the XIX Workshop on Security, Emergencies, and Catastrophes (Malaga, Spain), over 150 first responders participated in six parallel SAR missions, with some teams providing their real-time positional data via equipment-integrated sensors. The LLM-MAS used this data to make autonomous decisions on the ground, such as determining which SAR team the UGV should assist, and then sent the selected target location to an external path planner through a tool call. The planner generated a route to the target, which the UGV followed while its movements were monitored in real time through a web interface. The system successfully executed material transport tasks across three critical service routes that supported the six parallel missions. Direct feedback from first responders confirmed that our system improved mission efficiency and was praised for its intuitive, responsive performance in coordinated decision-making.
Reliable communication is critical for robotic teleoperation in Search and Rescue missions, particularly in challenging environments such as street canyons, underground garages, and industrial halls. While mesh networks, operating within unlicensed frequencies, extend range through multi-hop connectivity, they face interference, frequency competition, and reduced data rates per hop. Cellular technologies offer a promising alternative by leveraging dedicated frequency bands. However, standard-compliant Integrated Access and Backhaul (IAB) solutions are limited by hardware compatibility restrictions that result in vendor lock-in. This paper introduces LANTERN, an open multi-radio relay system that builds on the established SEAMLESS Multi-Link protocol, enabling interoperability across diverse communication technologies, including cellular and mesh networks. This architecture ensures reliable operation even in complex environments by combining established systems with modern ones without requiring substantial infrastructure changes. Initial validation experiments using a homogeneous 5G over-the-top relay demonstrate seamless transitions between the umbrella and relay networks, enabling uninterrupted immersive robotic teleoperation during mission-critical operations. In addition, a second case study demonstrates the functionality of heterogeneous relaying between a cellular umbrella and a mesh network, while highlighting potential future improvements to enhance handover and link usage when dealing with heterogeneous technologies.
This paper presents a heterogeneous aerial-ground robotic system for reliable communication and teleoperation in CBRNe (Chemical, Biological, Radiological, Nuclear, and Explosive) environments. The system follows a human-in-the-loop design where an operator teleoperates a ground vehicle equipped with a robotic manipulator, while multiple UAVs maintain wireless connectivity. One UAV is tethered to the UGV through an optical fiber providing a high-bandwidth link, and others form a Wi-Fi HaLow mesh network. To the best of the authors' knowledge, Wi-Fi HaLow has not yet been experimentally validated for real-time robotic teleoperation. Experiments demonstrated stable real-time teleoperation at a 300m distance, confirming the system's reliability in communication-challenged environments.
