A Heterogeneous Unmanned Ground Vehicle and Blimp Robot Team for Search and Rescue using Data-driven Autonomy and Communication-aware Navigation

Abstract

This paper describes the architecture and implementation of a heterogeneous team comprising unmanned ground vehicles and blimp robots capable of navigating unknown subter- ranean environments for search and rescue missions. The ground vehicles are equipped with a range of sensors for accurate perception, localization, and mapping. The blimps feature a long flight duration and collision tolerance when traversing uneven terrain. The design of the system was meant to satisfy the requirements of the Defense Advanced Research Projects Agency (DARPA) Subterranean Challenge in terms of perception capability and autonomy. To facilitate navigation through smoke-filled spaces, we employed novel millimeter wave radar to enable cross-modal representations for integration via deep reinforcement learning. The autonomy of the proposed scheme was augmented using simulations to train deep neural networks, thereby allowing the system to perform sequential decision-making for collision avoidance and navigation toward a specific goal. The navigation system was evaluated in the DARPA SubT Urban Circuit, and quantitative localization results and recovery strategy from failures was discussed. The proposed communication system comprises mesh WiFi with XBee (ZigBee network with XBee radios) and ultra-wideband (UWB) communication modules as well as spherical nodes that can be shot out like a cannonball and miniature cars deployed as mobile nodes. The propagation and radio signal strength index of various modules were evaluated using data collected during field tests in order to overcome the uncertainties of subterranean environments, including non-line-of-sight propagation, multipath propagation, and fading reception. We also discuss the lessons learned during this project and reflect on future plans.

UAVs Beneath the Surface: Cooperative Autonomy for Subterranean Search and Rescue in DARPA SubT

“…Although team NCTU did not participate in the Final Event, their solution (Chen-Lung et al, 2022) to the tunnel and urban circuit showcased originality in the form of autonomous visually localized blimps (Huang et al, 2019). Their navigation was based on policies learned by deep reinforcement learning with simulation-to-world transfer.…”

Lessons learned: Symbiotic autonomous robot ecosystem for nuclear environments

“…A heterogeneous unmanned ground vehicle and blimp robot team for search and rescue using data‐driven autonomy and communication‐aware navigation [72] 1x Blimp UAV 1x Clearpath wheeled Jackal 2x Clearpath wheeled Husky 1x Spherical robot 1x Race car robot wheeled …”

“…The competition states that during the mission, a single human operator is only permitted to oversee and interact with the mission from a dashboard interface. Combining this operation requirement with the need for several robotic platform types to be deployed, this often resulted in operator overload when challenges in mission resilience were faced during artefact detection [72]. In several cases, the robots often had to be teleoperated to overcome resilience challenges such as the unstructured environments presented in each segment of the course [65,71,73,74].…”

Robotic Arm Control System Based on Brain-Muscle Mixed Signals

“…As a type of lighter-than-air (LTA) aerial vehicle, robotic blimps hold significant potential for applications requiring extended airborne operation at low speeds. Examples include search and rescue missions [6], [11], environmental monitoring [12], [13], human-robot interaction [14]- [16], and entertainment [17], [18]. Unlike heavier-than-air (HTA) robots, such as drones, robotic blimps utilize buoyancy to counteract gravity, leading to lower energy consumption.…”