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Rendezvous with sperm whales for biological observations is made challenging by their prolonged dive patterns. Here, we propose an algorithmic framework that codevelops multiagent reinforcement learning–based routing (autonomy module) and synthetic aperture radar–based very high frequency (VHF) signal–based bearing estimation (sensing module) for maximizing rendezvous opportunities of autonomous robots with sperm whales. The sensing module is compatible with low-energy VHF tags commonly used for tracking wildlife. The autonomy module leverages in situ noisy bearing measurements of whale vocalizations, VHF tags, and whale dive behaviors to enable time-critical rendezvous of a robot team with multiple whales in simulation. We conducted experiments at sea in the native habitat of sperm whales using an “engineered whale”—a speedboat equipped with a VHF-emitting tag, emulating five distinct whale tracks, with different whale motions. The sensing module shows a median bearing error of 10.55° to the tag. Using bearing measurements to the engineered whale from an acoustic sensor and our sensing module, our autonomy module gives an aggregate rendezvous success rate of 81.31% for a 500-meter rendezvous distance using three robots in postprocessing. A second class of fielded experiments that used acoustic-only bearing measurements to three untagged sperm whales showed an aggregate rendezvous success rate of 68.68% for a 1000-meter rendezvous distance using two robots in postprocessing. We further validated these algorithms with several ablation studies using a sperm whale visual encounter dataset collected by marine biologists. 

This paper presents Millimetro, an ultra-low-power tag that can be localized at high accuracy over extended distances. We develop Mil-limetro in the context of autonomous driving to efficiently localize roadside infrastructure such as lane markers and road signs, even if obscured from view, where visual sensing fails. While RF-based localization offers a natural solution, current ultra-low-power local-ization systems struggle to operate accurately at extended ranges under strict latency requirements. Millimetro addresses this challenge by re-using existing automotive radars that operate at mmWave fre-quency where plentiful bandwidth is available to ensure high accuracy and low latency. We address the crucial free space path loss problem experienced by signals from the tag at mmWave bands by building upon Van Atta Arrays that retro-reflect incident energy back towards the transmitting radar with minimal loss and low power consumption. Our experimental results indoors and outdoors demonstrate a scal-able system that operates at a desirable range (over 100 m), accuracy (centimeter-level), and ultra-low-power (< 3 uW). 

Tire wear is a leading cause of automobile accidents globally. Beyond safety, tire wear affects performance and is an important metric that decides tire replacement, one of the biggest maintenance expense of the global trucking industry. We believe that it is important to measure and monitor tire wear in all automobiles. The current approach to measure tire wear is manual and extremely tedious. Embedding sensor electronics in tires to measure tire wear is challenging, given the inhospitable temperature, pressure, and dynamics of the tire. Further, off-tire sensors placed in the well such as laser range-finders are vulnerable to road debris that may settle in tire grooves. This paper presents Osprey, the first on-automobile, mmWave sensing system that can measure accurate tire wear continuously and is robust to road debris. Osprey’s key innovation is to leverage existing, high-volume, automobile mmWave radar, place it in the tire well of automobiles, and observe reflections of the radar’s signal from the tire surface and grooves to measure tire wear, even in the presence of debris. We achieve this through a super-resolution Inverse Synthetic Aperture Radar algorithm that exploits the natural rotation of the tire and improves range resolution to sub-mm. We show how our system can eliminate debris by attaching specialized metallic structures in the grooves that behave as spatial codes and offer a unique signature, when coupled with the rotation of the tire. In addition to tire wear sensing, we demonstrate the ability to detect and locate unsafe, metallic foreign objects such as nails lodged in the tire. We evaluate Osprey on commercial tires mounted on a mechanical, tire-rotation rig and a passenger car.We test Osprey at different speeds, in the presence of different types of debris, different levels of debris, on different terrains, and different levels of automobile vibration. We achieve a median absolute tire wear error of 0.68 mm across all our experiments. Osprey also locates foreign objects lodged in the tire with an error of 1.7 cm and detects metallic foreign objects with an accuracy of 92%. 

Tag localization is crucial for many context-aware and automation applications in smart homes, retail stores, or warehouses. While custom localization technologies (e.g RFID) have the potential to support low-cost battery-free tag tracking, the cost and complexity of commissioning a space with beacons or readers has stifled adoption. In this paper, we explore how WiFi backscatter localization can be realized using the existing WiFi infrastructure already deployed for data applications. We present a new approach that leverages existing WiFi infrastructure to enable extremely low-power and accurate tag localization relative to a single scanning device. First, we adopt an ultra-low power tag design in which the tag blindly modulates ongoing WiFi packets using On-Off Keying (OOK). Then, we utilize the underlying physical properties of multipath propagation to detect the passive wireless reflection from the tag in the presence of rich multipath propagations. Finally, we localize the tag from a single receiver by forming a triangle between the tag reflection and the LoS path between the two WiFi transceivers. We implement TagFi using a customized backscatter tag and off-the-shelf WiFi chipsets. Our empirical results in a cluttered office building demonstrate that TagFi achieves a median localization accuracy of 0.2m up to 8 meters range.