Aerial robots are widely deployed, but highly cluttered environments such as dense forests remain inaccessible to drones and even more so to swarms of drones. In these scenarios, previously unknown surroundings and narrow corridors combined with requirements of swarm coordination can create challenges. To enable swarm navigation in the wild, we develop miniature but fully autonomous drones with a trajectory planner that can function in a timely and accurate manner based on limited information from onboard sensors. The planning problem satisfies various task requirements including flight efficiency, obstacle avoidance, and inter-robot collision avoidance, dynamical feasibility, swarm coordination, and so on, thus realizing an extensible planner. Furthermore, the proposed planner deforms trajectory shapes and adjusts time allocation synchronously based on spatial-temporal joint optimization. A high-quality trajectory thus can be obtained after exhaustively exploiting the solution space within only a few milliseconds, even in the most constrained environment. The planner is finally integrated into the developed palm-sized swarm platform with onboard perception, localization, and control. Benchmark comparisons validate the superior performance of the planner in trajectory quality and computing time. Various real-world field experiments demonstrate the extensibility of our system. Our approach evolves aerial robotics in three aspects: capability of cluttered environment navigation, extensibility to diverse task requirements, and coordination as a swarm without external facilities.

Developing untethered millirobots that can adapt to harsh environments with high locomotion efficiency is of interest for emerging applications in various industrial and biomedical settings. Despite recent success in exploiting soft materials to impart sophisticated functions which are not available in conventional rigid robotics, it remains challenging to achieve superior performances in both wet and dry conditions. Inspired by the flexible, soft, and elastic leg/foot structures of many living organisms, here we report an untethered soft millirobot decorated with multiple tapered soft feet architecture. Such robot design yields superior adaptivity to various harsh environments with ultrafast locomotion speed (>40 limb length/s), ultra-strong carrying capacity (>100 own weight), and excellent obstacle-crossing ability (stand up 90° and across obstacle >10 body height). Our work represents an important advance in the emerging area of bio-inspired robotics and will find a wide spectrum of applications.

Global localization plays a critical role in many robot applications. LiDAR-based global localization draws the community's focus with its robustness against illumination and seasonal changes. To further improve the localization under large viewpoint differences, we propose RING++ that has roto-translation-invariant representation for place recognition and global convergence for both rotation and translation estimation. With the theoretical guarantee, RING++ is able to address the large viewpoint difference using a lightweight map with sparse scans. In addition, we derive sufficient conditions of feature extractors for the representation preserving the roto-translation invariance, making RING++ a framework applicable to generic multichannel features. To the best of our knowledge, this is the first learning-free framework to address all the subtasks of global localization in the sparse scan map. Validations on real-world datasets show that our approach demonstrates better performance than state-of-the-art learning-free methods and competitive performance with learning-based methods. Finally, we integrate RING++ into a multirobot/session simultaneous localization and mapping system, performing its effectiveness in collaborative applications.

The soft millirobot is a promising candidate for emerging applications in various in‐vivo/vitro biomedical settings. Despite recent success in its design and actuation, the absence of sensing ability makes it still far from being a reality. Here, a radio frequency identification (RFID) based battery‐less soft millirobot that can move, sense, and communicate remotely by coupling the magnetic and piezoelectric effects is reported. This design integrates the robot actuation and power generation units within a thin multilayer film (<0.5 mm), i.e., a lower magnetic composite limb decorated with multiple feet imparts locomotion and a flexible piezoceramic composite film recovers energy simultaneously. Under a trigger of external magnetic guidance, the millirobot can achieve remote locomotion, environment monitoring, and wireless communication with no requirement of any on‐board battery or external wired power supply. Furthermore, this robot demonstrates the sensing capability in measuring environment temperature and contact interface by two different sensing models, i.e., carried‐on and build‐in sensing mode, respectively. This research represents a remarkable advance in the emerging area of untethered soft robotics, benefiting a broad spectrum of promising applications, such as in‐body monitoring, diagnosis, and drug delivery.

Developing small‐scale soft continuum robots with large‐angle steering capacity and high‐precision manipulation offers broad opportunities in various biomedical settings. However, existing continuum robots reach the bottleneck in actuation on account of the contradiction among small size, compliance actuation, large tender range, high precision, and small dynamic error. Herein, a 3D‐printed millimeter‐scale soft continuum robot with an ultrathin hollow skeleton wall (300 μm) and a large inner‐to‐outer ratio (0.8) is reported. After coating a thin ferromagnetic elastomer layer (≈100–150 μm), the proposed soft continuum robot equipped with hybrid actuation (tendon‐ and magnetic‐driven mode) achieves large‐angle (up to 100°) steering and high‐precision (low to 2 μm for static positioning) micromanipulation simultaneously. Specifically, the robot implements an ultralow dynamic tracking error of ≈10 μm, which is ≈30‐fold improved than the state of art. Combined with a microneedle/knife or nasopharyngeal swab, the robot reveals the potential for versatile biomedical applications, such as drug injection on the target tissue, diseased tissue ablation, and COVID‐19 nasopharyngeal sampling. The proposed millimeter‐scale soft continuum robot presents remarkable advances in large‐range and high‐precise actuation, which provides a new method for miniature continuum robot design and finds broad applications in biomedical engineering.

The unequal distribution of medical resources and scarcity of experienced practitioners confine access to bronchoscopy primarily to well-equipped hospitals in developed regions, contributing to the unavailability of bronchoscopic services in underdeveloped areas. Here, we present an artificial intelligence (AI) co-pilot bronchoscope robot that empowers novice doctors to conduct lung examinations as safely and adeptly as experienced colleagues. The system features a user-friendly, plug-and-play catheter, devised for robot-assisted steering, facilitating access to bronchi beyond the fifth generation in average adult patients. Drawing upon historical bronchoscopic videos and expert imitation, our AI–human shared control algorithm enables novice doctors to achieve safe steering in the lung, mitigating misoperations. Both in vitro and in vivo results underscore that our system equips novice doctors with the skills to perform lung examinations as expertly as seasoned practitioners. This study offers innovative strategies to address the pressing issue of medical resource disparities through AI assistance.