Aerial Robotics

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38 papers in the last four weeks, up 217% on the four weeks before. 0.4% of all new papers.

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Latest papers 220

Sep 16, 2026cs.RO

A Morphing Aerial Robot With Thruster-Integrated Flexible Continuum Links for Shape Adaptive Aerial Manipulation

In recent years, aerial manipulation has attracted increasing attention as a key to expand the application of aerial robots. In this work, we focus on two major research directions for achieving versatile aerial manipulation: (i) acquiring high environmental adaptability using soft manipulators, and (ii) expanding the feasible wrench space by distributing thrusters along the manipulator. However, no aerial robot has simultaneously satisfied these two requirements. Therefore, in this paper, we propose a morphing rotor-distributed aerial robot with flexible continuum links that achieves both high shape adaptability and an expanded wrench space. The flexible continuum links function as soft manipulators, passively conforming to the shape of the environment, while the thrusters distributed along the continuum links expand the feasible thrust wrench space and enable the end-effector to exert large interaction forces. To realize the proposed robot, it is essential to suppress vibrations of the lightweight continuum links. Thus, we develop a composite leaf-spring structure that provides both high torsional and vertical stiffness, and vibration-suppressing control methods. Using these implementations, we demonstrate stable flight and a variety of aerial manipulation tasks. To the best of our knowledge, this is the first work to realize aerial manipulations using flexible links with an integrated thruster.

Eri Sawada, Kazuki Sugihara, Ayano Miyamichi +2

Department of Mechano-Infomatics, The University of Tokyo, Bunkyo-ku, Tokyo 113-8656, Japan

Aerial Robotics Soft Robotics Aerial Manipulation

Sep 16, 2026cs.AI

AeroWeaver: An Embodied-Agent Harness for Weaving Aerial Skills into Distributed, Adaptive Swarm Execution

Collective intelligence is a collaborative autonomy paradigm in which multiple agents pursue shared objectives through local perception, information exchange, and coordinated action. UAV swarms embody this paradigm by coordinating multiple vehicles in tasks such as search, inspection, and tracking. Recent advances in large language model (LLM) agents have strengthened natural-language task understanding and high-level planning, providing a flexible semantic interface between mission descriptions and collective behavior. While these advances expand semantic reasoning, applying LLM agents to UAV swarms raises challenges in grounding model decisions in executable capabilities, reconciling global task reasoning with distributed execution, and using mission-specific experience for continual adaptation. To address these challenges, we introduce AeroWeaver, an embodied-agent harness that weaves individual UAV skills into coordinated mission-level behavior. AeroWeaver connects semantic decisions to governed skills, organizes role-conditioned local agents for distributed coordination, and uses role-indexed state-action-reward experience to refine skill selection online. Experiments and runtime validation show that AeroWeaver maintains valid skill execution under tested conditions and supports body-local multi-UAV operation without a central agent generating joint actions from global context, while reward-guided online updates provide a training-free path for adaptive learning swarm agents from accumulated execution experience. Code: https://github.com/Admire-ljb/AeroWeaver.

Jiabin Lou, Yirong Yang, Haopeng Wang +6

Beihang University, 37 Xueyuan Road, Haidian District, Beijing 100191, China

Aerial Robotics Swarm Robotics LLM Agent Skill Learning Multi-Robot Coordination LLM Agent Harnesses

Sep 16, 2026cs.RO

Real-Time Bounded Catenary Solver for UAV Tether Modeling

For non-stationary tethered multirotor UAVs in real-world conditions, simulating the forces imposed on the drone by the aerodynamic drag of the tether becomes crucial, with online use cases placing a hard bound on the maximum solve time. In previous work, a quasi-analytical catenary tether model reached a mean solve time of 0.51 ms using a general-purpose root finder, but without any worst-case guarantees or proven convergence. In this work, we reformulate the inner solver by reducing the catenary boundary-value problem to a single transcendental equation in one well-conditioned unknown. We derive a closed-form bracket and prove monotonicity and convexity as well as existence and uniqueness of the root, which together guarantee convergence of the solver. We further propose a two-regime initial guess which approximates the true root within 3.4% and reduces the mean iteration count by 68.0% to 2.36 compared to the textbook initialization. Building on the hybrid root-finding method rtsafe (Newton-Raphson with bisection fallback giving bounded iteration counts), we implement a specialized variant that exploits the problem structure to omit unnecessary checks while retaining correctness, which gives up to 1.3 times speedup. With the proposed solver the full tether model achieves a nearly constant solve time of 6.9 us on average and 7.7 us at worst, a 40 times speedup over an optimized re-implementation of the previous method, while agreeing with it to a relative deviation of 8.7e-9. Because the reformulation leaves the underlying physical model untouched, the experimental validation of the previous work carries over unchanged. We further demonstrate its suitability for embedded, resource-constrained platforms with a Lua implementation running directly in ArduPilot on a drone's flight controller, where it stays well inside the scheduling budget with a mean solve time of 0.74 ms.

Max Beffert, Andreas Zell

Cognitive Systems Group, University of Tübingen, Germany

Aerial Robotics

Sep 16, 2026cs.RO

UAVs Meet Embodied Intelligence: Bridging Human Intents and Flying Dynamics Via Harnessing Physical-Digital AI Agents

Unmanned aerial vehicles (UAVs) extend embodied intelligence into continuous three-dimensional space, where perception, reasoning, physical embodiment, and action are tightly coupled through flight and environmental interaction. Recent advances in foundation models, world models, and AI agents are shifting UAV autonomy from task-specific perception and control toward systems that can interpret human intent, understand open environments, reason about physical consequences, and organize complex behaviors under embodiment and flight-dynamic constraints. We characterize this emerging paradigm as UAV embodied intelligence (UAV EI) and distinguish it from its system realization, the embodied-intelligent UAV (EI UAV). To provide a unified view of the field, we introduce a 5+5 framework that describes UAV EI through five capability dimensions and EI UAVs through five architectural layers spanning physical embodiment, general cognition, embodied skills, external interaction, and system harnessing. Based on this framework, we systematically review recent progress in embodied morphology, embodied perception, world models, embodied planning, vision-language navigation, embodied manipulation, and embodied collaboration. We further identify long-horizon autonomy, predictive physical reasoning, test-time skill acquisition, and autonomous capability evolution as key challenges toward more general aerial embodied intelligence. Finally, we argue that harnessing physical-digital AI agents, through persistent coupling of digital intelligence with physical sensing, dynamics, action, and feedback, provides a system-level pathway toward adaptive and continuously evolving UAV autonomy. Project resources are available at our project website and GitHub repository.

Yonglin Tian, Weiyi Wang, Houhua Lu +13

Institute of Automation, Chinese Academy of Sciences, China. · Beihang University, China. · Shanxi Datong University, China. +7

Aerial Robotics UAV Navigation Embodied AI

Sep 16, 2026cs.RO

OmniRisk: Omnidirectional Trajectory-Risk Learning for Agile Quadrotor Dynamic Avoidance

Agile quadrotor avoidance of fast-moving obstacles requires anticipating collisions and selecting feasible maneuvers within short reaction windows. Reliable predictive avoidance remains challenging because sparse range observations do not directly reveal obstacle motion, while online trajectory optimizers either scale poorly with obstacle count or remain efficient at the expense of reliability in dense, high-speed encounters. We present OmniRisk, an omnidirectional planning framework that learns trajectory-level risk offline for efficient onboard evasion. A fixed-dimensional tensor combines LiDAR range panoramas, dynamic masks, and Cartesian surface velocities to represent geometry and motion jointly. We formulate an asymmetric risk field aligned with obstacle velocity that emphasizes approaching interactions and attenuates receding ones. Accumulating this risk along predicted relative trajectories provides dense supervision and discourages unnecessary hesitation after obstacles pass. A dual-branch circular convolutional network predicts terminal boundary states and dynamic risks for candidate primitives over an omnidirectional anchor lattice in a single forward pass, followed by selection and closed-form reconstruction of the selected candidate primitive. This formulation removes online risk accumulation along trajectories and makes risk-inference cost independent of obstacle count. OmniRisk enables efficient onboard avoidance, with real-world flights demonstrating consecutive evasive maneuvers at relative encounter speeds up to 15 m/s without fine-tuning. Code is available at https://github.com/VANdexj/OmniRisk.

Yifan He, Yang Liu, Wenhao Zhao +8

Differential Robotics, Hangzhou, China · Zhejiang University, Hangzhou, China

Aerial Robotics Safe Motion Planning Dynamic Obstacle Avoidance

Sep 16, 2026cs.RO

Prior Evolution and Task Alignment for Aerial Grasping

Aerial grasping is a remarkable capability exhibited by predatory birds, allowing them to capture prey through highly coordinated maneuvers in flight. Inspired by this capability, researchers have developed various formulations to reproduce such maneuvers through trajectory optimization. However, two limitations remain in practice. First, the resulting optimization problem is highly nonconvex and sensitive to initialization, making high-quality solutions difficult to obtain under a limited computational budget. Second, prescribed numerical objectives are human-designed abstractions that describe successful grasping through a limited set of mathematically tractable quantities and may not fully capture what determines task success. We investigate how learning can address these limitations within an analytical planner. Accordingly, a trajectory prior is first learned from optimized motions and then evolved through a CEM-based process that evaluates sampled initializations with the deployed optimizer and retains favorable ones as new supervision. An Execution-Aware Critic learns from contact, lift, and completion outcomes to assess whether the optimized trajectories are likely to succeed in physical execution. Its frozen energy can further serve as a differentiable grasping cost, allowing execution data to directly shape trajectory generation. Simulation and real-world experiments demonstrate improved optimization reliability, trajectory consistency, and grasping performance.

Weiliang Deng, Zhengyang Dang, Yao Mu +1

School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510275, China · AI Institute, School of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China · Differential Robotics Technology Co., Ltd., Hangzhou, China

Aerial Robotics Trajectory Optimization Robotic Grasping Aerial Manipulation

Sep 15, 2026cs.CV

Optical-Flow Wingbeat Counting in MuJoCo: A Comparison of Convolutional, Spiking, and Attention-Based Temporal Models

Visual monitoring of flapping-wing vehicles requires distinguishing individual wingbeats from motion strength and average frequency. This paper presents a controlled MuJoCo evaluation of wingbeat counting from signed optical flow observed by virtual cameras mounted on Crazyflie vehicles. Three flapping-wing models were recorded at optical distances of 1.5 and 3.0 m, producing 1,440 clips from 240 paired scene configurations with a scene-level 3:1 training-test split. A common spatial convolutional encoder was combined with a causal temporal convolutional network, a recurrent leaky integrate-and-fire spiking network, or causal self-attention. Each model predicted phase and activity, followed by the same directed-crossing event counter. The six existing convolutional models were retained, and all twelve new models were frozen before their test predictions were generated. Exact-count accuracies at 1.5 m were 96.67%, 95.00%, and 96.67%, respectively; at 3.0 m they were 94.44%, 92.22%, and 95.00%. All paired scene-bootstrap intervals for differences in exact-count accuracy included zero. Seven far-distance spiking-model clips had correct totals despite event-timing mismatches, demonstrating why total-count and event-level measurements must be reported together. The results support the feasibility of causal optical-flow counting in the tested setting and identify boundary-sensitive errors. They do not establish an architecture ranking across repeated training, real-flight robustness, or hardware efficiency.

Zhang Nengbo

School of Aerospace Engineering, Engineering Campus Universiti Sains Malaysia, 14300 Nibong Tebal, Pulau Pinang, Malaysia

Optical Flow Aerial Robotics Object Counting Temporal Convolutional Networks Spiking Neural Networks

Sep 15, 2026cs.MA

Calibrate Once, Fly Any Team: Residual-Grounded Low-Fidelity Training for Cooperative Drone Swarms

Training multi-agent drone-swarm policies directly in high-fidelity (HF) rigid-body physics is accurate but computationally expensive. This cost scales poorly with team size, as each additional agent multiplies contact-resolution complexity and sharply raises the in-simulation crash rate. To address this, we propose a mixed-fidelity training scheme that eliminates HF reinforcement learning entirely. A single shared, decentralized policy is optimized inside a fully-differentiable, JAX-native low-fidelity (LF) point-mass simulator. The simulator is corrected by a small, per-agent bagged residual ensemble fit once, offline, using short calibration flights in the HF simulator. Because calibration requires only one isolated drone, the data collection budget does not compound with team size. Reference trajectories are generated by rolling out an existing LF-only policy and tracked in the HF simulator by a zero-training PD controller. Evaluated across four cooperative drone tasks and team sizes from 3 to 18, the residual-corrected policy outperforms an uncorrected LF baseline in all combinations, and a from-scratch HF policy in 22 of 24 combinations tested. It trails an HF-finetuned policy by a margin that narrows steadily with team size. Ultimately, the proposed method achieves near-equivalent performance at the largest team sizes at a fraction of the computational cost, completely avoiding the high crash rates typical of HF training.

Maxim Mednikov, Oren Gal

Swarm & AI Lab (SAIL), University of Haifa, Israel

Aerial Robotics Swarm Robotics Residual Dynamics Learning Cooperative MARL

Sep 14, 2026cs.RO

DuctAM: A Duct-Assisted Quadrotor-Based Aerial Manipulator Enabling High-Force Push-and-Pull Interactions

Uncrewed Aerial Manipulators (UAMs) extend the capabilities of Uncrewed Aerial Vehicles (UAVs) from perception to physical interaction. Among various aerial interactions, push-and-pull operations are fundamental manipulation primitives that require sustained horizontal forces while maintaining stable flight. In this paper, we propose DuctAM, a compact aerial manipulation platform that enhances horizontal force capability for push-and-pull interactions using two ducted fans integrated along the quadrotor interaction axis. An attitude-force decoupled control scheme enables controllable horizontal forces without requiring large attitude changes. Extensive real-world experiments are conducted to validate the DuctAM. Figure-eight trajectory tracking experiments demonstrate stable flight and accurate motion control in both quad and duct modes. Force-measurement experiments quantify the decoupled longitudinal force capability of DuctAM. Finally, representative push-and-pull interaction tasks, including cart pushing, door closing, and drawer opening, verify the practical effectiveness of DuctAM. The results show that DuctAM achieves significantly improved horizontal interaction force capability while maintaining stable flight compared with conventional UAVs.

Yi Wang, Rui Jin, Xinhang Xu +4

School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798 · NTU–VinUni Joint Research Laboratory for Embodied AI and Robotics, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, and VinUniversity, Hanoi, Vietnam

Aerial Robotics Quadrotor Control Aerial Manipulation

Sep 14, 2026cs.RO

PATH: Continuous Target Sensing among Autonomous Cooperative Drones

Continuous target sensing by uncrewed aerial vehicles (UAVs) is constrained by limited flight endurance, motivating the transfer of tracking responsibility between cooperating UAVs. Such a handoff requires the receiver to identify the same physical target currently tracked by the sender despite differences in viewpoint, scale, and target appearance. Existing approaches based on global target localization or appearance-based cross-view association are limited by positioning uncertainty or ambiguous visual features. This paper presents Perspective Alignment & Tracking Handoff (\textbf{PATH}), a platform-agnostic, geometry-assisted sensing and verification framework for target handoff between two moving UAVs. The sender reconstructs the tracked target as a metric 3D point using RGB-D sensing, while the receiver estimates its relative pose from a fiducial observation and projects the transmitted target point into its own image as a spatial prior for target acquisition. The receiver-generated candidate is then returned to the sender and verified through a cross-view Mutual Agreement Handshake before tracking responsibility is transferred. Real-world UAV experiments show mean relative-position and target-position errors of 0.047~~m and 0.030~~m, respectively. Under visually ambiguous conditions, PATH achieves 96.0% frame-level receiver-side target acquisition accuracy, with 2.0% false-positive and 2.0% false-negative rates. A sensor-error sensitivity analysis shows that relative-pose uncertainty is the dominant contributor to receiver-view projection error. The implementation operates at video rate with compact inter-UAV communication below 16~~kB/s at 60~~Hz, demonstrating the feasibility of lightweight geometry-assisted target handoff on resource-constrained UAV platforms.

Heegyeong Kim, Alice James, Avishkar Seth +4

School of Computing, Macquarie University, Sydney, NSW, Australia · School of Natural Sciences, Macquarie University, Sydney, NSW, Australia

Aerial Robotics Multi-Robot Systems Visual Object Tracking Multi-Robot Coordination Cooperative Perception

Sep 14, 2026cs.LG

Curriculum-Based Adversarial Heterogeneous Agent Reinforcement Learning for Autonomous Quad-Copter Landing in Maritime Settings

Recovering unmanned aerial vehicles (UAVs) in maritime environments is challenging due to wind turbulence and ship-deck motion, making it a valuable test case for alternative control and learning approaches as conventional landing approaches often become unreliable. We study simulated mid-air capture of quadrotor UAVs by a ship-mounted robotic arm, learning robust cooperative control policies with Heterogeneous-Agent Proximal Policy Optimization (HAPPO) Reinforcement Learning. We train with HAPPO using a curriculum and an adversarial wind agent (HARL-AC) in NVIDIA Isaac Lab, and compare the obtained control policies against those generated through curriculum-based domain randomization and a benchmark trained on a single sea state. In-distribution evaluation on sea states

0/4/5

shows comparable success for HARL-AC and domain randomization of up to

97.5\%

. On out-of-distribution sea states

7/8/10

, HARL-AC generalizes better, achieving up to

16\%

higher median success rate at sea state 10, and substantially lower crash rates of up to

14\%

compared to the domain randomization policy. Furthermore, we show that the adversarially trained policy shows more cautious behavior, slightly increasing timeouts by

<3\%

, but yields safer recovery behavior in severe, unseen conditions.

Allan Minh-Tam Nguyen, Sree Showrya Kotala, Stefan Banioi-Crijman +2

Department of Advanced Computing Sciences, Maastricht University, Minderbroedersberg 4-6, 6211 LK Maastricht, Netherlands

Adversarial Training Aerial Robotics Quadrotor Control Robust RL Heterogeneous MARL

Sep 11, 2026cs.RO

SwarmNxt: Open-source Software-Hardware Platform for Fast and Agile Aerial Swarms

Aerial robot swarms have the potential to transform time-critical safety, security, and search-and-rescue operations. By coordinating multiple robots, they can rapidly survey disaster sites, map collapsed or GPS-denied environments, and search cluttered areas faster than a single robot, reducing response times and minimizing risks to first responders. Realizing this potential, however, requires robust autonomous swarm navigation, which remains an active research challenge. Progress is further constrained by existing platforms, as commercial drones are often closed-source or lack the onboard computational resources needed for agile, vision-based collective flight. Moreover, developing, deploying, and maintaining software across multiple aerial robots requires significant engineering effort. To address these challenges, we present SwarmNxt, an open-source software platform built on the open-source OmniNxt drone hardware. SwarmNxt provides an end-to-end toolkit, including detailed hardware assembly instructions with a video tutorial, automation tools for parallel software deployment and swarm-wide updates, and a ROS 2-based framework for autonomous navigation. The platform integrates state-of-the-art control, planning, and depth estimation into a single ROS 2 multi-agent system, providing an open research infrastructure for physical swarm experimentation. We validate SwarmNxt through two real-world experiments: a six-drone swarm performing decentralized planning with high-speed inter-drone collision avoidance, and a four-drone swarm executing collective flight with onboard depth estimation in an obstacle-filled environment. Both experiments were run indoors with global position from external motion capture; perception, planning, and control run onboard.

Charbel Toumieh, Niel Mistry, Benjamin Jarvis +4

Laboratory of Intelligent Systems, Ecole Polytechnique Federale de Lausanne (EPFL), CH1015 Lausanne, Switzerland · Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong

Aerial Robotics Swarm Robotics

Sep 11, 2026cs.RO

Aerodynamic Prior-Free Coordinated Trajectory Generation and Tracking Control for a Tail-Sitter UAV

This paper presents a coordinated trajectory generation and tracking control framework for a tail-sitter unmanned aerial vehicle (UAV), which does not require aerodynamic priors identified for a specific airframe while addressing the challenge of flight control under highly nonlinear aerodynamics across the full flight envelope. The core innovation lies in employing phase-specific aerodynamic modeling strategies for planning and tracking, tailored to their distinct functional characteristics, without requiring airframe-specific aerodynamic priors. Specifically, the phi-theory model under coordinated flight is employed to derive an analytic differential flatness mapping, and a simplified but locally accurate model is established for predictive control to enable real-time aerodynamic parameter estimation. The proposed framework is evaluated extensively through both simulation and challenging real-world flight tests under mild wind conditions, showing high-precision tracking and adaptability across the tested aerodynamic conditions. To the best of our knowledge, this is the first real-world demonstration of accurate trajectory tracking over tested flight regimes spanning the full envelope of a tail-sitter UAV without relying on aerodynamic identification campaigns. The source code of our framework is available at: https://github.com/SYSU-HILAB/AP-PnC.

Erchao Rong, Zihao Liu, Junning Liang +5

School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510275, China

Aerial Robotics UAV Navigation Model Predictive Control UAV Trajectory Optimization

Sep 11, 2026cs.RO

EVPeriscope: Extended Perception across Aerial and Ground Vehicles with Event-based Propeller Tracking

Reliable relative localization between aerial and ground robots is a key requirement for tightly coordinated heterogeneous teams. This can be difficult to do using conventional frame-based cameras and fiducial markers because they are sensitive to motion blur, lighting variations, and payload constraints. This paper presents EVPeriscope, an event-based perception system that enables detection, localization and control of a quadrotor using an upward-facing event camera on a ground robot by detecting the high-frequency visual signature of its propellers. This system allows the quadrotor to function as an extended perception system for the ground robot when onboard sensors exhibit degradation or occlusion. We demonstrate the capabilities of this marsupial ground-aerial system via experiments in challenging field conditions with wind speeds of up to 15 mph, in both daylight and at night. We show that the system supports localization and closed-loop navigation through dense foliage where the ground robot's sensors are occluded. Our control system for the quadrotor operates at 200 Hz entirely with onboard sensing and computation. More details and experiment videos can be found on the project page: https://ongdexter.github.io/evperiscope.

Dexter Ong, Vijay Kumar, Pratik Chaudhari

General Robotics, Automation, Sensing and Perception (GRASP) Laboratory, University of Pennsylvania, Philadelphia PA 19104, USA

Aerial Robotics Relative Pose Estimation Multi-Robot Systems Cooperative Localization Vision-Based Robot Control

Sep 9, 2026cs.RO

Multi-Agent Reinforcement Learning for Autonomous UAV Exploration in Wildfire Response

This study develops a deep reinforcement learning framework for training Unmanned Aerial Vehicle (UAV) agents to navigate and monitor simulated wildfire environments. Results show that agents learn increasingly stable and effective behaviors over time, as demonstrated by converging loss trends, improved reward signals, and more consistent navigation patterns such as fire-boundary tracking. Overall, these findings highlight the potential of deep reinforcement learning (DRL) based UAV systems for autonomous wildfire monitoring and suggest that environmental structure and reward design influence policy effectiveness.

Caden Chandra, Jerry Ng

Aerial Robotics Reinforcement Learning Multi-Agent Reinforcement Learning

Sep 8, 2026cs.CV

Towards Embodied Air-Ground Cooperative Object Search: Benchmark, Dataset and Agentic Method

Air-Ground Object Search (AGOS) in urban environments is a challenging embodied task, which requires an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV) to jointly search for and verify a specified target vehicle from multi-view visual references. To study this underexplored problem, we introduce AGOS-Bench, the first dedicated benchmark for evaluating whether general-purpose Vision-Language Models (VLMs) can integrate aerial discoveries and ground-level verification through UAV-UGV cooperation. We further provide AGOS-Dataset as the companion resource of exemplary trajectories constructed by an automatic pipeline. It consists of 7.7k episodes for searching objects of diverse categories and attributes, spanning three difficulty levels. To address the AGOS task, we propose AGOS-Agent, a training-free and tool-augmented approach. The agentic method relieves VLMs from complex and dynamic coordination via a deliberate search-handoff-verify cooperation protocol, only demanding VLMs for scene understanding and decision-making. Extensive experiments on nine VLMs show that AGOS-Agent improves overall success rate for eight of the nine evaluated backbones while reducing decision steps for all nine. On the hard split, the SR and SPL of Gemini-3.6-Flash increase from 8.6% to 55.7% and from 7.6% to 44.0%, respectively.

Boao Yu, Zimo Chen, Junreng Rao +4

National University of Defense Technology National Key Laboratory of Digital Intelligent Modeling and Simulation

Aerial Robotics AI Agent Benchmarks Tool-Using Vision-Language Agents VLMs for Robotics

Sep 7, 2026cs.RO

Anti-Gravity Walking by a Flying Humanoid Robot via Thrust-Rate Input Whole-Body Model Predictive Control

Flying humanoids are expected to perform tasks in diverse environments, while their existing locomotion is mainly limited to aerial flight and ground walking. The capability to move in complex three-dimensional space can greatly expand their application range. For such walking motion on ceilings and similar anti-gravity environments, whole-body MPC is effective. However, the discontinuous changes in dynamic structure accompanying contact switching during walking can induce thrust spikes, resulting in control instability. Therefore, in this work, we propose and implement a real-time whole-body MPC framework for anti-gravity bipedal walking. First, we formulate whole-body MPC using the time derivative of thrust, namely thrust-rate, as the control input. This formulation guarantees continuity of the thrust trajectory during contact switching while preserving the sparse structure of the optimal control problem for fast computation. Second, we address the lack of natural support forces in anti-gravity environments. We introduce lower bounds on the foot-normal component of the contact force, and smoothly transfer them during the doublesupport phase. Finally, we implement the proposed framework and demonstrate anti-gravity walking by a flying humanoid through simulation and a hardware experiment. To the best of our knowledge, this is the first demonstration of multi-contact whole-body MPC for a transformable aerial robot and walking by a flying humanoid beyond the ground.

Kazuki Sugihara, Kei Okada

Department of Mechano-Informatics, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.

Aerial Robotics Humanoid Robot Locomotion Model Predictive Control

Sep 2, 2026cs.RO

From Multi-Fisheye Sensing to Panoramic Perception: A Parallax-Aware Onboard Platform for Ultra-Low-Altitude UAVs

Ultra-low-altitude unmanned aerial vehicles (UAVs) require surround vision near buildings, vegetation, and other obstacles. We present a parallax-aware onboard platform that converts four synchronized fisheye streams into an open 1280x640 equirectangular panorama (ERP) interface. A purpose-built carbon-fiber airframe integrates the cameras, NVIDIA Jetson Orin NX, a flight controller, and a global navigation satellite system (GNSS) receiver. The formation pipeline selects projection depth per overlap and combines controlled seams and photometric fusion. Its accuracy profile adds content-adaptive seam search and a validation-gated residual mesh, whereas its deployed profile retains margin-gated Per-seam updates for sensor-rate operation. Evaluation uses more than 50,000 four-view groups from 18 field sequences. Relative to Fixed Depth, the accuracy profile reduces far-field P90 feature misalignment by 41.6%; the deployed Per-seam profile achieves the lowest aggregate geometric errors across held-out sites. Under a paced 20 Hz replay, the deployed profile sustains 19.99 frames/s at 13.29W mean module-input power. Eight-sector ERP sampling reaches 90.8% mean daytime visual-place-recognition Recall@5. Together, these results validate an integrated onboard panoramic-perception architecture that unifies parallax-aware formation, sensor-rate embedded execution, and reusable downstream vision interfaces for ultra-low-altitude UAVs. The project has been open-sourced at https://github.com/DUNDAI1998/parallax-aware-uav-panorama.

Dun Dai, Ze Lu, Cheng He +2

School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China · Tianmushan Laboratory, Hangzhou 311115, China

Aerial Robotics Computational Imaging

Sep 1, 2026cs.RO

TriSAR: Task Coordination and Collision Avoidance for Aerial Robot Teams in Disaster Response

Multi-Unmanned Aerial Vehicle (UAV) disaster-response systems require coordinated task assignment and local trajectory control, yet the individual and combined contributions of these coordination layers to mission efficiency and operational safety remain insufficiently characterised under controlled experimental conditions. TriSAR is evaluated as a five-UAV coordination system operating in a physics-based Gazebo simulation of an earthquake-damaged urban environment. A 2 x 2 factorial design compares two task-allocation strategies (Genetic Algorithm and greedy fitness-based allocation) with reactive collision avoidance enabled or disabled. Each of the four configurations was evaluated over 30 stochastic episodes in a common scenario of five UAVs and eight targets. Under greedy allocation, enabling repulsion eliminated recorded collision-threshold violations, confirmed by a Mann-Whitney test (U = 885, p = 4.03 x 10^-12, rank-biserial r = 0.97). Under GA allocation, the same protective effect was confirmed (U = 675, p = 1.26 x 10^-5, rank-biserial r = 0.50). For mission-efficiency metrics, GA-based allocation showed no statistically detectable advantage over greedy allocation when repulsion was enabled, but a significant advantage in steps, path length, and energy when repulsion was disabled (Welch's t-tests, |g| between 0.92 and 1.76). These results show that reactive repulsion provides a substantial, allocation-dependent safety benefit, while the additional computational complexity of GA-based task allocation yields a detectable mission-efficiency benefit only when repulsion is disabled.

Aditya Anil Kapile, Pedro Machado, Isibor Kennedy Ihianle

School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom

Aerial Robotics Multi-Robot Coordination Multi-Robot Task Allocation Search-and-Rescue Robotics Robot Collision Avoidance

Sep 1, 2026cs.RO

Vision-Based Leader-Follower Formation Control for Cooperative UAVs in GPS-Degraded Environments

Cooperation in multi-UAV systems requires reliable relative perception so that follower vehicles can maintain formation and continue their mission safely even when absolute positioning sensors degrade or fail. This paper presents a vision-based cooperative formation framework running on a follower UAV that uses a front-facing RGB-D camera to detect, track, and localize a leader UAV in real-time. A lightweight YOLO-based detector is trained on a dedicated drone dataset and deployed onboard to predict leader bounding boxes, which are then fused with depth information via a pinhole camera model to estimate the leader's relative pose. These estimates provide a leader-follower position controller and can also be used as a backup when GPS or external localization is unavailable. This framework is implemented as a set of ROS nodes and evaluated in a physics-based multi-UAV simulation built on XTDrone, with sensor noise and communication dropouts. We evaluate detection accuracy, runtime, and formation-keeping error under nominal conditions and under simulated failures of the positioning sensors. The results show that the proposed framework maintains stable leader-follower formations with reasonable computational cost and provides a practical basis for extending vision-based cooperative formation control to real-world multi-UAV systems.

Deekshitha Angadi, Naveena Budda, Vikas Agarwal +4

AI, IoT and Robotics Lab (AIR Lab), UAVs Group, Autonomous Robotics Systems Limited, Hyderabad, India · Department of Microelectronics and VLSI Design, University of Hyderabad, Hyderabad, India · Department of Internet of Things, Ideabytes Software India Private Limited, Hyderabad, India +5

Multi-Robot Formation Control Aerial Robotics Relative Pose Estimation Vision-Based Robot Control

Sep 1, 2026cs.RO

Evaluating Multimodal LLMs as Generalist Vision-Language-Action Agents for Drone Control: Commanding, Approaching, Tracking and Searching

Multimodal Large Language Models (MLLMs) are strong perceivers of images and video. We ask how far that reach extends into acting: dropping an MLLM directly into a drone's control loop, with its entire action space declared solely in the prompt. Recent systems approach this setting but increasingly narrow the model's decision-making. We widen it back. We introduce DroneCATS-Agent, an architecture where the MLLM is a swappable component, and DroneCATS, a benchmark treating the model as the independent variable. Beyond merely flying toward a pixel, our agent entrusts the model to yaw and search, deliberate when unsure, and self-declare arrival---all without fine-tuning or function-calling schemas. Evaluating frontier and open models across four core capabilities---approaching a visible target, tracking a moving one, searching outside the initial view, and commanding a multi-drone fleet---reveals that even the simplest embodied settings are far from solved. Crucially, to identify what breaks first at the edge, our roster scales down to 2B parameters. The findings expose a stark paradox: it is not the flying that fails. Small open models often navigate into the success radius more reliably than frontier models, yet lose the episode by declaring arrival prematurely or not at all. Multi-drone commanding amplifies this divide, with small models failing by blindly copying a single coordinate across distinct views. Viewed as vision-language-action agents, the models' spatial perception holds up, but their action protocol does not. What separates a deployable edge model from a frontier model is not navigation, but the discipline to sustain a declared protocol and emit the correct terminating action. The open problem is closing this gap at onboard compute costs---yielding a fast model that plans persistently and knows exactly when it is done---and DroneCATS is built to measure that distance.

Jaewoo Park, Minyoung Lee, Sukmin Seo +11

Drone AI Team, NAVER Cloud

Aerial Robotics Language Model-Based Control Multimodal Large Language Models Vision-Language-Action Models

Sep 1, 2026cs.RO

Fleets Need a Context Plane: Rethinking Cooperative Perception for Autonomous Drones

Cooperative perception allows a drone fleet to combine observations from multiple viewpoints. However, existing systems typically fix their feature-sharing policies at design time or adapt to only one context signal. This is a poor fit for aerial fleets, whose missions, bandwidth, formation geometry, and scene coverage can change during flight. We quantify the cost of context-blind sharing on UAV3D by controlling feature exchange at evaluation time using a released DiscoNet checkpoint, without retraining. Mission-aware sharing matches full-sharing accuracy while using only 5-10% of the bytes. The best tested peer selection policy changes with the byte budget, and choosing the wrong policy loses up to 7.7 AP. Moreover, under a constrained budget, two policies with the same full-scene accuracy differ by 5.9 AP within the mission region, showing that multiple context axes must be considered jointly. We therefore propose the context plane, a bounded, structured interface for runtime context. Each drone publishes a descriptor of at most 1 KB at 10 Hz, and lightweight, replaceable policies use the fleet context to decide what each drone computes, shares, and fuses. Existing sharing schemes become fixed policies within this interface. In our ROS 2 prototype on a Jetson AGX Orin, the context plane uses approximately 0.01% of the data-plane bandwidth, and each policy decision takes 0.10 ms. These results show that an explicit context interface can support low-overhead runtime adaptation without modifying or retraining the perception model.

Liangkai Liu, Xiaoxiao Wu

Department of Computer Science, Texas Tech University, Lubbock, USA

Aerial Robotics Communication-Efficient Collaborative Perception Cooperative Perception

Aug 31, 2026cs.RO

Optimized Modular Design and Development of a Tilt-Rotor Bicopter Drone

Hybrid systems like tilt-rotor bicopter drones combine the beneficial characteristics of both fixed-wing and rotary-wing technology, enabling long endurance and VTOL capability. However, such drones also require an optimum design to ensure both static and dynamic stability. The modular design of a traditional bicopter is developed in this paper based on extensive analysis and in-depth structural and aerodynamic simulations. The structural analysis has been performed to ensure that the aircraft's structure withstands the stresses encountered during different flight modes. Controlling the relative positions of the Center of Gravity (CG) and Neutral Point (NP) is an essential aspect of the design, ensuring stability during hover and positive stability during forward flight. The thrust and power analyses have been conducted to assess the flight performance and endurance. After analysis, the drone has been developed, and flight tests with a basic flight controller were conducted to validate the performance metrics obtained in the simulation.

Saideep Verma, Nimisha Tatapudi, Akshay Arjun +3

Department of Mechanical Engineering, BITS Pilani Hyderabad Campus, Hyderabad, India. · Department of Electrical and Electronics Engineering, BITS Pilani Hyderabad Campus, Hyderabad, India.

Aerial Robotics Vehicle Dynamics

Aug 30, 2026cs.RO

VeloBins: Learning Velocity and Its Uncertainty via Bins and Error-Conditioned Gaussian Labels for Aerial Inertial Odometry

Inertial odometry (IO) is critical for aerial robots, where aggressive maneuvers and poor lighting degrade visual sensors. Recent learning-based IO methods improve traditional integration-based approaches by learning motion priors from IMU and platform-specific sensors, then fusing the predictions within an extended Kalman filter. However, learning velocity through regression is difficult, while jointly estimating uncertainty with a separate decoder and negative log-likelihood (NLL) loss further complicates training and can lead to over-confident estimates. We introduce VeloBins, which reformulates velocity regression as classification over discretized velocity bins. We decode both the velocity from the bin distribution's expectation and the uncertainty from its variance, removing the need for a separate uncertainty decoder. We further supervise the uncertainty explicitly using an error-conditioned Gaussian label centered at the ground-truth velocity, with a standard deviation set to the velocity error. We evaluate VeloBins on four aerial datasets, ranging from free-form aggressive flights and a 27 g nano-quadrotor to drone racing at over 21~m/s. VeloBins achieves the lowest average errors on all four datasets, reducing velocity, relative trajectory, and absolute trajectory errors by 3-27%, 8-40%, and 6-53%, respectively, compared with the strongest baseline. Notably, the proposed supervision achieves the lowest NLL and best filter consistency despite never optimizing an NLL loss. The code will be available upon acceptance.

Maulana Bisyir Azhari, Seungwook Lee, Donghun Han +2

Unmanned Systems Research Group (USRG), School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, South Korea

Aerial Robotics Inertial Odometry Robot State Estimation Inertial Navigation

Aug 17, 2026cs.CV

Graph Neural Assisted Actor-Critic for Latency-Efficient Edge Vision System

UAV on-board vision systems are widely used for different activities, including monitoring in no-fly zones. In this case, the vision-equipped UAV streams a video to a ground server where an operator assists its activities. The latency of video transmission has a profound impact on the effectiveness of the operator assistance. However, most techniques available for video transmission still incur significant latency costs. In this paper, we propose a graph convolutional neural network-assisted (GCN-Assisted A2C) deep reinforcement learning (DRL) system model to find the optimal pixel-correlated area of a suspicious object. We combine the Lagrangian dual form with gradient descent to prevent lack of convergence and over- and under-penalization constraint violation during latency optimization. The proposed system model sends a sub-group pixel-correlated area of the frame from the UAV to the server rather than the transmission of the whole video frame. The proposed framework utilizes the GCN model to explore hidden representations of feature-correlated groups of pixels. Moreover, the GCN supervises the A2C model, which selects a subgroup to enhance transmission latency, thus supervising the training of UAV actions in A2C. Experimental results show that GCN-assisted A2C reduces video frame transmission latency together with false detection rate in UAV vision systems over other DRL and state-of-the-art models.

Alam Noor, Luis Almeida, Kai Li +3

CISTER Research Center, Porto, Portugal. · Instituto de Telecomunicac¸˜oes, Faculdade de Engenharia, Universidade do Porto, Portugal. · Department of Information Technology, Kennesaw State University, USA. +2

Aerial Robotics Edge Computing Visual RL

Aug 13, 2026cs.RO

FAM-DQ: A Dual-Quadrotor-Based Fully Actuated Aerial Manipulator for High-Torque Interaction

Aerial physical interaction requires aerial manipulation platforms to generate large interaction forces and torques while maintaining precise end-effector control. However, conventional underactuated aerial manipulators suffer from strong position-attitude coupling, whereas fully actuated platform designs often face structural complexity, limited payload capacity, and insufficient torque output. This paper presents FAM-DQ, a dual-quadrotor based fully actuated aerial manipulator designed for high-torque physical interaction tasks. By mounting two quadrotor propulsion modules at the ends of a central frame through passive joints, while using a gear-driven servo to regulate the pointing direction, FAM-DQ achieves decoupled

6

-DoF end-effector control with omnidirectional manipulation capability and enhanced torque output. Experiments including trajectory tracking, attitude tracking, static torque measurement, and screw driving validate the proposed design. FAM-DQ achieves a maximum torque of

1.019~\mathrm{N}\cdot\mathrm{m}

with a total mass of

0.447~\mathrm{kg}

, corresponding to a torque-to-mass ratio of

2.28~\mathrm{N}\cdot\mathrm{m/kg}

.

Xuwei Yang, Ruoyu Ren, Ziqian Guo

Aerial Robotics Quadrotor Control Aerial Manipulation

Aug 11, 2026cs.RO

Nonlinear Model Predictive Control via Sequential Convex Programming for Drone-to-Drone Docking

Autonomous mid-air docking of multi-rotor vehicles under disturbance-driven target motion poses a constrained non-linear trajectory optimization challenge. This work formulates the docking task as a finite-horizon optimal control problem based on a reduced-order nonlinear model augmented with disturbance states. The resulting problem is solved using sequential convex programming within a receding-horizon framework to generate dynamically feasible docking trajectories. State estimation with noisy measurements is incorporated to enable robust relative motion prediction, while trajectory execution is validated in a high-fidelity rigid-body MuJoCo simulation environment. The proposed framework is evaluated for stationary and constant-velocity target motions, demonstrating reliable convergence to the docking interface while satisfying geometric capture constraints. Quantitatively, the method maintains negligible docking-cone violations and terminal state errors within prescribed tolerances, and achieves consistent, safe docking performance for cone half-angles as low as 10 degrees. Robust operation is observed for wind disturbance levels up to a standard deviation of 0.5, while preserving bounded approach velocities and stable control effort. These results demonstrate the effectiveness of the SCP-based trajectory optimization framework for disturbance-robust aerial docking under estimation uncertainty.

Neeraj Balachandar, Shriram Hari, Vishnu R. Unni

Department of Mechanical and Aerospace Engineering, Indian Institute of Technology Hyderabad, Telangana, India

Aerial Robotics Trajectory Optimization Nonlinear MPC

Aug 11, 2026cs.RO

JitTrack: Onboard Multi-Object Tracking Against Viewpoint Jitter for Agile UAVs

Multi-object tracking (MOT) onboard agile unmanned aerial vehicles (UAVs) remains challenging due to severe viewpoint jitter induced by camera ego-motion. Rapid attitude changes during flight often lead to significant target displacement across frames, causing inaccurate target association and degraded tracking performance. Existing UAV MOT methods are primarily evaluated on offline benchmarks and seldom address the practical requirements of real-world onboard deployment, including robustness to camera motion and active target following. To address these challenges, we propose JitTrack, an active onboard multi-object tracking framework that accommodates drone dynamics and camera ego-motion. Built upon a query-based transformer tracker, JitTrack introduces semantic refinement to improve the detection of emerging targets, motion-aware query rectification to compensate for target misalignment caused by viewpoint jitter, and a motion-inspired denoising training strategy that simulates camera motion patterns for robust supervision. Furthermore, we develop a perception-planning-control closed-loop tracking pipeline for real-world deployment, enabling collision-free and physically feasible target following on agile UAVs. Extensive experiments on public UAV MOT benchmarks demonstrate consistent improvements over the baseline method, while real-world flight experiments validate the effectiveness and practicality of JitTrack for robust onboard visual tracking under viewpoint jitter.

Yachun Shan, Feitian Zhang

The Department of Advanced Manufacturing and Robotics, Peking University, Beijing, China

Aerial Robotics Vision-Based Robot Control Multi-Object Tracking

Aug 10, 2026cs.RO

Model-Based Systems Engineering Framework for SysML-Driven Design of Autonomous UAVs

Autonomous Unmanned Aerial Vehicles (UAVs) are complex cyber-physical systems that require the coordinated integration of flight control, navigation, perception, communication, power management, and mission-level decision-making under safety, timing, and reliability constraints. However, many autonomous UAV development workflows still rely on document-centric requirements, separated architectural descriptions, and software implementation artifacts, which can lead to ambiguity, interface inconsistencies, and weak traceability during early design. This paper presents a Model-Based Systems Engineering (MBSE) design framework for the SysML-driven development of autonomous UAVs. The proposed framework uses the Systems Modeling Language (SysML) as a formal design backbone to structure UAV development across four connected layers: stakeholder requirements, functional decomposition, logical architecture, and physical/software allocation. SysML requirement diagrams, activity diagrams, block definition diagrams, internal block diagrams, state machine diagrams, and parametric diagrams are used to capture the functional, structural, behavioral, interface, and performance aspects of the UAV system. The logical architecture is then systematically mapped to a Robot Operating System 2 (ROS 2) software architecture by relating SysML blocks to ROS 2 nodes, flow ports and connectors to topics, request-response interactions to services, and goal-oriented behaviors to actions. The framework is illustrated at the design level using representative autonomous UAV mission scenarios, including autonomous take-off, waypoint navigation, hover stabilization, obstacle avoidance, return-to-home, and emergency handling. The resulting model supports requirement allocation, interface definition, subsystem responsibility assignment, and verification planning before simulation or physical deployment.

Deekshitha Angadi, Naveena Budda, Vikas Agarwal +3

AIR Lab, UAVs Group, Autonomous Robotics Systems Limited, Hyderabad, India · Department of Microelectronics and VLSI Design, University of Hyderabad, Hyderabad, India · Department of IoT, Ideabytes Software India Private Limited, Hyderabad, India +4

Model-Based Systems Engineering Aerial Robotics Robotics

Aug 7, 2026cs.RO

Exact Thrust-Reversal Limits of Bidirectional Propellers under Bounded Motor Inputs

Bidirectional propellers are often treated as signed thrust sources, but their thrust is a signed-quadratic function of rotor speed.Thus, thrust reversal necessarily occurs through zero rotor speed, where the ability of a bounded motor torque to change thrust collapses.This work formalizes this obstruction by studying exact thrust-trajectory reproducibility under bounded motor inputs with prescribed smoothness.We derive a normalized thrust-coordinate model with vanishing input gain at zero thrust, and prove necessary and sufficient reproducibility conditions in terms of the zero-crossing order of the desired thrust.Generic reversals, in which thrust crosses zero with nonzero slope, require unbounded motor input; the resulting conditions provide direct design rules for shaping thrust reversals that avoid singular motor commands.We also derive the corresponding current and voltage regularity requirements for a DC motor driving a bidirectional propeller.Experiments on a motor-propeller setup validate the predicted reversal-order effects, showing localized current/voltage peaks and thrust-tracking degradation for linear reversals, but not for higher-order reversals.These results expose an intrinsic actuator-level limitation that must be considered in force, acceleration, and interaction-control references for aerial robots.

Ahmed Ali, Chiara Gabellieri, Antonio Franchi

Robotics and Mechatronics Department, Electrical Engineering, Mathematics, and Computer Science (EEMCS) Faculty, University of Twente, 7500 AE Enschede, The Netherlands · Department of Computer, Control and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy

Aerial Robotics