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arXiv 最近完成扫描:2026-11-16 01:34 · 全站最近入库:2026-11-14 15:09各信源进度 →

统计口径与数据新鲜度

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版本更新 · v2arXiv:2509.24921

CineWild: Balancing Art and Robotics for Ethical Wildlife Documentary Filmmaking

Pablo Pueyo, Fernando Caballero, Ana Cristina Murillo, Eduardo Montijano

原文摘要

Unmanned aerial vehicles (UAVs) have become powerful tools across domains from industry to the arts. In documentary filmmaking, they offer dynamic, otherwise unreachable perspectives, transforming how stories are told. Wildlife documentaries especially benefit, yet UAVs also raise ethical concerns like the risk of disturbing the animals they aim to capture. This paper introduces CineWild, a model predictive control (MPC) formulation for autonomous aerial cinematography that combines robotics, cinematography, and ethics. Building on an MPC solution that jointly controls drone motion and camera focal length, CineWild dynamically adjusts flight paths and camera settings to balance cinematic quality with animal welfare. Key features include adaptive zoom for filming from acoustic and visual safe distances, path-planning that avoids an animal's field of view, and smooth, low-noise maneuvers. CineWild exemplifies interdisciplinary innovation, bridging engineering, visual storytelling, and environmental ethics. We evaluate the formulation in simulation, with ablation studies and a comparison against a cinematographic baseline and a welfare-aware baseline under identical weights, and we demonstrate the perception and camera-actuation pipeline in real-world proof-of-concept trials. We will release the code.

cs.RO · cs.MM

版本更新 · v2arXiv:2607.13624

From Language to Navigation Goals: A Vision-Language Approach for Semantic Navigation of Mobile Robots Using RGB-D Perception

Jose Martínez-Fajardo, Pablo Pueyo, Fernando Caballero, Luis Merino

原文摘要

Natural language interaction provides an intuitive way for non-expert users to communicate with robotic platforms. However, transforming user requests into executable navigation actions remains a challenging task, requiring the integration of language understanding, environment perception, and autonomous navigation. This work presents a language-driven navigation framework that enables mobile robots to interpret user requests in natural language to move the robot to a destination and autonomously navigate towards it. The framework is composed of modular ROS 2 components that cooperate to transform natural language instructions into navigation actions. Given a natural language request referring to a target in the environment (e.g., "go to the mail box"), the system identifies the referenced object, estimates its position using RGB-D data, and generates a navigation goal, which is then executed through the ROS 2 Nav2 navigation stack. The ROS 2-based implementation facilitates portability across different robotic platforms, requiring only the configuration of the corresponding topics and services. The system is evaluated in both simulation and real-world scenarios using a TurtleBot3 Waffle and a Unitree Go2 robot with a RealSense camera. Experimental results show that the framework successfully interprets both direct commands and contextual requests, generates meaningful natural-language feedback, and navigates towards the desired target. These results demonstrate the feasibility of combining semantic perception and autonomous navigation to provide an intuitive human-robot interaction paradigm. Code will be released as open source upon acceptance.

cs.RO · cs.AI