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Thanks to recent advances in generative AI, computers can now simulate realistic and complex natural processes. We apply this capability to predict how wildfires spread, a task made difficult by the unpredictable nature of fire and the…

机器学习 · 计算机科学 2026-03-24 Wenbo Yu , Anirbit Ghosh , Tobias Sebastian Finn , Rossella Arcucci , Marc Bocquet , Sibo Cheng

Contrary to the stunning feats observed in birds of prey, aerial manipulation and grasping with flying robots still lack versatility and agility. Conventional approaches using rigid manipulators require precise positioning and are subject…

机器人学 · 计算机科学 2023-08-15 Samuel Ubellacker , Aaron Ray , James Bern , Jared Strader , Luca Carlone

The aerial manipulator (AM) is a systematic operational robotic platform in high standard on algorithm robustness. Directly deploying the algorithms to the practical system will take numerous trial and error costs and even cause destructive…

机器人学 · 计算机科学 2021-03-22 Fengyu Quan , Huisheng Huang , Hongjie Zeng , Haoyao Chen , Yunhui Liu

In this paper, we examine the problem of robotic manipulation of granular media. We evaluate multiple predictive models used to infer the dynamics of scooping and dumping actions. These models are evaluated on a task that involves…

机器人学 · 计算机科学 2017-10-27 Connor Schenck , Jonathan Tompson , Dieter Fox , Sergey Levine

Early detection of wildfires is essential to prevent large-scale fires resulting in extensive environmental, structural, and societal damage. Uncrewed aerial vehicles (UAVs) can cover large remote areas effectively with quick deployment…

Due to climate change, the extreme wildfire has become one of the most dangerous natural hazards to human civilization. Even though, some wildfires may be initially caused by human activity, but the spread of wildfires is mainly determined…

机器学习 · 计算机科学 2025-03-13 Qijun Chen , Shaofan Li

In the robotic crop harvesting environment, foreign objects intrusion in the gripper workspace is frequently occurring and unignorable, however, rarely addressed. This paper presents a novel intelligent robotic grasping method capable of…

机器人学 · 计算机科学 2021-10-19 Hongyu Zhou , Xing Wang , Hanwen Kang , Chao Chen

Drone swarms coupled with data intelligence can be the future of wildfire fighting. However, drone swarm firefighting faces enormous challenges, such as the highly complex environmental conditions in wildfire scenes, the highly dynamic…

计算机与社会 · 计算机科学 2024-11-26 Shijie Pan , Aoran Cheng , Yiqi Sun , Kai Kang , Cristobal Pais , Yulun Zhou , Zuo-Jun Max Shen

In recent years, the increasing prevalence and intensity of wildfires have posed significant challenges to emergency response teams. The utilization of unmanned aerial vehicles (UAVs), commonly known as drones, has shown promise in aiding…

机器人学 · 计算机科学 2023-12-07 Chang Liu , Tamas Sziranyi

Most existing robotic harvesters utilize a unimanual approach; a single arm grasps the crop and detaches it, either via a detachment movement, or by cutting its stem with a specially designed gripper/cutter end-effector. However, such…

机器人学 · 计算机科学 2022-09-14 Sotiris Stavridis , Dimitrios Papageorgiou , Leonidas Droukas , Zoe Doulgeri

Wildfires are destructive and inflict massive, irreversible harm to victims' lives and natural resources. Researchers have proposed commissioning unmanned aerial vehicles (UAVs) to provide firefighters with real-time tracking information;…

机器人学 · 计算机科学 2019-03-19 Esmaeil Seraj , Andrew Silva , Matthew Gombolay

Robots are increasingly being deployed in agriculture to support sustainable practices and improve productivity. They offer strong potential to enable precise, efficient, and environmentally friendly operations. However, most existing…

机器人学 · 计算机科学 2026-03-31 Stephane Ngnepiepaye Wembe , Vincent Rousseau , Johann Laconte , Roland Lenain

The application of autonomous robots in agriculture is gaining increasing popularity thanks to the high impact it may have on food security, sustainability, resource use efficiency, reduction of chemical treatments, and the optimization of…

The increasing frequency and severity of wildfires highlight the need for accurate fire and plume spread models. We introduce an approach that effectively isolates and tracks fire and plume behavior across various spatial and temporal…

计算机视觉与模式识别 · 计算机科学 2024-12-13 Daryn Sagel , Bryan Quaife

Precision agriculture relies heavily on effective weed management to ensure robust crop yields. This study presents RoWeeder, an innovative framework for unsupervised weed mapping that combines crop-row detection with a noise-resilient deep…

计算机视觉与模式识别 · 计算机科学 2024-10-10 Pasquale De Marinis , Gennaro Vessio , Giovanna Castellano

This paper introduces a novel and general method to address the problem of using a general-purpose robot manipulator with a parallel gripper to wrap a deformable linear object (DLO), called a rope, around a rigid object, called a rod,…

机器人学 · 计算机科学 2023-04-12 Zhaoyuan Ma , Jing Xiao

We present a formal mathematical multi-agent modeling framework for autonomously combating a wildland fire with unmanned aerial vehicles. The problem is formulated as a collaboration between a drone and a helicopter equipped with a tanker.…

最优化与控制 · 数学 2023-01-18 Lawrence Thul , Warren B Powell

A particular type of assistive robots designed for physical interaction with objects could play an important role assisting with mobility and fall prevention in healthcare facilities. Autonomous mobile manipulation presents a hurdle prior…

机器人学 · 计算机科学 2020-11-12 Roya Sabbagh Novin , Amir Yazdani , Andrew Merryweather , Tucker Hermans

Modern herbicide application in agricultural settings typically relies on either large scale sprayers that dispense herbicide over crops and weeds alike or portable sprayers that require labor intensive manual operation. The former method…

The accumulation of litter is increasing in many places and is consequently becoming a problem that must be dealt with. In this paper, we present a manipulator robotic system to collect litter in outdoor environments. This system has three…

机器人学 · 计算机科学 2024-07-17 Ignacio de Loyola Páez-Ubieta , Julio Castaño-Amorós , Santiago T. Puente , Pablo Gil