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相关论文: Cooperative Receding Horizon 3D Coverage Control w…

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This work proposes a coverage controller that enables an aerial team of distributed autonomous agents to collaboratively generate non-myopic coverage plans over a rolling finite horizon, aiming to cover specific points on the surface area…

Nowadays, unmanned aerial vehicles or UAVs are being used for a wide range of tasks, including infrastructure inspection, automated monitoring and coverage. This paper investigates the problem of 3D inspection planning with an autonomous…

This work proposes a jointly optimized trajectory generation and camera control approach, enabling an autonomous agent, such as an unmanned aerial vehicle (UAV) operating in 3D environments, to plan and execute coverage trajectories that…

This work introduces a cooperative inspection system designed to efficiently control and coordinate a team of distributed heterogeneous UAV agents for the inspection of 3D structures in cluttered, unknown spaces. Our proposed approach…

In previous work, a Cooperative Receding Horizon (CRH) controller was developed for solving cooperative multi-agent problems in uncertain environments. In this paper, we overcome several limitations of this controller, including potential…

系统与控制 · 计算机科学 2014-09-01 Yasaman Khazaeni , Christos G. Cassandras

In this work we propose a coverage planning control approach which allows a mobile agent, equipped with a controllable sensor (i.e., a camera) with limited sensing domain (i.e., finite sensing range and angle of view), to cover the surface…

We propose a novel probabilistically robust controller for the guidance of an unmanned aerial vehicle (UAV) in coverage planning missions, which can simultaneously optimize both the UAV's motion, and camera control inputs for the 3D…

In this paper, we propose a cost-effective strategy for heterogeneous UAV swarm systems for cooperative aerial inspection. Unlike previous swarm inspection works, the proposed method does not rely on precise prior knowledge of the…

机器人学 · 计算机科学 2024-03-05 Xinhang Xu , Muqing Cao , Shenghai Yuan , Thien Hoang Nguyen , Thien-Minh Nguyen , Lihua Xie

Although a number of solutions exist for the problems of coverage, search and target localization---commonly addressed separately---whether there exists a unified strategy that addresses these objectives in a coherent manner without being…

机器人学 · 计算机科学 2017-08-29 Anastasia Mavrommati , Emmanouil Tzorakoleftherakis , Ian Abraham , Todd D. Murphey

This article addresses the visual area coverage problem using a team of Unmanned Aerial Vehicles (UAVs). The UAVs are assumed to be equipped with a downward facing camera covering all points of interest within a circle on the ground. The…

系统与控制 · 计算机科学 2017-01-05 Sotiris Papatheodorou , Anthony Tzes , Yiannis Stergiopoulos

Cooperative control of multi-UAV systems has attracted substantial research attention due to its significance in various application sectors such as emergency response, search and rescue missions, and critical infrastructure inspection.…

机器人学 · 计算机科学 2024-09-23 Angelos Zacharia , Savvas Papaioannou , Panayiotis Kolios , Christos Panayiotou

We address the multi-agent persistent monitoring problem defined on a set of nodes (targets) interconnected over a network topology. A measure of mean overall node state uncertainty evaluated over a finite period is to be minimized by…

系统与控制 · 电气工程与系统科学 2020-10-06 Shirantha Welikala , Christos G. Cassandras

This article examines the problem of visual area coverage by a network of Mobile Aerial Agents (MAAs). Each MAA is assumed to be equipped with a downwards facing camera with a conical field of view which covers all points within a circle on…

系统与控制 · 计算机科学 2017-11-27 Sotiris Papatheodorou , Anthony Tzes , Yiannis Stergiopoulos

In this paper, a deep structured tracking problem is introduced for a large number of decision-makers. The problem is formulated as a linear quadratic deep structured team, where the decision-makers wish to track a global target…

系统与控制 · 电气工程与系统科学 2021-10-22 Jalal Arabneydi , Amir G. Aghdam

Collaborative 3D object detection exploits information exchange among multiple agents to enhance accuracy of object detection in presence of sensor impairments such as occlusion. However, in practice, pose estimation errors due to imperfect…

计算机视觉与模式识别 · 计算机科学 2023-03-06 Yifan Lu , Quanhao Li , Baoan Liu , Mehrdad Dianati , Chen Feng , Siheng Chen , Yanfeng Wang

In this paper we propose a periodic solution to the problem of persistently covering a finite set of interest points with a group of autonomous mobile agents. These agents visit periodically the points and spend some time carrying out the…

机器人学 · 计算机科学 2024-01-25 José Manuel Palacios-Gasós , Eduardo Montijano , Carlos Sagüés , Sergio Llorente

In this paper, we propose a novel semi-autonomous image sampling strategy, called stealthy coverage control, for human-enabled 3D structure reconstruction. The present mission involves a fundamental problem: while the number of images…

系统与控制 · 电气工程与系统科学 2026-02-03 Reiji Terunuma , Yuta Nakamura , Takuma Abe , Takeshi Hatanaka

In this paper, a novel and innovative methodology for feasible motion planning in the multi-agent system is developed. On the basis of velocity obstacles characteristics, the chance constraints are formulated in the receding horizon control…

机器人学 · 计算机科学 2021-03-25 Xiaoxue Zhang , Jun Ma , Zilong Cheng , Sunan Huang , Tong Heng Lee

This paper addresses the motion planning problem for a team of aerial agents under high level goals. We propose a hybrid control strategy that guarantees the accomplishment of each agent's local goal specification, which is given as a…

机器人学 · 计算机科学 2016-10-05 Christos K. Verginis , Ziwei Xu , Dimos V. Dimarogonas

Camera-only 3D detection provides an economical solution with a simple configuration for localizing objects in 3D space compared to LiDAR-based detection systems. However, a major challenge lies in precise depth estimation due to the lack…

计算机视觉与模式识别 · 计算机科学 2023-03-27 Yue Hu , Yifan Lu , Runsheng Xu , Weidi Xie , Siheng Chen , Yanfeng Wang
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