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Recently, centralized receding horizon online multi-robot coverage path planning algorithms have shown remarkable scalability in thoroughly exploring large, complex, unknown workspaces with many robots. In a horizon, the path planning and…

机器人学 · 计算机科学 2025-07-30 Ratijit Mitra , Indranil Saha

In autonomous navigation of mobile robots, sensors suffer from massive occlusion in cluttered environments, leaving significant amount of space unknown during planning. In practice, treating the unknown space in optimistic or pessimistic…

机器人学 · 计算机科学 2021-03-30 Lizi Wang , Hongkai Ye , Qianhao Wang , Yuman Gao , Chao Xu , Fei Gao

Proper path planning is the first step of robust and efficient autonomous navigation for mobile robots. Meanwhile, it is still challenging for robots to work in a complex environment without complete prior information. This paper presents…

机器人学 · 计算机科学 2020-09-17 Wenjian Hao , Rongyao Wang , Alexander Krolicki , Yiqiang Han

In this paper, we present an innovative technique for the path planning of flying robots in a 3D environment in Rough Mereology terms. The main goal was to construct the algorithm that would generate the mereological potential fields in…

机器人学 · 计算机科学 2024-05-16 Aleksandra Szpakowska , Piotr Artiemjew

Mobile robots dedicated in security tasks should be capable of clearly perceiving their environment to competently navigate within cluttered areas, so as to accomplish their assigned mission. The paper in hand describes such an autonomous…

This paper proposes a novel mission planning platform, capable of efficiently deploying a team of UAVs to cover complex-shaped areas, in various remote sensing applications. Under the hood lies a novel optimization scheme for grid-based…

In large unknown environments, search operations can be much more time-efficient with the use of multi-robot fleets by parallelizing efforts. This means robots must efficiently perform collaborative mapping (exploration) while…

机器人学 · 计算机科学 2023-03-07 Indraneel Patil , Rachel Zheng , Charvi Gupta , Jaekyung Song , Narendar Sriram , Katia Sycara

Hierarchical, multi-resolution volumetric mapping approaches are widely used to represent large and complex environments as they can efficiently capture their occupancy and connectivity information. Yet widely used path planning methods…

机器人学 · 计算机科学 2026-02-25 Victor Reijgwart , Cesar Cadena , Roland Siegwart , Lionel Ott

Future crewed missions to the Moon will face significant environmental and operational challenges, posing risks to the safety and performance of astronauts navigating its inhospitable surface. Whilst head-up displays (HUDs) have proven…

This document presents the study conducted during the European Moon Rover System Pre-Phase A project, in which we have developed a lunar rover system, with a modular approach, capable of carrying out different missions with different…

In this paper we propose a planner for 3D exploration that is suitable for applications using state-of-the-art 3D sensors such as lidars, which produce large point clouds with each scan. The planner is based on the detection of a frontier -…

机器人学 · 计算机科学 2021-09-15 Ana Batinović , Tamara Petrović , Antun Ivanovic , Frano Petric , Stjepan Bogdan

This work describes the implementation of a simple and computationally efficient Intelligent Navigation System (INS) for autonomous systems used in areas where human access is impossible. The system uses Laser Range Finder (LRF) readings as…

In this paper, emerging deep learning techniques are leveraged to deal with Mars visual navigation problem. Specifically, to achieve precise landing and autonomous navigation, a novel deep neural network architecture with double branches…

计算机视觉与模式识别 · 计算机科学 2018-08-28 Jiang Zhang , Yuanqing Xia , Ganghui Shen

Surveillance and exploration of large environments is a tedious task. In spaces with limited environmental cues, random-like search is an effective approach as it allows the robot to perform online coverage of environments using simple…

机器人学 · 计算机科学 2022-11-15 Karan Sridharan , Patrick McNamee , Zahra Nili Ahmadabadi , Jeffrey Hudack

In this work, we propose the use of Ground Penetrating Radar (GPR) for rover localization on Mars. Precise pose estimation is an important task for mobile robots exploring planetary surfaces, as they operate in GPS-denied environments.…

机器人学 · 计算机科学 2025-11-04 Anja Sheppard , Katherine A. Skinner

Exploration of Mars has been made possible using a series of landers, rovers and orbiters. The HiRise camera on the Mars Reconnaissance Orbiter (MRO) has captured high-resolution images covering large tracts of the surface. However, orbital…

天体物理仪器与方法 · 物理学 2019-02-18 Adrien Bouskela , Aman Chandra , Jekan Thangavelautham , Sergey Shkarayev

This paper presents a novel multi-robot coverage path planning (CPP) algorithm - aka SCoPP - that provides a time-efficient solution, with workload balanced plans for each robot in a multi-robot system, based on their initial states. This…

机器人学 · 计算机科学 2021-03-30 Leighton Collins , Payam Ghassemi , Ehsan T. Esfahani , David Doermann , Karthik Dantu , Souma Chowdhury

Unmanned Aerial Vehicles (UAVs) represent a new frontier in a wide range of monitoring and research applications. To fully leverage their potential, a key challenge is planning missions for efficient data acquisition in complex…

机器人学 · 计算机科学 2020-01-10 Marija Popovic , Teresa Vidal-Calleja , Gregory Hitz , Jen Jen Chung , Inkyu Sa , Roland Siegwart , Juan Nieto

This work presents an innovative solution for robotic odometry, path planning and exploration in wild unknown environments, focusing on digital modelling. The approach uses a minimum cost formulation with pseudo-randomly generated…

机器人学 · 计算机科学 2024-03-29 Naizhong Zhang. Yaoqiang Pan , Yangwen Jin , Peiqi Jin , Kewei Hu , Xiao Huang , Hanwen Kang

Path planning is one of the most vital elements of mobile robotics, providing the agent with a collision-free route through the workspace. The global path plan can be calculated with a variety of informed search algorithms, most notably the…

人工智能 · 计算机科学 2015-11-04 Alexander Lavin