Reactive Navigation in Cluttered Indoor Environment for Autonomous MAVs

Prophet, Silvia; Trommer, Gert F.

doi:10.23919/icins43215.2020.9133812

Cited by 3 publications

(11 citation statements)

References 18 publications

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“…Ten papers focus on the planning method as a graph-based approach, using a cost function over the graph's vertices to determine which point to reach. Among the graph-based methods, tree-search algorithms were found, such as Rapidly-exploring Random Tree (RRT) (Dang et al, 2018;Prophet and Trommer, 2020), its improved version RRT* (Alarcon et al, 2021), Dijkstra algorithm (Bershadsky and Johnson, 2013), and frontierbased exploration (Bachrach et al, 2010). The main idea behind graph-based methods is to go from one node to another, traveling the shortest obstaclefree path through vertex cost analysis between the required nodes.…”

Section: Planning Methodsmentioning

confidence: 99%

“…One article uses a tracking-only method (Fu et al, 2013) to report the target's position from the visual input information. Another custom implementations use Density-Based Spatial Clustering of Applications with Noise (DBSCAN) approach (Chen and Lu, 2022;Prophet and Trommer, 2020) to produce surrounding information from laser data. Another method processes the sensor data a spatial-structure information (Zhao et al, 2021b) to solve the autonomous navigation problem.…”

Section: Perception Methodsmentioning

confidence: 99%

“…Aerial vehicle sensor data can vary from 1-dimensional to multi-dimensional, with the latter being the more computationally expensive. Moreover, different descriptors from multi-dimensional Others: Custom controlling Cao (2015); Dang et al (2018); Prophet and Trommer (2020) data can be found, e.g., the internal vehicle control state, objects on the scene, or the surroundings' structure. Different papers address the perception through diverse methods, most of which are probabilistic models to obtain sensor information robust to noise.…”

Section: Tasks For Autonomous Object-goal Navigationmentioning

confidence: 99%

“…(2022);Al-Jarrah et al (2013);Bershadsky and Johnson (2013);Bhusal et al (2022);Capi et al (2021);Cocchioni et al (2014);Fuentes et al (2014);Helgesen et al (2019);Kumar et al (2020);Li et al (2018);Michaelsen et al (2011Michaelsen et al ( , 2012; Nagarani et al (2020); Nakamura et al (2016); Narazaki et al (2022); Tsiourva and Papachristos (2020); Xu et al (2013); Yu et al (2017); Zhang and Duan (2014) (2022); Cho et al (2022); Delamer et al (2021); Mansouri et al (2020a); Meng et al (2019); Nakamura et al (2016, 2017); Perez-Grau et al (2021); Zhu et al (2018) (2022); Albanese et al (2022); Bhusal et al (2022); Dang et al (2018, 2020); Fu et al (2022); Grando et al (2020); LI and HU (2021); Mansouri et al (2020b); Mumuni et al (2022); Peake et al (2020); You et al (2020); Zaki et al (2017)Other: Custom sensor process Chen and Lu (2022);Fu et al (2013);Prophet and Trommer (2020);Zhao et al (2021b) …”

mentioning

confidence: 99%

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UAV Control in Autonomous Object-Goal Navigation: A Systematic Literature Review

Ayala

Portela

Buarque

et al. 2023

Preprint

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Autonomous control for unmanned aerial vehicles (UAVs) has rapidly raised research interest in the last decade. Widely used in civilian, military, and private areas, UAVs have been implemented in surveillance, search and rescue, and delivery tasks, solving problems where a significant space must be covered and traveled. However, using UAVs for navigation problems with full autonomy in control, planning, localization, and mapping tasks remains an open challenge. This paper presents a systematic literature review on object-goal navigation using autonomous UAVs. Object-goal navigation inherits the same navigation challenges, such as control and navigation, in addition to estimating the target's location. The object-search problem requires to understand what object must be found and where it can be located.In this regard, survey taxonomies were found for the tasks and methods behind navigation and target localization problems using UAVs. This review reveals essential issues related to autonomous navigation task dependencies and gaps in UAV development and framework standardization. Open challenges for autonomous UAV control under object-goal navigation must address the research on finding methods for problems, such as autonomy level and comparison metrics, considering safety, ethics, and legal implications.

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Section: Planning Methodsmentioning

confidence: 99%

Section: Perception Methodsmentioning

confidence: 99%

Section: Tasks For Autonomous Object-goal Navigationmentioning

confidence: 99%

mentioning

confidence: 99%

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UAV Control in Autonomous Object-Goal Navigation: A Systematic Literature Review

Ayala

Portela

Buarque

et al. 2023

Preprint

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show abstract

“…They enable to guide a robot under very dynamic conditions at the search optimality cost, that is, reactive navigation is suitable mainly on short distances, and if rapid responses are possible, else a collision can occur. Thus we can find their use in the navigation of robotic cars or drones in dense and cluttered environments [53][54][55]. However, most navigation methods rely on sensory data obtained from own onboard sensors.…”

Section: Reactive Navigation In the Concept Of Ubiquitous Roboticsmentioning

confidence: 99%

Means of IoT and Fuzzy Cognitive Maps in Reactive Navigation of Ubiquitous Robots

et al. 2021

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Development of accessible and cheap sensors as well as the possibility to transfer and process huge amounts of data offer new possibilities for many areas utilizing till now conventional approaches. Navigation of robots and autonomous vehicles is no exception in this aspect and Internet of Things (IoT), together with the means of computational intelligence, represents a new way for construction and use of robots. In this paper, the possibility to move sensors from robots to their surroundings with the help of IoT is presented and the modification of the IoT concept in the form of intelligent space as well as the concept of ubiquitous robot are shown in the paper. On an example of route tracking, we will clarify the potential of distributed networked sensors and processing their data with the use of fuzzy cognitive maps for robotic navigation. Besides, two modifications of adaptation approaches, namely particle swarm optimization and migration algorithm, are presented here. A series of simulations was performed, which are discussed and future research directions are proposed.

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UAV control in autonomous object-goal navigation: a systematic literature review

Ayala,

Portela,

Buarque

et al. 2024

Artif Intell Rev

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Research interest in autonomous control of unmanned aerial vehicles (UAVs) has increased rapidly over the past decade. They are now widely used in civilian, military, and private areas. Applications include surveillance, search and rescue, and delivery tasks. More broadly, they excel at solving problems where a significant amount of space must be covered and traveled. However, using UAVs to solve navigation problems with full autonomy necessitates the mastering of complex subtasks. A solution that includes control, planning, localization, and mapping remains an open challenge. Object-goal navigation contains the same navigation problems where the main objective is to reach a target object. The search and identification of this target are central to the vehicle’s navigation. This requires an understanding of what it is and where it can be located to move around the scene. This paper presents a systematic literature review on object-goal navigation and its subtasks, using autonomous UAVs. Survey taxonomies were found for the tasks and methods behind navigation and target localization problems using UAVs. The review analyzed 67 articles found between 2011 and 2022. They were found in the ACM, IEEE Xplore, WebOfScience, Science Direct, and Scopus databases. This review revealed essential issues related to autonomous navigation task dependencies. Moreover, it highlighted gaps in UAV development and framework standardization. Open challenges for autonomous UAV control for object-goal navigation must address the research on finding methods for problems. For example, autonomy level and comparison metrics, considering safety, ethics, and legal implications.

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Reactive Navigation in Cluttered Indoor Environment for Autonomous MAVs

Cited by 3 publications

References 18 publications

UAV Control in Autonomous Object-Goal Navigation: A Systematic Literature Review

UAV Control in Autonomous Object-Goal Navigation: A Systematic Literature Review

Means of IoT and Fuzzy Cognitive Maps in Reactive Navigation of Ubiquitous Robots

UAV control in autonomous object-goal navigation: a systematic literature review

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