Autonomous VTOL-UAV Docking System for Heterogeneous Multirobot Team

Narvaez, Eduardo; Ravankar, Ankit A.; Ravankar, Abhijeet; Emaru, Takanori; Kobayashi, Yukinori

doi:10.1109/tim.2020.3039649

Cited by 41 publications

(33 citation statements)

References 48 publications

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“…The works mentioned in Table I mostly used state-machine or planning-based algorithms, resulting in possible limitations on robustness. To name a few, the study conducted in [8] used a procedure with state identification and a state-based rapidly exploring random tree star (RRT*) algorithm. Another work [11] used statistical adaptive methods.…”

Section: Simulated Experiments 1) Experiments On Different Mechanismsmentioning

confidence: 99%

Asymmetric Self-Play-Enabled Intelligent Heterogeneous Multirobot Catching System Using Deep Multiagent Reinforcement Learning

Gao

Chen

et al. 2023

IEEE Trans. Robot.

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Aiming to develop a more robust and intelligent heterogeneous system for adversarial catching in security and rescue tasks, in this article, we discuss the specialities of applying asymmetric self-play and curriculum learning techniques to deal with the increasing heterogeneity and number of different robots in modern heterogeneous multirobot systems (HMRS). Our method, based on actor-critic multiagent reinforcement learning, provides a framework that can enable cooperative behaviors among heterogeneous multirobot teams. This leads to the development of an HMRS for complex catching scenarios that involve several robot teams and real-world constraints. We conduct simulated experiments to evaluate different mechanisms' influence on our method's performance, and real-world experiments to assess our system's performance in complex real-world catching problems. In addition, a bridging study is conducted to compare our method with a state-of-the-art method called S2M2 in heterogeneous catching problems, and our method performs better in adversarial settings. As a result, we show that the proposed framework, through fusing asymmetric self-play and curriculum learning during training, is able to successfully complete the HMRS catching task under realistic constraints in both simulation and the real world, thus providing a direction for future large-scale intelligent security & rescue HMRS.

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Section: Simulated Experiments 1) Experiments On Different Mechanismsmentioning

confidence: 99%

Asymmetric Self-Play-Enabled Intelligent Heterogeneous Multirobot Catching System Using Deep Multiagent Reinforcement Learning

Gao

Chen

et al. 2023

IEEE Trans. Robot.

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“…The first ones are ground-moving docking stations. Some of them are Unmanned Ground Vehicles which have landing systems mounted on, like in [65][66][67][68][69][70][71]. The UGV can be used to swap the battery of a UAV, like in [72] or to wirelessly charge the battery- [35,73,74].…”

Section: Mobile Docking Stationmentioning

confidence: 99%

A Decade of UAV Docking Stations: A Brief Overview of Mobile and Fixed Landing Platforms

Grlj

Krznar²,

Pranjic

2022

Drones

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Unmanned Aerial Vehicles have advanced rapidly in the last two decades with the advances in microelectromechanical systems (MEMS) technology. It is crucial, however, to design better power supply technologies. In the last decade, lithium polymer and lithium-ion batteries have mainly been used to power multirotor UAVs. Even though batteries have been improved and are constantly being improved, they provide fairly low energy density, which limits multirotors’ UAV flight endurance. This problem is addressed and is being partially solved by using docking stations which provide an aircraft to land safely, charge (or change) the batteries and to take-off as well as being safely stored. This paper focuses on the work carried out in the last decade. Different docking stations are presented with a focus on their movement abilities. Rapid advances in computer vision systems gave birth to precise landing systems. These algorithms are the main reason that docking stations became a viable solution. The authors concluded that the docking station solution to short ranges is a viable option, and numerous extensive studies have been carried out that offer different solutions, but only some types, mainly fixed stations with storage systems, have been implemented and are being used today. This can be seen from the commercially available list of docking stations at the end of this paper. Nevertheless, it is important to be aware of the technologies being developed and implemented, which can offer solutions to a vast number of different problems.

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“…The attachment of the UAV to the flange of the MRM was made possible by a special interlocking magnetic connection. The same authors validated their system in [114] with real scenario experiments. In addition to the simulation in [113], the authors extended their former approach with a control strategy, path planning algorithms for UAVs and MRMs, and simulating physical contact forces between robots for a robust design of a docking device.…”

Section: Landing the Uav On An Mrmmentioning

confidence: 99%

Systematic literature review of applications and usage potentials for the combination of unmanned aerial vehicles and mobile robot manipulators in production systems

Sinnemann

Boshoff

Dyrska

et al. 2022

Prod. Eng. Res. Devel.

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The cooperation of Unmanned Aerial Vehicles (UAVs) and Mobile Robot Manipulators (MRMs) offers enormous possibilities to modern industry. It paves the way for logistics, cooperative assembling or manipulation and will provide even more flexibility and autonomy to today’s manufacturing processes. Currently, some systematic literature reviews exist that provide an overview on research fields and gaps in the field of UAVs and MRMs. However, an investigation of the research landscape for combined use of UAVs and MRMs does not exist to the best of the authors’ knowledge. Therefore, in this paper, a systematic review of the current research landscape for the combined use of UAV and MRM is conducted to finally identify fields of action that need to be addressed in the future to harness the full potential.

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Autonomous VTOL-UAV Docking System for Heterogeneous Multirobot Team

Cited by 41 publications

References 48 publications

Asymmetric Self-Play-Enabled Intelligent Heterogeneous Multirobot Catching System Using Deep Multiagent Reinforcement Learning

Asymmetric Self-Play-Enabled Intelligent Heterogeneous Multirobot Catching System Using Deep Multiagent Reinforcement Learning

A Decade of UAV Docking Stations: A Brief Overview of Mobile and Fixed Landing Platforms

Systematic literature review of applications and usage potentials for the combination of unmanned aerial vehicles and mobile robot manipulators in production systems

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