Intelligent cooperative control for urban tracking with Unmanned Air Vehicles

Cook, Kevin; Bryan, Everett; Yu, Huili; Bai, He; Seppi, Kevin; Beard, Randal W.

doi:10.1109/icuas.2013.6564667

Cited by 12 publications

(9 citation statements)

References 5 publications

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“…• ground, non-moving robots platforms with movable components such as the Baxter platforms; 8 • ground, moving, human-like robots legged, humanoid or human-like robots such as Pepper, 9 ASI-MO, 10 or the Boston Dynamics suites; 11 • ground, moving, wheeled robots robots provided with wheels, such as the Starship deliverers, the Robotnik Summit suites 12 or Komodo, 13 which are not necessarily human-like; • aerial robots drones provided with rotors, such as the Erle Robotics's copters 14 or the Parrot drones; 15 • marine robots vessels used for underwater activities such as the Searobotics suite. 16 Robot actions Here, we focus on which actions are mostly performed by robots in Robot-City Interaction scenarios. We consider high-level actions, re-adapted based on the work of [98]: Levels of autonomy This dimension aims at analyzing which degree of autonomy the agents taking part to a Robot-City Interaction scenario exhibit.…”

Section: Agent Contextmentioning

confidence: 99%

“…15 https://www.parrot.com/uk#drones-fpv. 16 http://www.searobotics.com/. 17 Originally considered as social interaction by [98], we believe that "verbal communication" and "acquisition" should be considered separately due to the variety and complexity of city scenarios.…”

Section: Agent Contextmentioning

confidence: 99%

“…This is likely due to the presence of the citizens, which might naturally be more inclined to share the urban space with human-friendly presences. Aerial platforms-employed both as single units [16,26,28,76] and in combination with other platforms [60], are less common, likely due to the rather recent emergence of drones as reliable platforms, as well as to the regulations currently restricting their usage in public spaces. The ability of robots to perform different actions is analyzed in Fig.…”

Section: Agent Contextmentioning

confidence: 99%

See 2 more Smart Citations

Robot–City Interaction: Mapping the Research Landscape—A Survey of the Interactions Between Robots and Modern Cities

Tiddi

Bastianelli²,

Daga

et al. 2019

Int J of Soc Robotics

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The goal of this work is to describe how robots interact with complex city environments, and to identify the main characteristics of an emerging field that we call Robot-City Interaction (RCI). Given the central role recently gained by modern cities as use cases for the deployment of advanced technologies, and the advancements achieved in the robotics field in recent years, we assume that there is an increasing interest both in integrating robots in urban ecosystems, and in studying how they can interact and benefit from each others. Therefore, our challenge becomes to verify the emergence of such area, to assess its current state and to identify the main characteristics, core themes and research challenges associated with it. This is achieved by reviewing a preliminary body of work contributing to this area, which we classify and analyze according to an analytical framework including a set of key dimensions for the area of RCI. Such review not only serves as a preliminary state-of-the-art in the area, but also allows us to identify the main characteristics of RCI and its research landscape.

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Section: Agent Contextmentioning

confidence: 99%

Section: Agent Contextmentioning

confidence: 99%

Section: Agent Contextmentioning

confidence: 99%

See 1 more Smart Citation

Robot–City Interaction: Mapping the Research Landscape—A Survey of the Interactions Between Robots and Modern Cities

Tiddi

Bastianelli²,

Daga

et al. 2019

Int J of Soc Robotics

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“…Several approaches have been presented for multi-target tracking with UAS, as for instance [7], [8], [9], [5], [10]. *This work has been supported by the FP7 EC-SAFEMOBIL Project (grant number 288082) and the PAIS-MultiRobot (TIC-7390) Regional Project.…”

Section: Introductionmentioning

confidence: 99%

Decentralized cooperation of multiple UAS for multi-target surveillance under uncertainties

Capitán

Merino

Ollero

2014

2014 International Conference on Unmanned Aircraft Systems (ICUAS)

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An interesting applications of Unmanned Aerial Systems is surveillance. Surveillance typically involves the tracking of one or several targets in an area. A key issue for this application is the autonomous decision-making to allocate UAS to targets and determine the actions to be performed by the UAS of the fleet. Since this optimal decision-making needs to deal with uncertainties, Partially Observable Markov Decision Processes (POMDPs) are proposed as models for the surveillance mission. The paper proposes a role-assignment method to alleviate the computational complexity of the application of POMDPs to multi-UAS surveillance. The method is evaluated by simulation and compared with other similar approaches. Furthermore, the system has been implemented in a testbed with real quadcopters to show some preliminary experiments of two UAS tracking two different targets.

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“…A coordinated control method was proposed to solve the problem of cooperative control of multiple heterogeneous autonomous in [21]. In [22], an intelligent cooperative control system for unmanned air vehicles was introduced based on the machine learning component. In [23], the decentralized coordinated control method was presented based on the strongly nonlinear and coupled characteristics of multi-infeed DC (MIDC) system.…”

Section: Literature Reviewmentioning

confidence: 99%

A Cooperative Control Method for Fully Mechanized Mining Machines Based on Fuzzy Logic Theory and Neural Networks

Tan

Zhou³

et al. 2014

Advances in Mechanical Engineering

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In a fully mechanized mining face, the coordinated control of coal mining machines has a significant promoting effect to perfect the mining environment and improve the efficiency of coal production and has become a research focus all over the world. In this paper, a cooperative control method based on the integration of fuzzy logic theory and neural networks was proposed. The improved Elman neural network (ENN) through a threshold strategy was presented to predict the running parameters of coal mining machines. On the basis of coupling analysis of coal mining machines, the expert knowledge base of scraper conveyor was established based on fuzzy logic theory. Furthermore, the probabilistic neural network (PNN) was applied to evaluate the running status of scraper conveyor, and the cooperative control flow was designed and analyzed. Finally, a simulation example was provided and the comparison results illustrated that the proposed method was feasible and superior to the manual control.

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Intelligent cooperative control for urban tracking with Unmanned Air Vehicles

Cited by 12 publications

References 5 publications

Robot–City Interaction: Mapping the Research Landscape—A Survey of the Interactions Between Robots and Modern Cities

Robot–City Interaction: Mapping the Research Landscape—A Survey of the Interactions Between Robots and Modern Cities

Decentralized cooperation of multiple UAS for multi-target surveillance under uncertainties

A Cooperative Control Method for Fully Mechanized Mining Machines Based on Fuzzy Logic Theory and Neural Networks

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