A Multi-Agent System for Autonomous Adaptive Control of a Flapping-Wing Micro Air Vehicle

Greenwood, Garrison W.; Podhradský, Michal; Gallagher, John C.; Matson, Eric T.

doi:10.1109/ssci.2015.154

Cited by 3 publications

(5 citation statements)

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“…Each of these activities is associated with its own control strategy. Planners of this kind are being used in a wide range of applications including, music improvisation [31], flight control [32], [33], emergency obstacle avoidance [34], and intelligent transportation systems [35]. For crystal harvesting with a RodBot, these activities can be enumerated as [21].…”

Section: Planning Architecturementioning

confidence: 99%

Automated Particle Collection for Protein Crystal Harvesting

Zeydan

Petruska

Somm

et al. 2017

IEEE Robot. Autom. Lett.

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A robust automated system to collect protein crystals for X-ray crystallography is presented. This system uses an ultra violet imaging system based on commercial off the shelf components, a magnetically manipulated tool, and a resilient behavior-based controller. The system is validated by collecting over 350 polystyrene beads, used as crystal emulators, and transporting them 2mm to a predefined goal in a 14 hour period without human intervention. The average time to identify, collect, transport, and deliver a crystal emulator is 2.4 minutes, similar to an expert operator. This is the first demonstration of a completely automated robust system for protein crystal harvesting.

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Section: Planning Architecturementioning

confidence: 99%

Automated Particle Collection for Protein Crystal Harvesting

Zeydan

Petruska

Somm

et al. 2017

IEEE Robot. Autom. Lett.

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“…Ev decides whether the controller agent's rule base has to be adapted. The MAS diagram is shown in Figure 5, for more details about the MAS refer to [21].…”

Section: Multi-agent Control Systemmentioning

confidence: 99%

“…After learning control parameters for the basic movements the multi-agent system [21] can perform autonomous waypoint following. The experiment is held in a large water tank to lock the vehicle into 2D of freedom and to mechanically low-pass noise in the control system by using water surface as a damper.…”

Section: Waypoint Followingmentioning

confidence: 99%

“…The waypoints are virtual and dynamically placed in the experiment area. For more details about the experimental setup, the reader should see [21].…”

Section: Waypoint Followingmentioning

confidence: 99%

“…The immediate next steps in our work are to implement obstacle avoidance mechanism in the multi-agent system, so the vehicle is able to operate in an environment with obstacles, being more closely related to a real application. The second step is to implement in-flight learning and fault-detection function as proposed in [21], so the vehicle can recover from certain faults and finish its mission even if it sustains damage in the control system.…”

Section: Future Workmentioning

confidence: 99%

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An Evolutionary Approach to Tuning a Multi-Agent System for Autonomous Adaptive Control of a Flapping-Wing Micro Air Vehicle

Podhradský

Greenwood

2016

Int. J. Robot. Autom. Technol.

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Biomimetic flapping wing vehicles have attracted recent interest because of their numerous potential military and civilian applications. In this paper, we describe an evolutionary approach to tuning a Multi-Agent System for autonomous adaptive control of a Flapping-Wing Micro Air Vehicle. The wings of the vehicle are controlled by a split cycle oscillator, which combined with non-linearities and differences between each vehicle, brings significant challenge for selecting the proper parameters for the control system. Adopting a Neo-Darwinistic evolutionary approach, where solutions are evolved in a similar manner as in nature, allows us to precisely learn control parameters for each vehicle. After describing the evolution algorithm and evolving the control parameters, we utilize these values for autonomous waypoint following by the micro air vehicle.

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