Control System Framework for Autonomous Robots Based on Extended State Machines

Merz, Torsten; Rudol, Piotr; Wzorek, Mariusz

doi:10.1109/icas.2006.19

Cited by 28 publications

(28 citation statements)

References 5 publications

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“…In 2011, the system capability allowed it to follow a pre-set flight plan, taking images at a fixed time separation of 4 s, which was the maximum capture rate of the digital cameras, for any chosen sectors of a manually-created flight plan [24]. At this stage, the take-off and landing were operator controlled.…”

Section: Robotic Helicoptermentioning

confidence: 99%

“…The key criteria measure for the robotic helicopter system was dependability, with the development being facilitated by the modelling of all system behaviour using an extended state machine software framework [24]. For new flight plans, all critical components can be ground-tested before flight to minimize the risk of failure.…”

Section: Robotic Helicoptermentioning

confidence: 99%

“…During landing, the helicopter flies to an elevation of approximately 15 m above the pre-set landing site, and the operator needs only to control the descent rate of the helicopter, with the system controlling all other position factors. While completely autonomous landings would be possible with a system suggested by Merz et al (2006) [25], we decided not to sacrifice payload capacity and endurance for the integration of the landing system. Moreover, it is safer for the operator to observe and control the last part of the vertical descent in order to anticipate ideal conditions for landing, e.g., to avoid wind gusts at the exact time of landing, as these can be seen coming across the field by the operator.…”

Section: Robotic Helicoptermentioning

confidence: 99%

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Pheno-Copter: A Low-Altitude, Autonomous Remote-Sensing Robotic Helicopter for High-Throughput Field-Based Phenotyping

et al. 2014

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Plant breeding trials are extensive (100s to 1000s of plots) and are difficult and expensive to monitor by conventional means, especially where measurements are time-sensitive. For example, in a land-based measure of canopy temperature (hand-held infrared thermometer at two to 10 plots per minute), the atmospheric conditions may change greatly during the time of measurement. Such sensors measure small spot samples (2 to 50 cm 2 ), whereas image-based methods allow the sampling of entire plots (2 to 30 m 2 ). Capturing images from an aircraft which is flown precisely at low altitude (10 to 40 m) to obtain high ground resolution data for every plot allows the rapid measurement of large numbers of plots. This OPEN ACCESSAgronomy 2014, 4 280 paper outlines the implementation of a customized robotic helicopter (gas-powered, 1.78-m rotor diameter) with autonomous flight control and software to plan flights over experiments that were 0.5 to 3 ha in area and, then, to extract, straighten and characterize multiple experimental field plots from images taken by three cameras. With a capacity to carry 1.5 kg for 30 min or 1.1 kg for 60 min, the system successfully completed >150 flights for a total duration of 40 h. Example applications presented here are estimations of the variation in: ground cover in sorghum (early season); canopy temperature in sugarcane (mid-season); and three-dimensional measures of crop lodging in wheat (late season). Together with this hardware platform, improved software to automate the production of ortho-mosaics and digital elevation models and to extract plot data would further benefit the development of high-throughput field-based phenotyping systems.

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Section: Robotic Helicoptermentioning

confidence: 99%

Section: Robotic Helicoptermentioning

confidence: 99%

Section: Robotic Helicoptermentioning

confidence: 99%

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Pheno-Copter: A Low-Altitude, Autonomous Remote-Sensing Robotic Helicopter for High-Throughput Field-Based Phenotyping

et al. 2014

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“…All four platforms are fully autonomous and have been deployed. Previous work has focused on the development of robust autonomous systems for UAV's which seamlessly integrate control, reactive and deliberative capabilities that meet the requirements of hard and soft realtime constraints [17,55]. Additionally, we have focused on the development and integration of many high-level autonomous capabilities studied in the area of cognitive robotics such as task planners [18,19], motion planners [66][67][68], execution monitors [21], and reasoning systems [20,23,54], in addition to novel middleware frameworks which support such integration [40,42,43].…”

Section: Introductionmentioning

confidence: 99%

A Delegation-Based Architecture for Collaborative Robotics

Doherty

Heintz

Landén

2011

Agent-Oriented Software Engineering XI

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Abstract. Collaborative robotic systems have much to gain by leveraging results from the area of multi-agent systems and in particular agent-oriented software engineering. Agent-oriented software engineering has much to gain by using collaborative robotic systems as a testbed. In this article, we propose and specify a formally grounded generic collaborative system shell for robotic systems and human operated ground control systems. Collaboration is formalized in terms of the concept of delegation and delegation is instantiated as a speech act. Task Specification Trees are introduced as both a formal and pragmatic characterization of tasks and tasks are recursively delegated through a delegation process implemented in the collaborative system shell. The delegation speech act is formally grounded in the implementation using Task Specification Trees, task allocation via auctions and distributed constraint problem solving. The system is implemented as a prototype on Unmanned Aerial Vehicle systems and a case study targeting emergency service applications is presented.

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“…To tackle the problem of complexity we decompose a system in hardware and software components which can be individually tested against a specification (Figure 2). For modelling system behavior and seamless integration of software components considering realtime constraints we use the ESM software framework (Merz et al, 2006). To minimize risks, critical components have to pass ground tests before they are flight tested and all glitches observed on the ground and during flight are thoroughly analyzed.…”

Section: System Components and Flight Servicesmentioning

confidence: 99%

Autonomous Unmanned Helicopter System for Remote Sensing Missions in Unknown Environments

Merz

Chapman

2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper presents the design of an autonomous unmanned helicopter system for low-altitude remote sensing. The proposed concepts and methods are generic and not limited to a specific helicopter. The development was driven by the need for a dependable, modular, and affordable system with sufficient payload capacity suitable for both research and real-world deployment. The helicopter can be safely operated without a backup pilot in a contained area beyond visual range. This enables data collection in inaccessible or dangerous areas. Thanks to its terrain following and obstacle avoidance capability, the system does not require a priori information about terrain elevation and obstacles. Missions are specified in state diagrams and flight plans. We present performance characteristics of our system and show results of its deployment in real-world scenarios. We have successfully completed several dozen infrastructure inspection missions and crop monitoring missions facilitating plant phenomics studies.

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Control System Framework for Autonomous Robots Based on Extended State Machines

Cited by 28 publications

References 5 publications

Pheno-Copter: A Low-Altitude, Autonomous Remote-Sensing Robotic Helicopter for High-Throughput Field-Based Phenotyping

Pheno-Copter: A Low-Altitude, Autonomous Remote-Sensing Robotic Helicopter for High-Throughput Field-Based Phenotyping

A Delegation-Based Architecture for Collaborative Robotics

Autonomous Unmanned Helicopter System for Remote Sensing Missions in Unknown Environments

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