Airborne sensor and perception management: Experiments and Results for surveillance UAS

Ruß, Martin; Schmitt, Marc; Hellert, Christian; Stütz, Peter

doi:10.2514/6.2013-5144

Cited by 15 publications

(9 citation statements)

References 2 publications

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“…If such a difference exists, the cell might contain an object like a tree or a vehicle. This region of interest can be further processed with other sensors such as EO and IR 11 .…”

Section: Object Detection Conceptmentioning

confidence: 99%

LIDAR-based Object Detection on Small UAV: Integration, Experimentation and Results

Ruß¹,

Vohla²,

Stuetz³

et al. 2012

Infotech@Aerospace 2012

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In this article we will present a LIDAR detection approach tailored to the platform constraints of Mini-UAVs in terms of SWaP issues (Size, Weight, and Power). The overall objective is the integration of a real-time range based perception module, which can be combined with other sensors such as EO or IR. As benchmark application a detection task for car-sized objects was selected. In order to implement this capability, a functional chain was set up consisting of the following major processing steps: Synchronization of LIDAR and IMU data, inertial correction of the LIDAR scan using the UAV attitude and position, accumulation of single LIDAR scans within an earth-fixed point cloud. The detection of car-like objects was then accomplished by comparing cell elevation mean values with neighboring cells applying heuristic thresholds. The article will provide details on the data acquisition and processing implementation, system integration issues as well as the experimental setup and results.

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“…If such a difference exists, the cell might contain an object like a tree or a vehicle. This region of interest can be further processed with other sensors such as EO and IR 11 .…”

Section: Object Detection Conceptmentioning

confidence: 99%

LIDAR-based Object Detection on Small UAV: Integration, Experimentation and Results

Ruß¹,

Vohla²,

Stuetz³

et al. 2012

Infotech@Aerospace 2012

Self Cite

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“…Results are transmitted back to the agent to allow their integration on a mission level. 16 The Perception Management consists of two layers. The upper layer receives perception tasks from the cognitive agent as the mission manager and deducts chains, consisting of requirements for sensor deployment and algorithms for the processing of sensor data.…”

Section: Perception Managementmentioning

confidence: 99%

UAV Capability Management using Agent Supervisory Control

Clauß

Kriegel

Schulte

2013

AIAA Infotech@Aerospace (I@A) Conference

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The key to mission performance is the capability of a UAV operator to command and monitor automation as well as his ability to coordinate its actions towards achieving the current task. In order to reproduce such capabilities onboard an unmanned aircraft to a certain extent, we present an approach to introduce an Artificial Cognitive Unit (ACU), acting as an onboard agent supervisor. The ACU commands and controls all onboard automation and acts as a single mission management system subordinate to the human supervisor. For this purpose the term "Agent Supervisory Control" is introduced. In this context onboard automation represents UAV capabilities that can be deployed by the agent supervisor to achieve tasks issued by the operator. These capabilities are managed by the ACU according to their availability, to task and to system constraints. The implementation of the cognitive agent and its automation components, integrated into a UAV system architecture is described. The system has been tested in real-flight experiments and results on in-mission planning and decision making are presented.

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“…The perception capabilities that are offered to the agent strictly depend on the hardware and software components implemented on the aircraft. For this purpose the system uses modifiable perception chains that are created and configured to perform a requested task [24]. The feedback information of the perception management varies between raw sensor output and pre-processed perception results on a symbolic level.…”

Section: Routeplanner Management (Rpm)mentioning

confidence: 99%

Implications for operator interactions in an agent supervisory control relationship

Clauß

Schulte

2014

2014 International Conference on Unmanned Aircraft Systems (ICUAS)

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In order to support a human UAV operator during his mission management task, an artificial cognitive agent (ACU), operated on the basis of high-level tasks, is introduced onboard an unmanned automated aircraft. In analogy to human UAV guidance, the ACU does not interact directly with the aircraft, but rather formulates discrete commands for the automation and continually supervises their execution. In accordance with Sheridan's Human Supervisory Control, we describe this relationship between the agent and the underlying automation by the term of Agent Supervisory Control. In this role, the ACU performs Supervisory Functions, with which the tasks of the operator are supplemented, reviewed and finally decomposed into single actions. These actions are then performed using the onboard conventional automation. The agent considers the automation functions of the UAV as capabilities that are managed and incorporated into its planning and execution process in accordance to their availability and requirements. During task execution, the ACU supplies the operator with information, as to keep him in charge of the aircraft at all times. The extent, form and frequency of the information depend on the necessities of the specific situation and the current task. The information requirements of the operator and a possible operator information-overload due to its detail must be weighed against each other.

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Airborne sensor and perception management: Experiments and Results for surveillance UAS

Cited by 15 publications

References 2 publications

LIDAR-based Object Detection on Small UAV: Integration, Experimentation and Results

LIDAR-based Object Detection on Small UAV: Integration, Experimentation and Results

UAV Capability Management using Agent Supervisory Control

Implications for operator interactions in an agent supervisory control relationship

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