Abstract
This paper tackles a Bayesian Decision Making approach for unmanned aerial vehicle (UAV) mission that allows UAV to quickly react to unexpected events under dynamic environments. From online observations and the mission state-ment, the proposed approach is designed by means of Dynamic Bayesian Networks (DBN) arising from the safety or performance failures analysis. After proposing a DBN model, a probabilistic approach based on Multiple-Criteria Decision-Making (MCDM) is then applied to find the best configuration reaching a balance between performance and energy consumption, thus decide which tasks will be implemented as SW and which as HW execution units, regarding the mission requirement. The proposal UAV mission decision-making is three-pronged, providing: (1) real time image pre-processing of sensor observations; (2) temporal and probabilistic approach based on Bayesian Networks to continuously update the mission plan during the flight; and (3) low-power hardware and software implementations for online and real time embedded Decision Making using Xilinx System on Programmable Chip (SoPC) platform. The proposed approach is then validated with a practical case UAV mission planning using the proposed dynamic decision-maker implemented on embedded system based on a hybrid device.
Highlights
Autonomous system, such as drone known as unmanned aerial vehicle (UAV), is a robot system composed of several components, fly autonomously according to a pre-programmed mission statement in unknown environment
Once the mission plan is updated, we propose a new Multiple-Criteria Decision-Making (MCDM) model to generate a suitable HW/SW configuration for the mission tasks regarding to performance constraints and resource availability that will allow the development of real time mission decision making module, on System-on-Chip (SoC) using Field-programmable gate arrays (FPGAs) for deployment on a UAV under extreme and uncertain environmental conditions
Our work provides a novel approach of combining Dynamic Bayesian Networks (DBN) with utilizing MCDM techniques to choose between an HW (FPGA) or SW implementation of the mission tasks
Summary
Autonomous system, such as drone known as UAV, is a robot system composed of several components (of hardware or software), fly autonomously according to a pre-programmed mission statement in unknown environment. Now-a-days, UAVs are being more widely used in different applications (military, aerospace reconnaissance, environmental and meteorological monitoring, aerial photography, and geophysical survey, etc.) achieving high performance and reliability combined with reduced size, weight, power consumption and cost. To execute successfully such missions, it was essential that the vehicle able to select appropriate scenario planning under consideration of the current state of the mission and uncertain environmental conditions. The UAVs are generally set to perform a particular mission under several requirements and environmental conditions (unexpected obstacles, weather changes and sensor or hardware/software component failures, etc.). We have focused on combining sensors and algorithms to understand the vehicle environment and to provide some autonomous processing directly into the UAV and achieve the necessary processing power to run the algorithms near the sensors
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