A dynamically adjustable autonomic agent framework. Mostafa SA, Ahmad MS, Annamalai M, Ahmad A, Gunasekaran SS (2013). Springer International Publishing, pp 545–556 Formulating dynamic agents’ operational state via situation awareness assessment. Mostafa SA, Ahmad MS, Annamalai M, Ahmad A, Gunasekaran SS (2015). In ISAMSR, 2015 international symposium on IEEE, pp 53–58 An autonomy viability assessment matrix for agent-based autonomous systems. Mostafa SA, Ahmad MS, Ahmad A, Annamalai M, Gunasekaran SS (2015). In ISAMSR, 2016 2nd international symposium on IEEE, pp 106–111 A Flexible human-agent interaction model for supervised autonomous systems. Mostafa SA, Ahmad MS, Ahmad A, Annamalai M, Gunasekaran SS (2016). Murthy R, Suresh H, Song C Learning control policies for quadcopter navigation with battery constraints. Mostafa SA, Ahmad MS, Mustapha A (2019a) Adjustable autonomy: a systematic literature review. In: International symposium on agent, multi-agent systems and robotics, IEEE, 2018, pp 1−6 A real-time autonomous flight navigation trajectory assessment for unmanned aerial vehicles. Mostafa SA, Mustapha A, Shamsudin AU, Ahmad A, Ahmad MS, Gunasekaran SS (2018). Mostafa SA, Ahmad MS, Mustapha A, Mohammed MA (2017) Formulating layered adjustable autonomy for unmanned aerial vehicles. López E, García S, Barea R, Bergasa LM, Molinos EJ, Arroyo R, Pardo S (2017) A multi-sensorial simultaneous localization and mapping (SLAM) system for low-cost micro aerial vehicles in GPS-denied environments. International conference on emerging trends and innovations in engineering and technological research (ICETIETR), IEEE, 2018 Krishnan RA, Jisha VR, Gokulnath K (2018) Path planning of an autonomous quadcopter based delivery system. Haque A, Elsaharti A, Elderini T, Elsaharty MA, Neubert J (2020) UAV autonomous localization using macro-features matching with a CAD model. In: 2015 International conference on unmanned aircraft systems, IEEE, pp 338–334. High-speed vision-based autonomous indoor navigation of a quadcopter. Garcia A, Mattison E, and Ghose K (2015). Guo K, Qiu Z, Miao C, Zaini AH, Chen CL, Meng W, Xie L (2016) Ultra-wideband-based localization for quadcopter navigation. Comput Electr Eng 74:196–209įlight plan tutorial: learn how to plan your flights!, Parrot blog drone technology (2015) įranklin S, Strain S, McCall R, Baars B (2013) Conceptual commitments of the LIDA model of cognition. Comput Electr Eng 74:184–195ĭ’Urso F, Santoro C, Santoro FF (2019) An integrated framework for the realistic simulation of multi-UAV applications. Springer, Cham, pp 155−183ĭ’Souza JM, Guruprasad KR, Padman A (2019) A realistic simulation platform for multi-quadcopter search using downward facing cameras. Ros-based approach for unmanned vehicles in civil applications. The AFC agent detects and identifies all the assigned objects with a recall score of 1.00, a precision score of 0.9563, an accuracy score of 0.9573, an F1 score of 0.9776, an efficiency score of 0.5239, a detection total time score of 225.5 s, and an identification total time of 275 s and outperforms a human operator.Īl-Kaff A, Moreno FM, Hussein A (2019). We conduct tests on the AFC agent, and the results show that the agent successfully controls the UAV in three performed test cases and a total of nine implemented missions. It captures the video images acquired from a solitary onboard, front-facing camera which are handled off-board on a computer. The agent implements several image handling algorithms to detect and identify objects from their colors and shapes. The AFC agent performs search and survey missions that entail commanding the UAV while performing object classifications and recognition tasks. We design the AFC agent architecture to consist of data acquisition, perception, localization, mapping, control, and planning modules. The specific problem of this research is the indoor environment because of the perplexing characteristics of the required flight mechanics. Consequently, this research addresses the general problem of designing an agent-based autonomous flight control (AFC) architecture of a UAV to facilitate autonomous routing/navigation in uncharted and unascertained environments of organized foyer surroundings. One of the major challenges in designing an autonomous agent system is to achieve the objective of recreating human-like cognition by exploiting the growing pragmatic architectures that act intelligently and intuitively in vital fields.
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