Herman Herman

David Guttendorf, D. W. Wilson Hamilton, Anne Harris Heckman et al.

A robotic system of multiple unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) has the potential for advancing autonomous object geolocation performance. Much research has focused on algorithmic improvements on individual components, such as navigation, motion planning, and perception. In this paper, we present a UGV-UAV object detection and geolocation system, which performs perception, navigation, and planning autonomously in real scale in unstructured environment. We designed novel sensor pods equipped with multispectral (visible, near-infrared, thermal), high resolution (181.6 Mega Pixels), stereo (near-infrared pair), wide field of view (192 degree HFOV) array. We developed a novel on-board software-hardware architecture to process the high volume sensor data in real-time, and we built a custom AI subsystem composed of detection, tracking, navigation, and planning for autonomous objects geolocation in real-time. This research is the first real scale demonstration of such high speed data processing capability. Our novel modular sensor pod can boost relevant computer vision and machine learning research. Our novel hardware-software architecture is a solid foundation for system-level and component-level research. Our system is validated through data-driven offline tests as well as a series of field tests in unstructured environments. We present quantitative results as well as discussions on key robotic system level challenges which manifest when we build and test the system. This system is the first step toward a UGV-UAV cooperative reconnaissance system in the future.

Albert Susanto, Herman, Tjeng Wawan Cenggoro et al.

The neural network needs excessive costs of time because of the complexity of architecture when trained on images. Transfer learning and fine-tuning can help improve time and cost efficiency when training a neural network. Yet, Transfer learning and fine-tuning needs a lot of experiment to try with. Therefore, a method to find the best architecture for transfer learning and fine-tuning is needed. To overcome this problem, neuro-evolution using a genetic algorithm can be used to find the best architecture for transfer learning. To check the performance of this study, dataset ChestX-Ray 14 and DenseNet-121 as a base neural network model are used. This study used the AUC score, differences in execution time for training, and McNemar's test to the significance test. In terms of result, this study got a 5% difference in the AUC score, 3 % faster in terms of execution time, and significance in most of the disease detection. Finally, this study gives a concrete summary of how neuro-evolution transfer learning can help in terms of transfer learning and fine-tuning.