In response to the evolving challenges posed by small unmanned aerial vehicles(UAVs),which have the potential to transport harmful payloads or cause significant damage,we present AV-FDTI,an innovative Audio-Visual Fus...In response to the evolving challenges posed by small unmanned aerial vehicles(UAVs),which have the potential to transport harmful payloads or cause significant damage,we present AV-FDTI,an innovative Audio-Visual Fusion system designed for Drone Threat Identification.AV-FDTI leverages the fusion of audio and omnidirectional camera feature inputs,providing a comprehensive solution to enhance the precision and resilience of drone classification and 3D localization.Specifically,AV-FDTI employs a CRNN network to capture vital temporal dynamics within the audio domain and utilizes a pretrained ResNet50 model for image feature extraction.Furthermore,we adopt a visual information entropy and cross-attention-based mechanism to enhance the fusion of visual and audio data.Notably,our system is trained based on automated Leica tracking annotations,offering accurate ground truth data with millimeter-level accuracy.Comprehensive comparative evaluations demonstrate the superiority of our solution over the existing systems.In our commitment to advancing this field,we will release this work as open-source code and wearable AV-FDTI design,contributing valuable resources to the research community.展开更多
This paper considers the scenario where multiple robots collaboratively cover a region in which the exact distribution of workload is unknown prior to the operation.The workload distribution is not uniform in the regi...This paper considers the scenario where multiple robots collaboratively cover a region in which the exact distribution of workload is unknown prior to the operation.The workload distribution is not uniform in the region,meaning that the time required to cover a unit area varies at different locations of the region.In our approach,we divide the target region into multiple horizontal stripes,and the robots sweep the current stripe while partitioning the next stripe concurrently.We propose a distributed workload partition algorithm and prove that the operation time on each stripe converges to the minimum under the discrete-time update law.We conduct comprehensive simulation studies and compare our method with the existing methods to verify the theoretical results and the advantage of the proposed method.Flight experiments on mini drones are also conducted to demonstrate the practicality of the proposed algorithm.展开更多
基金National Research Foundation,Singapore,under its Medium-Sized Center for Advanced Robotics Technology Innovation(CARTIN)under project WP5 within the Delta-NTU Corporate Lab with funding support from A*STAR under its IAF-ICP program(Grant no:I2201E0013)and Delta Electronics Inc.
文摘In response to the evolving challenges posed by small unmanned aerial vehicles(UAVs),which have the potential to transport harmful payloads or cause significant damage,we present AV-FDTI,an innovative Audio-Visual Fusion system designed for Drone Threat Identification.AV-FDTI leverages the fusion of audio and omnidirectional camera feature inputs,providing a comprehensive solution to enhance the precision and resilience of drone classification and 3D localization.Specifically,AV-FDTI employs a CRNN network to capture vital temporal dynamics within the audio domain and utilizes a pretrained ResNet50 model for image feature extraction.Furthermore,we adopt a visual information entropy and cross-attention-based mechanism to enhance the fusion of visual and audio data.Notably,our system is trained based on automated Leica tracking annotations,offering accurate ground truth data with millimeter-level accuracy.Comprehensive comparative evaluations demonstrate the superiority of our solution over the existing systems.In our commitment to advancing this field,we will release this work as open-source code and wearable AV-FDTI design,contributing valuable resources to the research community.
文摘This paper considers the scenario where multiple robots collaboratively cover a region in which the exact distribution of workload is unknown prior to the operation.The workload distribution is not uniform in the region,meaning that the time required to cover a unit area varies at different locations of the region.In our approach,we divide the target region into multiple horizontal stripes,and the robots sweep the current stripe while partitioning the next stripe concurrently.We propose a distributed workload partition algorithm and prove that the operation time on each stripe converges to the minimum under the discrete-time update law.We conduct comprehensive simulation studies and compare our method with the existing methods to verify the theoretical results and the advantage of the proposed method.Flight experiments on mini drones are also conducted to demonstrate the practicality of the proposed algorithm.
