Object tracking is one of the major tasks for mobile robots in many real-world applications.Also,artificial intelligence and automatic control techniques play an important role in enhancing the performance of mobile r...Object tracking is one of the major tasks for mobile robots in many real-world applications.Also,artificial intelligence and automatic control techniques play an important role in enhancing the performance of mobile robot navigation.In contrast to previous simulation studies,this paper presents a new intelligent mobile robot for accomplishing multi-tasks by tracking red-green-blue(RGB)colored objects in a real experimental field.Moreover,a practical smart controller is developed based on adaptive fuzzy logic and custom proportional-integral-derivative(PID)schemes to achieve accurate tracking results,considering robot command delay and tolerance errors.The design of developed controllers implies some motion rules to mimic the knowledge of experienced operators.Twelve scenarios of three colored object combinations have been successfully tested and evaluated by using the developed controlled image-based robot tracker.Classical PID control failed to handle some tracking scenarios in this study.The proposed adaptive fuzzy PID control achieved the best accurate results with the minimum average final error of 13.8 cm to reach the colored targets,while our designed custom PID control is efficient in saving both average time and traveling distance of 6.6 s and 14.3 cm,respectively.These promising results demonstrate the feasibility of applying our developed image-based robotic system in a colored object-tracking environment to reduce human workloads.展开更多
Radio spectrum awareness,including understanding radio signal activities,is crucial for improving spectrum utilization,detecting security vulnerabilities,and supporting adaptive transmissions.Related tasks include spe...Radio spectrum awareness,including understanding radio signal activities,is crucial for improving spectrum utilization,detecting security vulnerabilities,and supporting adaptive transmissions.Related tasks include spectrum sensing,identifying systems and terminals,and understanding various protocol layers.In this paper,we investigate various identification and classification tasks related to fading channel parameters,signal distortions,Medium Access Control(MAC)protocols,radio signal types,and cellular systems.Specifically,we utilize deep learning methods in those identification and classification tasks.Performance evaluations demonstrate the effectiveness of deep learning in those radio spectrum awareness tasks.展开更多
基金The authors extend their appreciation to the Deanship of Scientific Research at Shaqra University for funding this research work through the Project Number(SU-ANN-2023016).
文摘Object tracking is one of the major tasks for mobile robots in many real-world applications.Also,artificial intelligence and automatic control techniques play an important role in enhancing the performance of mobile robot navigation.In contrast to previous simulation studies,this paper presents a new intelligent mobile robot for accomplishing multi-tasks by tracking red-green-blue(RGB)colored objects in a real experimental field.Moreover,a practical smart controller is developed based on adaptive fuzzy logic and custom proportional-integral-derivative(PID)schemes to achieve accurate tracking results,considering robot command delay and tolerance errors.The design of developed controllers implies some motion rules to mimic the knowledge of experienced operators.Twelve scenarios of three colored object combinations have been successfully tested and evaluated by using the developed controlled image-based robot tracker.Classical PID control failed to handle some tracking scenarios in this study.The proposed adaptive fuzzy PID control achieved the best accurate results with the minimum average final error of 13.8 cm to reach the colored targets,while our designed custom PID control is efficient in saving both average time and traveling distance of 6.6 s and 14.3 cm,respectively.These promising results demonstrate the feasibility of applying our developed image-based robotic system in a colored object-tracking environment to reduce human workloads.
文摘Radio spectrum awareness,including understanding radio signal activities,is crucial for improving spectrum utilization,detecting security vulnerabilities,and supporting adaptive transmissions.Related tasks include spectrum sensing,identifying systems and terminals,and understanding various protocol layers.In this paper,we investigate various identification and classification tasks related to fading channel parameters,signal distortions,Medium Access Control(MAC)protocols,radio signal types,and cellular systems.Specifically,we utilize deep learning methods in those identification and classification tasks.Performance evaluations demonstrate the effectiveness of deep learning in those radio spectrum awareness tasks.
