Communication-efficient and crash-quiescent Omega with unknown membership

The failure detector class Omega (Ω) provides an eventual leader election functionality, i.e., eventually all correct processes permanently trust the same correct process. An algorithm is communication-efficient if the number of links that carry messages forever is bounded by n, being n the number of processes in the system. It has been defined that an algorithm is crash-quiescent if it eventually stops sending messages to crashed processes. In this regard, it has been recently shown the impossibility of implementing Ω crash quiescently without a majority of correct processes. We say that the membership is unknown if each process pi only knows its own identity and the number of processes in the system (that is, i and n), but pi does not know the identity of the rest of processes of the system. There is a type of link (denoted by ADD link) in which a bounded (but unknown) number of consecutive messages can be delayed or lost. In this work we present the first implementation (to our knowledge) of Ω in partially synchronous systems with ADD links and with unknown membership. Furthermore, it is the first implementation of Ω that combines two very interesting properties: communication-efficiency and crash-quiescence when the majority of processes are correct. Finally, we also obtain with the same algorithm a failure detector () such that every correct process eventually and permanently outputs the set of all correct processes.

UAS See-and-Avoid Strategy using a Fuzzy Logic Controller Optimized by Cross-Entropy in Scaling Factors and Membership Functions

This work aims to develop a novel Cross-Entropy (CE) optimization-based fuzzy controller for Unmanned Aerial Monocular Vision-IMU System (UAMVIS) to solve the seeand- avoid problem using its accurate autonomous localization information. The function of this fuzzy controller is regulating the heading of this system to avoid the obstacle, e.g. wall. In the Matlab Simulink-based training stages, the Scaling Factor (SF) is adjusted according to the specified task firstly, and then the Membership Function (MF) is tuned based on the optimized Scaling Factor to further improve the collison avoidance performance. After obtained the optimal SF and MF, 64% of rules has been reduced (from 125 rules to 45 rules), and a large number of real flight tests with a quadcopter have been done. The experimental results show that this approach precisely navigates the system to avoid the obstacle. To our best knowledge, this is the first work to present the optimized fuzzy controller for UAMVIS using Cross-Entropy method in Scaling Factors and Membership Functions optimization.

UAS See-and-Avoid Strategy using a Fuzzy Logic Controller Optimized by Cross-Entropy in Scaling Factors and Membership Functions.

This work aims to develop a novel Cross-Entropy (CE) optimization-based fuzzy controller for Unmanned Aerial Monocular Vision-IMU System (UAMVIS) to solve the seeand-avoid problem using its accurate autonomous localization information. The function of this fuzzy controller is regulating the heading of this system to avoid the obstacle, e.g. wall. In the Matlab Simulink-based training stages, the Scaling Factor (SF) is adjusted according to the specified task firstly, and then the Membership Function (MF) is tuned based on the optimized Scaling Factor to further improve the collison avoidance performance. After obtained the optimal SF and MF, 64% of rules has been reduced (from 125 rules to 45 rules), and a large number of real flight tests with a quadcopter have been done. The experimental results show that this approach precisely navigates the system to avoid the obstacle. To our best knowledge, this is the first work to present the optimized fuzzy controller for UAMVIS using Cross-Entropy method in Scaling Factors and Membership Functions optimization.