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This work focuses on low-energy collision avoidance and formation maintenance in autonomous swarms of drones. Here, the two main problems are: 1) how to avoid collisions by temporarily breaking the formation, i.e., collision avoidance reformation, and 2) how do such reformation while minimizing the deviation resulting in minimization of the overall time and energy consumption of the drones. To address the first question, we use cellular automata based technique to find an efficient formation that avoids the obstacle while minimizing the time and energy. Concerning the second question, a near-optimal reformation of the swarm after successful collision avoidance is achieved by applying a temperature function reduction technique, originally used in the point set registration process. The goal of the reformation process is to remove the disturbance while minimizing the overall time it takes for the swarm to reach the destination and consequently reducing the energy consumption required by this operation. To measure the degree of formation disturbance due to collision avoidance, deviation of the centroid of the swarm formation is used, inspired by the concept of the center of mass in classical mechanics. Experimental results show the efficiency of the proposed technique, in terms of performance and energy.
This work focuses on the formation reshaping in an optimized manner in autonomous swarm of drones. Here, the two main problems are: 1) how to break and reshape the initial formation in an optimal manner, and 2) how to do such reformation while minimi
The focus of this work is to present a novel methodology for optimal distribution of a swarm formation on either side of an obstacle, when evading the obstacle, to avoid overpopulation on the sides to reduce the agents waiting delays, resulting in a
Formation and collision avoidance abilities are essential for multi-agent systems. Conventional methods usually require a central controller and global information to achieve collaboration, which is impractical in an unknown environment. In this pape
This paper studies the collision avoidance problem for autonomous multiple fixedwing UAVs in the complex integrated airspace. By studying and combining the online path planning method, the distributed model predictive control algorithm, and the geome
Cross-lingual alignment of word embeddings play an important role in knowledge transfer across languages, for improving machine translation and other multi-lingual applications. Current unsupervised approaches rely on similarities in geometric struct