Decentralized formation control with connectivity maintenance and collision avoidance under limited and intermittent sensing

A decentralized switched controller is developed for dynamic agents to perform global formation configuration convergence while maintaining network connectivity and avoiding collision within agents and between stationary obstacles, using only local feedback under limited and intermittent sensing. Due to the intermittent sensing, constant position feedback may not be available for agents all the time. Intermittent sensing can also lead to a disconnected network or collisions between agents. Using a navigation function framework, a decentralized switched controller is developed to navigate the agents to the desired positions while ensuring network maintenance and collision avoidance.Comment: 8 pages, 2 figures, submitted to ACC 201

Energy extraction from rotating regular black hole via magnetic reconnection

Recently, it has been demonstrated that magnetic reconnection processes in the ergosphere of a Kerr black hole can provide us with a promising mechanism for extracting the rotational energy from it. In this paper, we study the energy extraction from the the newly proposed rotating regular black holes via this magnetic reconnection mechanism. This novel rotating regular black hole has an exponential convergence factor $e^{-k/r}$ on the mass term characterized by the regular parameter $k$ in the exponent. We explore the effects of this regular parameter on the magnetic reconnection as well as other critical parameters determining the magnetic reconnection process. The parameter spaces allowing energy extraction to occur are investigated. The power, efficiency and the power ratio to the Blandford-Znajek mechanism are studied. The results show that the regularity of the rotating black hole has significant effects on the energy extraction via the magnetic reconnection mechanism.Comment: 8 pages, 9 figure

Lightweight Neural Path Planning

Learning-based path planning is becoming a promising robot navigation methodology due to its adaptability to various environments. However, the expensive computing and storage associated with networks impose significant challenges for their deployment on low-cost robots. Motivated by this practical challenge, we develop a lightweight neural path planning architecture with a dual input network and a hybrid sampler for resource-constrained robotic systems. Our architecture is designed with efficient task feature extraction and fusion modules to translate the given planning instance into a guidance map. The hybrid sampler is then applied to restrict the planning within the prospective regions indicated by the guide map. To enable the network training, we further construct a publicly available dataset with various successful planning instances. Numerical simulations and physical experiments demonstrate that, compared with baseline approaches, our approach has nearly an order of magnitude fewer model size and five times lower computational while achieving promising performance. Besides, our approach can also accelerate the planning convergence process with fewer planning iterations compared to sample-based methods.Comment: 8 page