Edge Simultaneous Localization and Mapping (SLAM) retains only the tracking on the mobile device, while offloading the compute-intensive local mapping and loop close to edge computing. Existing Edge SLAM approaches in...
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ISBN:
(纸本)9781665435413
Edge Simultaneous Localization and Mapping (SLAM) retains only the tracking on the mobile device, while offloading the compute-intensive local mapping and loop close to edge computing. Existing Edge SLAM approaches incur relatively high delays for offloading, resulting in high failure probabilities, i.e., low reliability, for commonly used public SLAM datasets. We discovered that two parameters which had not previously been studied in detail, namely the number of features and the number of keyframes that are bundled for a local map update, play a critical role in the offloading delay. Also, previous approaches updated the local map in the mobile device in a serial manner, incurring map update latencies. We study the numbers of features and bundled keyframes in detail and we parallelize the local map update. We find that judicious parameter settings, namely relatively small numbers of features (750 per frame) and bundled keyframes (1, i.e., effectively no bundling), reduce the map update latency to less than half compared to the previously common settings (1000 features per frame and 6 keyframes used for a map update). For a low network latency of 20ms, these judicious parameter settings in conjunction with our parallelized local map updating, reduce the 79% failure rate of the previous Edge SLAM systems down to 2%.
Due to the increase of the complexity and uncertainty in the future sustainable energy system new control algorithms for decentralized acting energy entities are needed. We present an approach of distributed Reinforce...
Due to the increase of the complexity and uncertainty in the future sustainable energy system new control algorithms for decentralized acting energy entities are needed. We present an approach of distributed Reinforcement Learning in a multi-agent setup to find a control strategy of two cooperative agents within an energy cell. In order to practice energy sharing to decrease the energy cell's overall interdependence on the electrical grid, we train two independently learning agents, an energy storage and an electric power generator using Q-learning. We compare the learned strategy of the agents under partial and full observability of the environment and evaluate the interdependence of the energy cell on the electrical grid. Our results show that distributed Q-learning with independently learning agents works in the setup of an energy cell without the necessity of information exchange between agents. The algorithm under partial observability of the environment reaches comparable performance to that of full observability with fewer need of communication but at the cost of five times longer training time.
New ideas supporting the transition of the energy system are needed. To keep the electric grid in stable operation at times of high volatile supply from renewable energy sources one possibility is seen in distributed ...
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ISBN:
(纸本)9781538624111
New ideas supporting the transition of the energy system are needed. To keep the electric grid in stable operation at times of high volatile supply from renewable energy sources one possibility is seen in distributed battery energy storage systems. They provide flexibility as a ancillary service for transmission system operators as well as improving self-sufficiency for residential buildings with photovoltaic systems. This research-in-progress paper presents an approach towards a coordinated multi-agent reinforcement learning-based swarm battery control. The goal is to use reinforcement learning to manage the battery's power flows between the battery, a photovoltaic system, a household's electric load and the electric grid. Our approach includes the use of the battery to offer frequency containment reserve as well as to improve energy self-sufficiency. As a last step, we compare the performance of our algorithm with a rule-based approach defining the same system configuration and objective.
Research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes an essential research product in its...
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