High-Level Tracking of Autonomous Underwater Vehicles Based on Pseudo Averaged Q-Learning

2018 ◽  
Author(s):  
Wenjie Shi ◽  
Shiji Song ◽  
Cheng Wu
Keyword(s):  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Q Learning ◽  
High Level

HIGH-LEVEL AUTONOMY FOR AUTONOMOUS UNDERWATER VEHICLES

2007 ◽  
Vol 40 (17) ◽  
pp. 38-43
Author(s):  
Hans-Ulrich Kobialka ◽  
Walter Nowak
Keyword(s):  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
High Level

scholarly journals Two-Level Evolutionary Approach to Persistent Surveillance for Multiple Underwater Vehicles with Energy Constraints

2019 ◽  
Vol 18 (2) ◽  
pp. 267-301 ◽  
Author(s):  
Igor Bychkov ◽  
Maksim Kenzin ◽  
Nikolai Maksimkin
Keyword(s):  
Large Scale ◽  
Group Performance ◽  
Autonomous Underwater Vehicles ◽  
Extended Period ◽  
Underwater Vehicles ◽  
Evolutionary Approach ◽  
Point Selection ◽  
Selection Scheme ◽  
Battery Capacity ◽  
High Level

Currently, the coordinated use of autonomous underwater vehicles groups seems to be the most promising and ambitious technology to provide a solution to the whole range of oceanographic problems. Complex and large-scale underwater operations usually involve long stay activities of robotic groups under the limited vehicle’s battery capacity. In this context, available charging station within the operational area is required for long-term mission implementation. In order to ensure a high level of group performance capability, two following problems have to be handled simultaneously and accurately – to allocate all tasks between vehicles in the group and to determine the recharging order over the extended period of time. While doing this, it should be taken into account, that the real world underwater vehicle systems are partially self-contained and could be subjected to any malfunctions and unforeseen events. The article is devoted to the suggested two-level dynamic mission planner based on the rendezvous point selection scheme. The idea is to divide a mission on a series of time-limited operating periods with the whole group rendezvous at the end of each period. The high-level planner’s objective here is to construct the recharging schedule for all vehicles in the group ensuring well-timed energy replenishment while preventing the simultaneous charging of a plenitude of robots. Based on this schedule, mission is decomposed to assign group rendezvous to each regrouping event (robot leaving the group for recharging or joining the group after recharging). This scheme of periodic rendezvous allows group to keep up its status regularly and to re-plan current strategy, if needed, almost on-the-fly. Low-level planner, in return, performs detailed group routing on the graph-like terrain for each operating period under vehicle’s technical restrictions and task’s spatiotemporal requirements. In this paper, we propose the evolutionary approach to decentralized implementation of both path planners using specialized heuristics, solution improvement techniques, and original chromosome-coding scheme. Both algorithm options for group mission planner are analyzed in the paper; the results of computational experiments are given.

scholarly journals Towards Reducing the Impact of Localisation Errors on the Behaviour of a Swarm of Autonomous Underwater Vehicles

MENDEL ◽  
2020 ◽  
Vol 26 (2) ◽  
pp. 1-8
Author(s):  
Tarek El-Mihoub ◽  
Christoph Tholen ◽  
Lars Nolle
Keyword(s):  
Search Algorithm ◽  
Autonomous Underwater Vehicles ◽  
Search Space ◽  
Underwater Vehicles ◽  
Search Algorithms ◽  
Cooperative Search ◽  
Global View ◽  
High Level ◽  
The Impact ◽  
Search Information

Localisation errors have a great impact on Autonomous Underwater Vehicles (AUVs) as search agents. Different approaches for solving the localisation problem can be used and combined together for greater accuracy in estimating AUVs’ locations. The effect of localisation errors on locating a target can be lightened by designing a search algorithm that avoids extensive use of exact lo-cation information. In this paper, two cooperative search algorithms are proposed and evaluated. In these algorithms, a high-level mechanism is employed for building a global view of the search space using minimum possible search information. These algorithms rely on low-level search algorithms with exploring roles. Particle Swarm Optimisation (PSO) and all-to-one Self-Organising Migrating Algorithm (SOMA) are selected as high-level mechanisms. The conducted experiments demonstrate that both algorithms show a robust behaviour within a range of localisation errors.

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