Autonomous system (mathematics)

About: Autonomous system (mathematics) is a research topic. Over the lifetime, 1648 publications have been published within this topic receiving 38373 citations.

System and method for identifying addresses to modify and the effects thereof

Abstract: A system and method identifies ranges of addresses that can be reallocated and/or split to cause traffic to be redirected in a network to reduce the utilization of the most utilized links in an autonomous system. The system and method then identifies the utilization of the autonomous system after the reallocation and/or split and reports on the differences of utilization pre- and post-split.

Novel platform for ocean survey and autonomous sampling using multi-agent system

Abstract: In-situ surveying and sampling of ocean environments provides critical data for laboratory work and oceanographic research. However, sampling a time-varying ocean field is often time and resource limited-meaning that samples often miss the features of interest. This paper presents a modular autonomous multi-agent robotic system which has been developed to accommodate a variety of research activities. This paper demonstrates the complementary capabilities of the agents by simultaneously surveying a time-varying coastal environment and using that information to obtain a sparse but representative set of water samples. This autonomous system enables the elimination of redundant resources, thereby reducing the overall cost of at-sea sampling and improving sample quality.

Feasibility of Turing-Style Tests for Autonomous Aerial Vehicle "Intelligence"

Abstract: A new approach is suggested to define and evaluate key metrics as to autonomous aerial vehicle performance. This approach entails the conceptual definition of a "Turing Test" for UAVs. Such a "UAV Turing test" would be conducted by means of mission simulations and/or tailored flight demonstrations of vehicles under the guidance of their autonomous system software. These autonomous vehicle mission simulations and flight demonstrations would also have to be benchmarked against missions "flown" with pilots/human-operators in the loop. In turn, scoring criteria for such testing could be based upon both quantitative mission success metrics (unique to each mission) and by turning to analog "handling quality" metrics similar to the well-known Cooper-Harper pilot ratings used for manned aircraft. Autonomous aerial vehicles would be considered to have successfully passed this "UAV Turing Test" if the aggregate mission success metrics and handling qualities for the autonomous aerial vehicle matched or exceeded the equivalent metrics for missions conducted with pilots/human-operators in the loop. Alternatively, an independent, knowledgeable observer could provide the "UAV Turing Test" ratings of whether a vehicle is autonomous or "piloted." This observer ideally would, in the more sophisticated mission simulations, also have the enhanced capability of being able to override the scripted mission scenario and instigate failure modes and change of flight profile/plans. If a majority of mission tasks are rated as "piloted" by the observer, when in reality the vehicle/simulation is fully- or semi- autonomously controlled, then the vehicle/simulation "passes" the "UAV Turing Test." In this regards, this second "UAV Turing Test" approach is more consistent with Turing s original "imitation game" proposal. The overall feasibility, and important considerations and limitations, of such an approach for judging/evaluating autonomous aerial vehicle "intelligence" will be discussed from a theoretical perspective.

Intelligent autonomous navigation for mobile robots: spatial concept acquisition and object discrimination

Abstract: An autonomous system able to construct its own navigation strategy for mobile robots is proposed. The navigation strategy is molded from navigation experiences (succeeding as the robot navigates) according to a classical reinforcement learning procedure. The autonomous system is based on modular hierarchical neural networks. Initially, the navigation performance is poor (many collisions occur). Computer simulations show that after a period of learning, the autonomous system generates efficient obstacle avoidance and target seeking behaviors. Experiments also offer support for concluding that the autonomous system develops a variety of object discrimination capability and of spatial concepts.