Modeling Human Steering Behavior in Teleoperation of Unmanned Ground Vehicles With Varying Speed

Objective This paper extends a prior human operator model to capture human steering performance in the teleoperation of unmanned ground vehicles (UGVs) in path-following scenarios with varying speed. Background A prior study presented a human operator model to predict human steering performance in the teleoperation of a passenger-sized UGV at constant speeds. To enable applications to varying speed scenarios, the model needs to be extended to incorporate speed control and be able to predict human performance under the effect of accelerations/decelerations and various time delays induced by the teleoperation setting. A strategy is also needed to parameterize the model without human subject data for a truly predictive capability. Method This paper adopts the ACT-R cognitive architecture and two-point steering model used in the previous work, and extends the model by incorporating a far-point speed control model to allow for varying speed. A parameterization strategy is proposed to find a robust set of parameters for each time delay to maximize steering performance. Human subject experiments are conducted to validate the model. Results Results show that the parameterized model can predict both the trend of average lane keeping error and its lowest value for human subjects under different time delays. Conclusions The proposed model successfully extends the prior computational model to predict human steering behavior in a teleoperated UGV with varying speed. Application This computational model can be used to substitute for human operators in the process of development and testing of teleoperated UGV technologies and allows fully simulation-based development and studies.

REGIONAL METHOD FOR DETERMINATION THE MAXIMUM RUNOFF OF THE SPRING FLOOD IN SUB-BASIN THE DESNA RIVER UNDER THE CLIMATE CHANGE

The results of development of the regional methodology for calculating the maximum water runoff of the rare probability of exceedance for the rivers of the sub-basin the Desna River under the conditions of modern climate change are presented. As basic for calculation authors used a modern modified version of the operator model of runoff formation developed at the Odessa State Environmental University to determine the characteristics of spring flood, which allows taking into account the influence of climate change on the calculated characteristics of the maximal runoff modules. The advantage of the proposed method is that it is based on the theory of channel isochrones, which allows describing the natural process of formation of runoff in the form of the operator “slope tide – channel runoff”. To substantiate the basic calculation parameters of the author’s methodology, was used the data of direct observations on the hydrological characteristics of the maximum waterrunoff of the spring flood (water discharges, depth of runoff and duration of the influx) and meteorological factors of its formation (maximum snow supply and precipitation during spring flood) for the period since its beginning to 2015, including. In the process of standardization of the main components of the proposed methodology, methods of statistical processing, spatial generalization, numerical problem solving and mathematical modeling were used. To account for possible climate change, the original author’s scientific and methodological approach is proposed, which is to determine “climate corrections” on the basis of modern baseline data – maximum of the water snow supply and precipitation during spring flood and runoff coefficients of the water, taking into account their dependence from long-term annual air temperatures that are projected according to the developed climate models and scenarios. The modified version of the operator model is proposed to be used as a regional calculation technique for determining maximum runoff modules of the rare probability of exceeding for ungauged rivers in the Desna sub-basin during the passage of the spring flood.

Cognition of a Robotic Manipulator Using the Q-Learning Based Situation-Operator Model

In this article, we have applied cognition on robot using Q-learning based situation operator model. The situation operator model takes the initial situation of the mobile robot and applies a set of operators in order to move the robot to the destination. The initial situation of the mobile robot is defined by a set of characteristics inferred by the sensor inputs. The Situation-Operator Model (SOM) model comprises of a planning and learning module which uses certain heuristics for learning through the mobile robot and a knowledge base which stored the experiences of the mobile robot. The control and learning of the robot is done using q-learning. A camera sensor and an ultrasonic sensor were used as the sensory inputs for the mobile robot. These sensory inputs are used to define the initial situation, which is then used in the learning module to apply the valid operator. The results obtained by the proposed method were compared to the result obtained by Reinforcement-Based Artificial Neural Network for path planning.

Application of the operator model of runoff formation to determine the average long-term values of maximum water discharge of spring flood in the Pivdenny Buh basin

In the context of Ukraine's integration into the European Union and implementation of the main provisions of Directive 2007/60/EC which implies assessment of potential hydrological risks, long-term factors of their formation, in particular the effects of climate change and the trend of river water regime changes should be taken into consideration. With this in mind, given the presence of both current long-term tendencies to reduction of runoff layers (volumes) and maximum discharge of water of spring flood across the Ukrainian rivers there is an important task to identify, using the modern initial data, both the average long-term values of these characteristics and different probability of their exceedance probability. For the first time, the authors of the study implemented an operator model of runoff formation to determine the average long-term values of maximum water discharge of spring flood in the basin of the Pivdenny Buh using meteorological characteristics (snowpack and precipitation) and runoff coefficients as basic parameters. The model was applied to determine the maximum runoff modules of spring floods for the rivers with a wide range of catchment areas affected by different physical and geographical conditions within the Pivdenny Buh Basin. Application of the operator model allowed the authors of the article to calculate and summarize all input parameters of the calculation model, including those obtained from observational data (snowpack, precipitation) and those that can't be measured by the hydrometeorological network (runoff coefficient, temporal irregularity coefficient and duration of surface inflow of snowmelt and rain water, transformational function of the flood waves layering under the influence of channel lag, coefficient of channel and floodplain regulation) for the rivers of the Pivdenny Buh Basin. The verifying calculation related to determination of the average long-term values of the maximum modules of spring flood runoff using the operator model showed satisfactory concordance with the initial data and this allowed recommending it for practical application for the rivers of the Pivdenny Buh Basin, including those that haven't been studied from the hydrological perspective.