A typical unmanned ground vehicle (UGV) mission can be composed of various tasks and several alternative paths. Small UGVs are typically teleoperated and rely on electric rechargeable batteries for their operations. Since each battery has limited energy storage capacity, it is essential to predict the expected mission energy requirement during the mission execution and update this prediction adaptively via real‐time performance measurements (e.g., vehicle power consumption and velocity). We propose and compare two methods in this paper. One is based on recursive least‐squares estimation built upon a UGV longitudinal dynamics model. The other is based on Bayesian estimation when prior knowledge (e.g., road average grade and operator driving style) is available. The proposed Bayesian prediction can effectively combine prior knowledge with real‐time performance measurements for adaptively updating the prediction of the mission energy requirement. Our experimental and simulation studies show that the Bayesian approach can yield more accurate predictions even with moderately imprecise prior knowledge. © 2013 Wiley Periodicals, Inc.

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A typical unmanned ground vehicle (UGV) mission can be composed of various tasks and several alternative paths. Small UGVs commonly rely on electric rechargeable batteries for their operations. Since each battery has limited energy storage capacity, it is e ssential to predict the expected mission energy requirement during the mission execution and update this prediction adaptively via real-time performance measurements, e.g., the total battery power required for the mission. We proposed and compared two methods in the paper. One is a linear regression model built upon the UGV longitudinal dynamics model alone. The other is a Bayesian regression model when prior knowledge, e.g., road average grade and operator driving style, is available . In this case, the proposed Bayesian prediction can effectively combine the prior knowledge with real-time performance measurements for adaptively updating the prediction of the mission energy requirement. Our comparative simulation studies show that the Bayesian model can yield more accurate predictions than the linear regression model, particularly during the initial execution stage of a mission.

Surveillance missions that involve unmanned ground vehicles (UGVs) include situations where a UGV has to choose between alternative paths to complete its mission. Currently, UGV missions are often limited by the available on-board energy. Thus, we propose a dynamic most energy-efficient path planning algorithm that integrates mission prior knowledge with real-time sensory information to identify the mission’s most energy-efficient path. Our proposed approach predicts and updates the distribution of energy requirement of alternative paths using recursive Bayesian estimation through two stages: (1) exploration — road segments are explored to reduce their energy prediction uncertainty; (2) exploitation — the most energy-efficient path is selected using the collected information in the exploration stage and is traversed. Our simulation results show that the proposed approach outperforms offline methods, as well as a method that only relies on exploitation to identify the most energy-efficient path.

Unmanned Ground Vehicle (UGV) missions include situations where a UGV has to choose between alternative paths, and are often limited by the available on-board energy. Thus, we propose a dynamic energy-efficient path planning algorithm that integrates mission prior knowledge with real-time sensory information to identify the most energy-efficient path for mission completion. Our proposed approach predicts and updates the distribution of the energy requirement for alternative paths using recursive Bayesian estimation through two stages: (a) exploration-road segments can be explored to reduce their energy prediction uncertainty; (b) exploitation-the most reliable path is selected using the collected information in the exploration stage and then traversed. Our simulation results show that the proposed approach outperforms offline methods, as well as a method that relies on exploitation only to identify the most energy-efficient path.