A Novel Integrated Path Planning and Mode Decision Algorithm for Wheel–Leg Vehicles in Unstructured Environment DOI Creative Commons
Kui Wang, Xitao Wu, Shuming Shi

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(9), P. 2888 - 2888

Published: May 3, 2025

Human exploration and rescue in unstructured environments including hill terrain depression are fraught with danger difficulty, making autonomous vehicles a promising alternative these areas. In flat terrain, traditional wheeled demonstrate excellent maneuverability; however, their passability is limited terrains due to the constraints of chassis drivetrain. Considering high efficiency, wheel–leg have garnered increasing attention recent years. automation process vehicles, planning mode decisions crucial components. However, current path decision algorithms mostly designed for cannot determine when adopt which mode, thus limiting full exploitation multimodal advantages vehicles. To address this issue, paper proposes an integrated algorithm (IPP-MD) environments, modeling problem using Markov Decision Process (MDP). The state space, action reward function innovatively dynamically most suitable progression, fully utilizing potential movement. simulation results show that proposed method demonstrates significant terms fewer mode-switching occurrences compared existing methods.

Language: Английский

A Novel Integrated Path Planning and Mode Decision Algorithm for Wheel–Leg Vehicles in Unstructured Environment DOI Creative Commons
Kui Wang, Xitao Wu, Shuming Shi

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(9), P. 2888 - 2888

Published: May 3, 2025

Human exploration and rescue in unstructured environments including hill terrain depression are fraught with danger difficulty, making autonomous vehicles a promising alternative these areas. In flat terrain, traditional wheeled demonstrate excellent maneuverability; however, their passability is limited terrains due to the constraints of chassis drivetrain. Considering high efficiency, wheel–leg have garnered increasing attention recent years. automation process vehicles, planning mode decisions crucial components. However, current path decision algorithms mostly designed for cannot determine when adopt which mode, thus limiting full exploitation multimodal advantages vehicles. To address this issue, paper proposes an integrated algorithm (IPP-MD) environments, modeling problem using Markov Decision Process (MDP). The state space, action reward function innovatively dynamically most suitable progression, fully utilizing potential movement. simulation results show that proposed method demonstrates significant terms fewer mode-switching occurrences compared existing methods.

Language: Английский

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