久保田 孝

<title>Abstract</title>In this paper, a novel terrain traversability prediction method is proposed for new operation environments. When an off-road vehicle is operated on rough terrains or slopes made up of unconsolidated materials, it is crucial to accurately predict terrain traversability to ensure efficient operations and avoid critical mobility risks. However, the prediction of traversability in new environments is challenging, especially for possibly risky terrains, because the traverse data available for such terrains is either limited or non-existent. To address this limitation, this study proposes an adaptive terrain traversability prediction method based on multi-source transfer Gaussian process regression. The proposed method utilizes the limited data available on low-risk terrains of the target environment to enhance the prediction accuracy on untraversed, possibly higher-risk terrains by leveraging past traverse experiences on multiple types of terrain surface. The effectiveness of the proposed method is demonstrated in scenarios where vehicle slippage and power consumption are predicted using a dataset of various terrain surfaces and geometries. In addition to predicting terrain traversability as continuous values, the utility of the proposed method is demonstrated in binary risk level classification of yet to be traversed steep terrains from limited data on safer terrains.

<title>Abstract</title>Hopping robots, called hoppers, are expected to move on rough terrains, such as disaster areas or planetary environments. The uncertainties of the hopping locomotion in such environments are high, making path planning algorithms essential to traverse these uncertain environments. Planetary surface exploration requires to generate a path which minimises the risk of failure and maximises the information around the hopper. This paper newly proposes a hopping path planning algorithm for rough terrains locomotion. The proposed algorithm takes into account the motion uncertainties using Markov decision processes (MDPs), and generates paths corresponding to the terrain conditions, or the mission requirements, or both. The simulation results show the effectiveness of the proposed route planning scheme in three cases as the rough terrain, sandy and hard ground environment, and non-smooth borders.