智能搜救机器人在障碍地形的自主构型规划

为了解决带有辅助摆臂的智能搜救机器人自动规划构型以实现自主越障的难题，提出一种能够适应复杂地面形状的搜救机器人越障构型规划新方法，其核心是一种高适应性、高效率的机器人姿态预测算法。通过将地形表示为离散的点集，建立了搜救机器人的单侧姿态预测数学模型；进一步提出了快速求解该问题的算法，每秒可预测1 000～1 500个姿态。基于此，设计了机器人越障过程中状态、动作的评价指标，运用动态规划算法与滚动优化思想构建了具有优化能力的、能够实时运行的构型规划器。仿真与实物实验的结果表明，该方法能够使机器人自主调整构型穿越复杂地形，且相较强化学习算法和人工操作具有更平稳的越障效果。

Autonomous configuration planning for intelligent search and rescue robots in rough terrains

Neural architecture search is a task that aims to automatically search for the optimal neural network structure for different tasks, which is of great importance and inevitability in the joint development of deep learning and computer vision to the current stage. A comprehensive review of the research on neural network search was provided. In specific, the definition and significance of neural architecture search were introduced, and the difficulties and challenges faced in relevant research were deeply analyzed. Based on this, the mainstream search strategies was elaborate and summarize; Finally, the potential problems and possible future research directions were summarized and discussed to promote further development in this field.