| 引用本文: | 陈柏良,黄开宏,潘海南,等.智能搜救机器人在障碍地形的自主构型规划.[J].国防科技大学学报,2023,45(6):132-142.[点击复制] |
| CHEN Bailiang,HUANG Kaihong,PAN Hainan,et al.Autonomous configuration planning for intelligent search and rescue robots in rough terrains[J].Journal of National University of Defense Technology,2023,45(6):132-142[点击复制] |
|
| |
|
|
| 本文已被：浏览 3582次 下载 2922次 |
| 智能搜救机器人在障碍地形的自主构型规划 |
| 陈柏良，黄开宏，潘海南，肖军浩，吴文启，卢惠民 |
|
(国防科技大学 智能科学学院, 湖南 长沙 410073)
|
| 摘要: |
| 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1000~1500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。 |
| 关键词: 搜救机器人 障碍地形 姿态预测 动作规划 |
| DOI：10.11887/j.cn.202306015 |
| 投稿日期：2023-02-28 |
| 基金项目:国家自然科学基金资助项目（U1913202，U22A2059，62203460） ； 湖南省自然科学基金资助项目（2021JC0004） |
|
| Autonomous configuration planning for intelligent search and rescue robots in rough terrains |
| CHEN Bailiang, HUANG Kaihong, PAN Hainan, XIAO Junhao, WU Wenqi, LU Huimin |
|
(College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073 , China)
|
| Abstract: |
| In order to solve the configuration planning problem for intelligent search and rescue robots with assisted flippers to achieve autonomous obstacle crossing, a novel method for planning robot configuration during obstacle crossing was proposed that can be applied to complex terrains. The core of the proposed method is an adaptable and efficient robot pose prediction algorithm. By representing the terrain as a series of discrete point sets, a mathematical model for predicting the one-sided pose of the tracked robot was established; further, a fast solver for this model was proposed, which can predict 1000~1500 poses per second. Based on this, the evaluation metrics of the robot′s state and action in the obstacle-crossing process were established, and an optimization-based real-time flippers action planner was realized by using the dynamic programming algorithm and rolling optimization. The simulation and real-robot experiments show that the proposed approach enables the robot to control the flippers to cross rough terrains autonomously. It performs more smoothly than the reinforcement-learning method and manual operation when crossing obstacles. |
| Keywords: search and rescue robots rough terrains pose prediction motion planning |
|
|
