风险敏感的马尔科夫决策过程的USV轨迹规划与时间有限的预算
1Maritime College, Guangdong Ocean University, Zhanjiang 524091, China.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
本研究介绍了马尔科夫决策过程启发式算法 (MHA),用于有效避免无人驾驶地面车辆 (USV) 的碰撞. 通过平衡时间成本和碰撞风险,MHA优化了船舶航行.
科学领域:
- 海上航行和机器人技术
- 人工智能和机器学习
背景情况:
- 安全的船舶航行依赖于复杂的轨迹规划.
- 无人驾驶地面车辆 (USV) 需要强大的避免碰撞策略.
研究的目的:
- 为无人潜艇开发一个时间优化的避免碰撞算法.
- 通过风险敏感的马尔科夫决策过程模型来增强轨迹规划.
主要方法:
- 介绍了马尔科夫决策过程启发式算法 (MHA).
- 利用风险敏感的马尔科夫决策过程模型来生成状态.
- 整合了用于路径规划的增强奖励和时间依赖的成本功能.
主要成果:
- 在时间限制下,MHA算法有效地计划了实际的运动路径.
- 决策者可以评估预算和目标实现概率之间的权衡.
- 局部随机优化有助于选择最小风险增加的避免碰撞路径.
结论:
- MHA提供了一种有效的解决方案,以优化时间来避免USV碰撞.
- 该算法支持有关风险和资源分配的知情决策.
- 这种方法提高了自主航海的安全性和效率.
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