---
格式版本: 2
标题: "Risk-Aware Kinodynamic Motion Planning Under Uncertainty For Safe Navigation on Planetary Environments"
原文链接: "https://arxiv.org/abs/2608.11175"
发布日期: "2026-08-11"
发布时间校准状态: "found"
发布时间需复核: "否"
发布时间来源: "rule:local:strict_original_body"
发布时间证据: "**\\[v1\\]** Tue, 11 Aug 2026 17:34:56 UTC (4,007 KB)"
发布时间校准原因: "规则确认唯一严格发布时间，来源 local:strict_original_body"
发布时间校准置信度: "high"
发布时间候选数量: 18
发布时间严格候选数量: 6
发布时间原页读取状态: "source template page reused from URL open"
发布时间未找到原因: ""
发布时间校准时间: "2026-08-12T11:17:46+08:00"
发布时间仲裁状态: "skipped"
发布时间仲裁尝试次数: 0
发布时间仲裁耗时毫秒: 0
发现时间: "2026-08-12T11:16:29+08:00"
入库时间: "2026-08-12T03:17:46.954Z"
来源平台: "arXiv 学术论文搜索"
搜索渠道: "source_template"
搜索词: "https://arxiv.org/search/?query=performance&searchtype=all"
匹配关键词:
  - "performance"
相关厂家:
  []
相关专家:
  []
内容类型: "网页"
抓取工具: "Free Fetch + Defuddle"
清洗工具: "Defuddle Markdown + Defuddle/Readability 正文提取"
原始附件:
  []
AI优质: "否"
AI打分: 0
AI分档: "非优质"
AI质检状态: "不通过"
AI打分理由: "论文内容为行星环境机器人运动规划，与超节点/AI Rack等机柜级AI基础设施完全无关。"
AI质检模型: "ali-deepseek-v4-flash"
AI质检时间: "2026-08-12T11:18:08+08:00"
AI主题相关性: 0
AI来源权威性: 0
AI新颖性: 0
AI技术细节: 0
AI商业部署信号: 0
AI完整性: 0
采集批次: "2026年8月12日2点04分20秒"
采集批次ID: "20260812-020420-289"
去重键: "https://arxiv.org/abs/2608.11175"
---

## Computer Science > Robotics

## Title:Risk-Aware Kinodynamic Motion Planning Under Uncertainty For Safe Navigation on Planetary Environments

[View PDF](https://arxiv.org/pdf/2608.11175) [HTML (experimental)](https://arxiv.org/html/2608.11175v1)

> Abstract:For autonomous space exploration, robotic agents need to perform motion planning in which environmental interactions may be unknown. Learning these interactions, such as terrain mechanics for wheeled robots, can introduce uncertainties that lead to risky motion plans and potentially hazardous operations or mission failures. Moreover, uncertainties induced by perception-based systems can exacerbate the problem of safe motion planning. In this letter, we address the problem of performing cost-optimal kinodynamic motion planning with risk awareness. We approach this in two steps. First, a sampling-based planner (AO-RRT) generates a dynamically feasible, risk-aware, and asymptotically cost-optimal trajectory. Second, we formulate motion planning as a nonlinear optimization problem and solve it using sequential convex programming (SCP), using the AO-RRT trajectory as an initial solution. By quantifying risk using conditional value-at-risk (CVaR), we demonstrate a reduction in risk by over $\sim$ 97\\% across trajectories in simulation and hardware experiments.

| Comments: |  |
| --- | --- |
| Subjects: | Robotics (cs.RO); Systems and Control (eess.SY) |
| Cite as: | [arXiv:2608.11175](https://arxiv.org/abs/2608.11175) \[cs.RO\] |
|  | (or [arXiv:2608.11175v1](https://arxiv.org/abs/2608.11175v1) \[cs.RO\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.11175](https://doi.org/10.48550/arXiv.2608.11175) |

## Submission history

From: Tanmay Dokania \[[view email](https://arxiv.org/show-email/93960d62/2608.11175)\]  
**\[v1\]** Tue, 11 Aug 2026 17:34:56 UTC (4,007 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.11175) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
