Motion planning with sequential convex optimization and convex collision checking
Motion planning with sequential convex optimization and convex collision checking
Section titled “Motion planning with sequential convex optimization and convex collision checking”学习档位 精读
类型 文献 · 更新 2026-07-20
所属 轨迹优化与模型预测控制 · 序列决策学习
Discovery evidence
Section titled “Discovery evidence”- topic:
benchmark-eval-safety - sources:
openalex - retrieved_at: 2026-07-20
- query: autonomous driving safety evaluation benchmark
- doi:
10.1177/0278364914528132 - score_total: 43
- suggested_tier:
foundational
Relevance
Section titled “Relevance”(no prose relevance explanation — numeric score only or HTTP source)
Snippets
Section titled “Snippets”(no snippet evidence in candidate pool)
AI analysis (heuristic fallback, needs-source-verification)
Section titled “AI analysis (heuristic fallback, needs-source-verification)”Status: metadata_only · Source: metadata
One-line takeaway
Section titled “One-line takeaway”Metadata-only card for Motion planning with sequential convex optimization and convex collision checking.
Problem & motivation
Section titled “Problem & motivation”—
Inputs & outputs
Section titled “Inputs & outputs”—
Core representation
Section titled “Core representation”—
Method pipeline & key modules
Section titled “Method pipeline & key modules”—
Training objectives
Section titled “Training objectives”—
Datasets & metrics
Section titled “Datasets & metrics”—
Main results
Section titled “Main results”—
Ablation findings
Section titled “Ablation findings”—
Limitations & failure modes
Section titled “Limitations & failure modes”—
Related work positioning
Section titled “Related work positioning”—
Open-source / code anchors
Section titled “Open-source / code anchors”—
Reproduction risks
Section titled “Reproduction risks”—
Practical value
Section titled “Practical value”—
Evidence
Section titled “Evidence”—
方法结构（重绘）
Section titled “方法结构（重绘）”flowchart LR A["输入 / 观测"] --> B["表示 / 编码"] B --> C["推理 / 解码"] C --> D["输出 / 动作或检测"] %% method sketch for: Motion planning with sequential convex optimization and convex collision checkin方法结构示意（重绘；细节以原论文为准，待 PDF 核验）。
精读判断（人工）
Section titled “精读判断（人工）”这篇文献回答什么问题
Section titled “这篇文献回答什么问题”如何把带碰撞与动力学约束的运动规划写成**序列凸优化（SCP）**可迭代求解的形式。
- 非凸问题可通过局部凸近似迭代逼近；
- 碰撞可用凸安全走廊 / 约束线性化表达。
这是「规划 = 优化」路线的工程入门，与端到端学习规划对照读：学习方法常隐式满足约束，SCP/MPC 则显式编码约束——算法专家必须两边都会讲。
- 初始化差导致不可行或振荡；
- 凸近似过松/过紧 → 保守或撞约束；
- 实时性受迭代次数与约束规模限制。
读完应能回答
Section titled “读完应能回答”- 为何轨迹规划常常非凸？
- SCP 一轮迭代做了什么？
- 与 MPC 滚动时域的关系？
英文自动分析（可折叠）
Section titled “英文自动分析（可折叠）”展开英文 Paper Card / AI deep analysis
Paper Card
Section titled “Paper Card”| Field | Content |
|---|---|
| Year | 2014 |
| Authors | John Schulman, Yan Duan, Jonathan Ho, Alex Pui‐Wai Lee, Ibrahim Awwal, Henry Bradlow, Jia Pan, Sachin Patil, Ken Goldberg, Pieter Abbeel |
| arXiv | — |
| DOI | 10.1177/0278364914528132 |
| Topics | math-optimization, sequential-decision |
原文摘录与素材（可折叠）
Section titled “原文摘录与素材（可折叠）”展开 Extract / Selections / Local assets
Selections
Section titled “Selections”math-optimization: tier=watch rank=3 score=53 — auto refresh 2026-07-19 sources=openalexsequential-decision: tier=watch rank=4 score=57 — cross-topic assign from registry title match=2 keywords; 2026-07-19
Extract excerpt
Section titled “Extract excerpt”(no PDF text available; metadata-only card)