提出智能决策机制，让机器人在合适时机定位以降低风险与成本
When to Localize? A POMDP Approach
- 将定位时机问题建模为带约束的部分可观马尔可夫决策过程
- 通过模拟失败概率，动态决定是否定位或继续前行
- 适用于资源受限或定位代价高的场景，如水下航行器
机器人常通过定位来降低导航误差并支持高层任务，但在资源受限（如小型机器人）或定位低效（如需上浮的潜水器）环境下，应选择性地进行定位。尤其在障碍物和航道数量不一的环境中，盲目定位会增加能耗或风险。本文提出一种方法，帮助机器人判断何时定位，以同时实现两个目标：1）最小化定位次数；2）确保失败概率（如在高密度航运区上浮）不超过阈值。该方法被建模为带有约束的部分可观马尔可夫决策过程（Constrained POMDP），并采用成本约束的POMCP求解器进行规划。求解器通过模拟失败概率，评估移动或定位的策略优劣。我们在多个基线方法上进行了数值实验，验证了该方法在减少定位频率的同时有效控制风险的能力。
原文摘要 · Abstract (English)
Robots often localize to lower navigational errors and facilitate downstream, high-level tasks. However, a robot may want to selectively localize when localization is costly (such as with resource-constrained robots) or inefficient (for example, submersibles that need to surface), especially when navigating in environments with variable numbers of hazards such as obstacles and shipping lanes. In this study, we propose a method that helps a robot determine ``when to localize'' to 1) minimize such actions and 2) not exceed the probability of failure (such as surfacing within high-traffic shipping lanes). We formulate our method as a Constrained Partially Observable Markov Decision Process and use the Cost-Constrained POMCP solver to plan the robot's actions. The solver simulates failure probabilities to decide if a robot moves to its goal or localizes to prevent failure. We performed numerical experiments with multiple baselines.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。