通过可行性证书的执行器感知候选选择
Executor-aware Candidate Selection via a Feasibility Certificate
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中文总结 AI 辅助
针对模块化机器人中几何有效候选与执行器硬约束不兼容的问题，提出基于可行性证书的候选选择框架，在不改变生成和排序下验证命令，实验显示其保守性并影响选择与性能。
中文摘要 AI 辅助
模块化机器人系统通常将运动规划与下游执行器分离，执行器会强制执行状态相关的硬约束。因此，几何上有效的候选可能与执行器的可用命令集不兼容。我们提出了一种基于证书的候选选择框架，该框架在预测的滚动状态处从执行器硬约束集构造命令见证，并针对原始约束进行验证，而不改变候选生成、排序或执行器。在两个机器人模型上的5,085次几何有效数值评估中，795次没有可执行器可行的命令。该证书是充分但保守的：795次中没有一次被认证，而参考可行案例中有7.09%仍未认证。在受控的FR3和固定基座RB-Y1仿真中，证书接纳经常改变候选选择，事后精确线性规划（LP）接纳基线揭示了平台相关的保守性。相对于基于几何的选择，证书接纳与较低的计划器命令覆盖率和较高的标称跟踪误差相关，而在达到状态交互储备方面没有一致优势。一项计划器生成的MoveIt/OMPL研究进一步评估了相同接纳规则在外部生成的候选池上的表现。
英文摘要
Modular robotic systems often separate motion planning from a downstream executor that enforces state-dependent hard constraints. A candidate that is geometrically valid may therefore be incompatible with the executor's available command set. We present a certificate-based candidate-selection framework that constructs a command witness from the executor hard set at predicted rollout states and verifies it against the original constraints, without changing candidate generation, ranking, or the executor. Across 5,085 geometry-valid numerical evaluations on two robot models, 795 admitted no executor-feasible command. The certificate is sufficient but conservative: none of the 795 was certified, while 7.09\% of reference-feasible cases remained uncertified. In controlled FR3 and fixed-base RB-Y1 simulations, certificate admission frequently changed candidate selection, and a post-hoc exact linear-programming (LP) admission baseline revealed platform-dependent conservatism. Relative to geometry-based selection, certificate admission was associated with lower planner-command coverage and higher nominal tracking error, without a consistent advantage in reached-state interaction reserve. A planner-generated MoveIt/OMPL study further evaluates the same admission rule on externally generated candidate pools.
发表机构
- Hanyang University（汉阳大学）
- Hanyang University ERICA（汉阳大学ERICA校区）
机构由 AI 辅助整理，请以论文原文为准。
