Robotic manipulators are essential for future autonomous systems, yet limited trust in their autonomy has confined them to rigid, task-specific systems. The intricate configuration space of manipulators, coupled with the challenges of obstacle avoidance and constraint satisfaction, often makes motion planning the bottleneck for achieving reliable and adaptable autonomy. Recently, a class of constant-time motion planners (CTMP) was introduced. These planners employ a preprocessing phase to compute data structures that enable online planning provably guarantee the ability to generate motion plans, potentially sub-optimal, within a user defined time bound. This framework has been demonstrated to be effective in a number of time-critical tasks. However, robotic systems often have more time allotted for planning than the online portion of CTMP requires, time that can be used to improve the solution. To this end, we propose an anytime refinement approach that works in combination with CTMP algorithms. Our proposed framework, as it operates as a constant time algorithm, rapidly generates an initial solution within a user-defined time threshold. Furthermore, functioning as an anytime algorithm, it iteratively refines the solution's quality within the allocated time budget. This enables our approach to strike a balance between guaranteed fast plan generation and the pursuit of optimization over time. We support our approach by elucidating its analytical properties, showing the convergence of the anytime component towards optimal solutions. Additionally, we provide empirical validation through simulation and real-world demonstrations on a 6 degree-of-freedom robot manipulator, applied to an assembly domain.

翻译：机器人机械手是未来自主系统的关键，但其自主性的信任不足使其局限于僵化的、特定任务系统。机械手复杂的构型空间、避障与约束满足的挑战，常使运动规划成为实现可靠且适应性自主性的瓶颈。近年来，一类恒定时间运动规划器（CTMP）被提出。这类规划器通过预处理阶段计算数据结构，保证在线规划能在用户定义的时间范围内生成运动规划（可能为次优）。该框架已在多个时间关键任务中展现有效性。然而，机器人系统通常有比CTMP在线部分所需更多的规划时间，这些时间可用于优化解。为此，我们提出一种与CTMP算法协同工作的任意时刻精化方法。本框架作为恒定时间算法运行时，在用户定义的时间阈值内快速生成初始解；同时作为任意时刻算法，其在分配的时间预算内迭代精化解的质量。这使得我们的方法能在保证快速规划生成与随时间追求优化之间取得平衡。我们通过阐明其解析性质（证明任意时刻分量收敛到最优解）来支持该方法，并通过仿真和在六自由度机械臂装配域的实际演示提供经验验证。