An exciting frontier in robotic manipulation is the use of multiple arms at
once. However, planning concurrent motions is a challenging task using current
methods. The high-dimensional composite state space renders many well-known
motion planning algorithms intractable. Recently, Multi-Agent Path-Finding
(MAPF) algorithms have shown promise in discrete 2D domains, providing rigorous
guarantees. However, widely used conflict-based methods in MAPF assume an
efficient single-agent motion planner. This poses challenges in adapting them
to manipulation cases where this assumption does not hold, due to the high
dimensionality of configuration spaces and the computational bottlenecks
associated with collision checking. To this end, we propose an approach for
accelerating conflict-based search algorithms by leveraging their repetitive
and incremental nature -- making them tractable for use in complex scenarios
involving multi-arm coordination in obstacle-laden environments. We show that
our method preserves completeness and bounded sub-optimality guarantees, and
demonstrate its practical efficacy through a set of experiments with up to 10
robotic arms.

通过利用冲突基础搜索算法的重复和增量特性，加速搜索算法的方法使其适用于多臂协调和复杂环境中的机器人操作，从而达到完整和有界的次优性保证。

通过利用在线生成的经验，加速多机器人操作的搜索规划

Accelerating Search-Based Planning for Multi-Robot Manipulation by  Leveraging Online-Generated Experiences

We study the TAPF (combined target-assignment and path-finding) problem for
teams of agents in known terrain, which generalizes both the anonymous and
non-anonymous multi-agent path-finding problems. Each of the teams is given the
same number of targets as there are agents in the team. Each agent has to move
to exactly one target given to its team such that all targets are visited. The
TAPF problem is to first assign agents to targets and then plan collision-free
paths for the agents to their targets in a way such that the makespan is
minimized. We present the CBM (Conflict-Based Min-Cost-Flow) algorithm, a
hierarchical algorithm that solves TAPF instances optimally by combining ideas
from anonymous and non-anonymous multi-agent path-finding algorithms. On the
low level, CBM uses a min-cost max-flow algorithm on a time-expanded network to
assign all agents in a single team to targets and plan their paths. On the high
level, CBM uses conflict-based search to resolve collisions among agents in
different teams. Theoretically, we prove that CBM is correct, complete and
optimal. Experimentally, we show the scalability of CBM to TAPF instances with
dozens of teams and hundreds of agents and adapt it to a simulated warehouse
system.

研究了已知地形中团队智能体的 TAPF（组合目标分配和路径查找）问题，提出了 CBM（基于冲突的最小成本流）算法，结合匿名和非匿名多智能体路径查找算法的思想，可用于处理各种规模的 TAPF 实例，并可适用于模拟仓库系统。