[Wang&Botea ICAPS-08, Surynek ICRA-09, Luna&Bekris IJCAI-11, de Wilde+ AAMAS-13, Okumura+ IJCAI-19, etc] Optimization is intractable in various criteria [Surynek AAAI-10, Yu&LaValle AAAI-13, Yu RA-L-15, Banfi+ RA-L-17, Ma+ AAAI-16] Even with SOTA optimal solvers, still challenging to find good solutions for ≥100 robots within acceptable time [Sharon+ AIJ-15, Lam+ IJCAI-19, etc] Practical scenarios need real-time planning work with ≥1000 robots Iterative refinement of known solutions is promising it is also anytime planning
non-trivial effectively using existing solvers our approach Get initial solutions by sub-optimal solvers 1. - Pickup a subset of robots - Use optimal solvers to refine their paths Repeat: 2. agent goal
solvers 1. - Pickup a subset of robots - Use optimal solvers to refine their paths Repeat: 2. agent goal wait how to improve known solutions non-trivial effectively using existing solvers our approach
solvers 1. - Pickup a subset of robots - Use optimal solvers to refine their paths Repeat: 2. agent goal wait how to improve known solutions non-trivial effectively using existing solvers our approach
solvers 1. - Pickup a subset of robots - Use optimal solvers to refine their paths Repeat: 2. agent goal how to improve known solutions non-trivial effectively using existing solvers our approach *each refinement is completed quickly because sub-problems are much smaller
k≥6 length: k≥6 initial: 1 + k Corollary 2 Depending on initial solutions, it may be impossible to reach optimal solutions unless selecting all robots as a modification set. Proof sketch. i.e., solving original problem modif.: modif.:
plan -2 cost effective for inefficient solutions intuition: extracting a set of robots that prevent earlier arrival of one robot modification set = { robots who use ’s goal at timestep t, ideal cost of ≤ t < real cost of }
solutions t=0 t=1 t=2 t=3 MDD for by t=2 stay turn out to be invalid MDD Multi-valued Decision Diagram [Srinivasan+ ICCAD-90] intuition: extracting a set of robots that prevent earlier arrival of one robot MDD for by t=3 If ‘s MDD is updated by ‘s path => two robots are interacting modification set = { robots that update MDD for by timestep t, ideal cost of ≤ t < real cost of }
solutions using-MDD effective for inefficient solutions focus-at-goals e.g., focusing-at-goals => using-MDD => random composite these rules switching: when no improvement is achieved for all robots *for other rules, check the paper
[Li+ IJCAI-21] framework of iterative refinement our approach target real-time path planning for multiple robots design good selection rules with theoretical guarantee future directions using optimal solvers to refine solutions further analysis of local minimum