Lifelong Multi-Agent Path Finding in Large-Scale Warehouses

Multi-Agent Path Finding (MAPF) is the problem of moving a team of agents to their goal locations without collisions. In this paper, we study the lifelong variant of MAPF, where agents are constantly engaged with new goal locations, such as in large-scale automated warehouses. We propose a new framework Rolling-Horizon Collision Resolution (RHCR) for solving lifelong MAPF by decomposing the problem into a sequence of Windowed MAPF instances, where a Windowed MAPF solver resolves collisions among the paths of the agents only within a bounded time horizon and ignores collisions beyond it. RHCR is particularly well suited to generating pliable plans that adapt to continually arriving new goal locations. We empirically evaluate RHCR with a variety of MAPF solvers and show that it can produce high-quality solutions for up to 1,000 agents (= 38.9\% of the empty cells on the map) for simulated warehouse instances, significantly outperforming existing work.Comment: Published at AAAI 202

Revisiting Bounded-Suboptimal Safe Interval Path Planning

Safe-interval path planning (SIPP) is a powerful algorithm for finding a path in the presence of dynamic obstacles. SIPP returns provably optimal solutions. However, in many practical applications of SIPP such as path planning for robots, one would like to trade-off optimality for shorter planning time. In this paper we explore different ways to build a bounded-suboptimal SIPP and discuss their pros and cons. We compare the different bounded-suboptimal versions of SIPP experimentally. While there is no universal winner, the results provide insights into when each method should be used

A Conflict-Based Search Framework for Multi-Objective Multi-Agent Path Finding

Conventional multi-agent path planners typically compute an ensemble of paths while optimizing a single objective, such as path length. However, many applications may require multiple objectives, say fuel consumption and completion time, to be simultaneously optimized during planning and these criteria may not be readily compared and sometimes lie in competition with each other. Naively applying existing multi-objective search algorithms, such as multi-objective A* (MOA*), to multi-agent path finding may prove to be inefficient as the size of the space of possible solutions, i.e., the Pareto-optimal set, can grow exponentially with the number of agents (the dimension of the search space). This article presents an approach named Multi-Objective Conflict-Based Search (MO-CBS) that bypasses this so-called curse of dimensionality by leveraging prior Conflict-Based Search (CBS), a well-known algorithm for single-objective multi-agent path finding, and principles of dominance from multi-objective optimization literature. We also develop several variants of MO-CBS to further improve its performance. We prove that MO-CBS and its variants are able to compute the entire Pareto-optimal set. Numerical results show that MO-CBS outperforms both MOA* as well as MOM*, a recently developed state-of-the-art multi-objective multi-agent planner.Comment: 11 pages, preliminary version published in ICRA 2021, journal version submitte

Hypergraph-based Multi-Robot Task and Motion Planning

We present a multi-robot task and motion planning method that, when applied to the rearrangement of objects by manipulators, produces solution times up to three orders of magnitude faster than existing methods. We achieve this improvement by decomposing the planning space into subspaces for independent manipulators, objects, and manipulators holding objects. We represent this decomposition with a hypergraph where vertices are substates and hyperarcs are transitions between substates. Existing methods use graph-based representations where vertices are full states and edges are transitions between states. Using the hypergraph reduces the size of the planning space-for multi-manipulator object rearrangement, the number of hypergraph vertices scales linearly with the number of either robots or objects, while the number of hyperarcs scales quadratically with the number of robots and linearly with the number of objects. In contrast, the number of vertices and edges in graph-based representations scale exponentially in the number of robots and objects. Additionally, the hypergraph provides a structure to reason over varying levels of (de)coupled spaces and transitions between them enabling a hybrid search of the planning space. We show that similar gains can be achieved for other multi-robot task and motion planning problems.Comment: This work has been submitted for revie

Interleaving Allocation, Planning, and Scheduling for Heterogeneous Multi-Robot Coordination through Shared Constraints

In a wide variety of domains, such as warehouse automation, agriculture, defense, and assembly, effective coordination of heterogeneous multi-robot teams is needed to solve complex problems. Effective coordination is predicated on the ability to solve the four fundamentally intertwined questions of coordination: what (task planning), who (task allocation), when (scheduling), and how (motion planning). Owing to the complexity of these four questions and their interactions, existing approaches to multi-robot coordination have resorted to defining and solving problems that focus on a subset of the four questions. Notable examples include Task and Motion Planning (what and how), Multi-Agent Planning (what and who), and Multi-Agent Path Finding (who and how). In fact, a holistic problem formulation that fully integrates the four questions lies beyond the scope of prior literature. This dissertation focuses on examining the use of shared constraints on tasks and robots to interleave algorithms for task planning, task allocation, scheduling, and motion planning and investigating the hypothesis that a framework that interleaves algorithms to these four sub-problems will lead to solutions with lower makespans, greater computational efficiency, and the ability to solve larger problems. To support this claim, this dissertation contributes: (i) a novel temporal planner that interleaves task planning and scheduling layers, (ii) a trait-based time-extended task allocation framework that interleaves task allocation, scheduling, and motion planning, (iii) the formulation of holistic heterogeneous multi-robot coordination problem that simultaneously considers all four questions, (iv) a framework that interleaves layers for all four questions to solve this holistic heterogeneous multi-robot coordination problem, (v) a scheduling algorithm that reasons about temporal uncertainty, provides a theoretical guarantee on risk, and can be utilized within our framework, and (vi) a learning-based scheduling algorithm that reasons about deadlines and can be utilized within our framework.Ph.D