Author name cluster

Hang Ma 0001

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

26 papers

1 author row

IROS Conference 2025 Conference Paper

Space-Time Graphs of Convex Sets for Multi-Robot Motion Planning

Jingtao Tang
Zining Mao
Lufan Yang
Hang Ma 0001

We address the Multi-Robot Motion Planning (MRMP) problem of computing collision-free trajectories for multiple robots in shared continuous environments. While existing frameworks effectively decompose MRMP into singlerobot subproblems, spatiotemporal motion planning with dynamic obstacles remains challenging, particularly in cluttered or narrow-corridor settings. We propose Space-Time Graphs of Convex Sets (ST-GCS), a novel planner that systematically covers the collision-free space-time domain with convex sets instead of relying on random sampling. By extending Graphs of Convex Sets (GCS) into the time dimension, ST-GCS formulates time-optimal trajectories in a unified convex optimization that naturally accommodates velocity bounds and flexible arrival times. We also propose Exact Convex Decomposition (ECD) to "reserve" trajectories as spatiotemporal obstacles, maintaining a collision-free space-time graph of convex sets for subsequent planning. Integrated into two prioritized-planning frameworks, ST-GCS consistently achieves higher success rates and better solution quality than state-of-the-art sampling-based planners— often at orders-of-magnitude faster runtimes—underscoring its benefits for MRMP in challenging settings. Project page: https://sites.google.com/view/stgcs.

SoCS Conference 2024 Conference Paper

Large-Scale Multi-Robot Coverage Path Planning via Local Search (Extended Abstract)

Jingtao Tang
Hang Ma 0001

We study graph-based Multi-Robot Coverage Path Planning (MCPP) that aims to compute paths for multiple robots to cover all vertices of a given 2D grid terrain graph G. Existing graph-based MCPP algorithms rely on computing a tree cover on G and then employ the Spanning Tree Coverage (STC) paradigm to generate coverage paths on the decomposed graph D of G. In this paper, we take a different approach by exploring how to systematically search for good coverage paths directly on D. We introduce a new algorithmic framework, called LS-MCPP, which leverages a local search to operate directly on D. We propose ESTC, that extends STC to achieve complete coverage for MCPP on any decomposed graphs, even those resulting from incomplete terrain graphs. Furthermore, we demonstrate how to integrate ESTC with three novel types of neighborhood operators into our framework to effectively guide its search process. Remarkably, LS-MCPP scales efficiently to handle MCPP instances with 32 robots on terrain graphs with 11, 892 vertices with just minutes of runtime, thereby showcasing its significant benefits for large-scale real-world coverage tasks.

IROS Conference 2024 Conference Paper

MFC-EQ: Mean-Field Control with Envelope Q-learning for Moving Decentralized Agents in Formation

Qiushi Lin
Hang Ma 0001

We study a decentralized version of Moving Agents in Formation (MAiF), a variant of Multi-Agent Path Finding aiming to plan collision-free paths for multiple agents with the dual objectives of reaching their goals quickly while maintaining a desired formation. The agents must balance these objectives under conditions of partial observation and limited communication. The formation maintenance depends on the joint state of all agents, whose dimensionality increases exponentially with the number of agents, rendering the learning process intractable. Additionally, learning a single policy that can accommodate different linear preferences for these two objectives presents a significant challenge. In this paper, we propose Mean-Field Control with Envelop Q-learning (MFC-EQ), a scalable and adaptable learning framework for this bi-objective multi-agent problem. We approximate the dynamics of all agents using mean-field theory while learning a universal preference-agnostic policy through envelop Q-learning. Our empirical evaluation of MFC-EQ across numerous instances shows that it outperforms state-of-the-art centralized MAiF baselines. Furthermore, MFC-EQ effectively handles more complex scenarios where the desired formation changes dynamically—a challenge that existing MAiF planners cannot address.

SoCS Conference 2024 Conference Paper

Mixed Integer Programming for Time-Optimal Multi-Robot Coverage Path Planning with Efficient Heuristics (Extended Abstract)

Jingtao Tang
Hang Ma 0001

We investigate time-optimal Multi-Robot Coverage Path Planning (MCPP) for both unweighted and weighted terrains, which aims to minimize the coverage time, defined as the maximum travel time of all robots. Specifically, we focus on a reduction from MCPP to Min-Max Rooted Tree Cover (MMRTC). For the first time, we propose a Mixed Integer Programming (MIP) model to optimally solve MMRTC, resulting in an MCPP solution with a coverage time that is provably at most four times the optimal. Moreover, we propose two suboptimal yet effective heuristics that reduce the number of variables in the MIP model, thus improving its efficiency for large-scale MCPP instances. We show that both heuristics result in reduced-size MIP models that remain complete (i. e. , guaranteed to find a solution if one exists) for all MMRTC instances. We validate the effectiveness of our MIP-based MCPP planner through experiments that compare it with two state-of-the-art MCPP planners on various instances, demonstrating a reduction in the coverage time by an average of 27. 65% and 23. 24% over them, respectively.

ICAPS Conference 2024 Conference Paper

Multi-Robot Connected Fermat Spiral Coverage

Jingtao Tang
Hang Ma 0001

We introduce Multi-Robot Connected Fermat Spiral (MCFS), a novel algorithmic framework for Multi-Robot Coverage Path Planning (MCPP) that adapts Coverage Fermat Spiral (CFS) from the computer graphics community to multi-robot coordination for the first time. MCFS uniquely enables the orchestration of multiple robots to generate coverage paths that contour around arbitrarily shaped obstacles, a feature notably lacking in traditional methods. Our framework not only enhances area coverage and optimizes task performance, particularly in terms of makespan, for workspaces rich in irregular obstacles but also addresses the challenges of path continuity and curvature critical for non-holonomic robots by generating smooth paths without decomposing the workspace. MCFS solves MCPP by constructing a graph of isolines and transforming MCPP into a combinatorial optimization problem, aiming to minimize the makespan while covering all vertices. Our contributions include developing a unified CFS version for scalable and adaptable MCPP, extending it to MCPP with novel optimization techniques for cost reduction and path continuity and smoothness, and demonstrating through extensive experiments that MCFS outperforms existing MCPP methods in makespan, path curvature, coverage ratio, and overlapping ratio. Our research marks a significant step in MCPP, showcasing the fusion of computer graphics and automated planning principles to advance the capabilities of multi-robot systems in complex environments. Our code is publicly available at https: //github. com/reso1/MCFS.

ICAPS Conference 2023 Conference Paper

Priority-Based Search for the Virtual Network Embedding Problem

Yi Zheng 0010
Hang Ma 0001
Sven Koenig
Erik Kline
T. K. Satish Kumar

The Virtual Network Embedding (VNE) problem is a constrained optimization problem. It arises in the context of allocating resources on heterogeneous physical networks to provide end-to-end computing services. In this paper, we introduce a new solver, called VNE-PBS, that uses priority-based search (PBS) for solving the VNE problem. VNE-PBS uses a prioritized heuristic search algorithm that explores the space of all possible priority orderings using a systematic depth-first search. The solver is inspired by the success of PBS for the Multi-Agent Path Finding (MAPF) problem and the similarities between the VNE and MAPF problems. We show that VNE-PBS significantly outperforms competing methods on various benchmark instances for both the offline and online versions of the VNE problem.

IROS Conference 2022 Conference Paper

Multi-Goal Multi-Agent Pickup and Delivery

Qinghong Xu
Jiaoyang Li 0001
Sven Koenig
Hang Ma 0001

In this work, we consider the Multi-Agent Pickup-and-Delivery (MAPD) problem, where agents constantly engage with new tasks and need to plan collision-free paths to execute them. To execute a task, an agent needs to visit a pair of goal locations, consisting of a pickup location and a delivery location. We propose two variants of an algorithm that assigns a sequence of tasks to each agent using the anytime algorithm Large Neighborhood Search (LNS) and plans paths using the Multi-Agent Path Finding (MAPF) algorithm Priority-Based Search (PBS). LNS-PBS is complete for well-formed MAPD instances, a realistic subclass of MAPD instances, and empirically more effective than the existing complete MAPD algorithm CENTRAL. LNS-wPBS provides no completeness guarantee but is empirically more efficient and stable than LNS-PBS. It scales to thousands of agents and thousands of tasks in a large warehouse and is empirically more effective than the existing scalable MAPD algorithm HBH+MLA*. LNS-PBS and LNS-wPBS also apply to a more general variant of MAPD, namely the Multi-Goal MAPD (MG-MAPD) problem, where tasks can have different numbers of goal locations.

ICRA Conference 2022 Conference Paper

Optimal and Bounded-Suboptimal Multi-Goal Task Assignment and Path Finding

Xinyi Zhong
Jiaoyang Li 0001
Sven Koenig
Hang Ma 0001

We formalize and study the multi-goal task assignment and path finding (MG-TAPF) problem from theoretical and algorithmic perspectives. The MG-TAPF problem is to compute an assignment of tasks to agents, where each task consists of a sequence of goal locations, and collision-free paths for the agents that visit all goal locations of their assigned tasks in sequence. Theoretically, we prove that the MG-TAPF problem is NP-hard to solve optimally. We present algorithms that build upon algorithmic techniques for the multi-agent path finding problem and solve the MG-TAPF problem optimally and bounded-suboptimally. We experimentally compare these algorithms on a variety of different benchmark domains.

ICAPS Conference 2021 Conference Paper

A Competitive Analysis of Online Multi-Agent Path Finding

Hang Ma 0001

We study online Multi-Agent Path Finding (MAPF), where new agents are constantly revealed over time and all agents must find collision-free paths to their given goal locations. We generalize existing complexity results of (offline) MAPF to online MAPF. We classify online MAPF algorithms into different categories based on (1) controllability (the set of agents that they can plan paths for at each time) and (2) rationality (the quality of paths they plan) and study the relationships between them. We perform a competitive analysis for each category of online MAPF algorithms with respect to commonly-used objective functions. We show that a naive algorithm that routes newly-revealed agents one at a time in sequence achieves a competitive ratio that is asymptotically bounded from both below and above by the number of agents with respect to flowtime and makespan. We then show a counter-intuitive result that, if rerouting of previously-revealed agents is not allowed, any rational online MAPF algorithms, including ones that plan optimal paths for all newly-revealed agents, have the same asymptotic competitive ratio as the naive algorithm, even on 2D 4-neighbor grids. We also derive constant lower bounds on the competitive ratio of any rational online MAPF algorithms that allow rerouting. The results thus provide theoretical insights into the effectiveness of using MAPF algorithms in an online setting for the first time.

ICRA Conference 2021 Conference Paper

Distributed Heuristic Multi-Agent Path Finding with Communication

Ziyuan Ma
Yudong Luo
Hang Ma 0001

Multi-Agent Path Finding (MAPF) is essential to large-scale robotic systems. Recent methods have applied reinforcement learning (RL) to learn decentralized polices in partially observable environments. A fundamental challenge of obtaining collision-free policy is that agents need to learn co-operation to handle congested situations. This paper combines communication with deep Q-learning to provide a novel learning based method for MAPF, where agents achieve cooperation via graph convolution. To guide RL algorithm on long-horizon goal-oriented tasks, we embed the potential choices of shortest paths from single source as heuristic guidance instead of using a specific path as in most existing works. Our method treats each agent independently and trains the model from a single agent’s perspective. The final trained policy is applied to each agent for decentralized execution. The whole system is distributed during training and is trained under a curriculum learning strategy. Empirical evaluation in obstacle-rich environment indicates the high success rate with low average step of our method.

SoCS Conference 2021 Conference Paper

Extended Abstract: A Competitive Analysis of Online Multi-Agent Path Finding

Hang Ma 0001

This is an extended abstract of a paper to be published at ICAPS 2021. We study online Multi-Agent Path Finding (MAPF), where new agents are constantly revealed over time and all agents must find collision-free paths to their given goal locations. We generalize existing complexity results of (offline) MAPF to online MAPF. We classify online MAPF algorithms into different categories. We present several complexity and competitiveness results for online MAPF and its algorithms, which provides theoretical insights into the effectiveness of using MAPF algorithms in an online setting for the first time.

ICAPS Conference 2021 Conference Paper

Scalable Rail Planning and Replanning: Winning the 2020 Flatland Challenge

Jiaoyang Li 0001
Zhe Chen 0016
Yi Zheng 0010
Shao-Hung Chan
Daniel Harabor
Peter J. Stuckey
Hang Ma 0001
Sven Koenig

Multi-Agent Path Finding (MAPF) is the combinatorial problem of finding collision-free paths for multiple agents on a graph. This paper describes MAPF-based software for solving train planning and replanning problems on large-scale rail networks under uncertainty. The software recently won the 2020 Flatland Challenge, a NeurIPS competition trying to determine how to efficiently manage dense traffic on rail networks. The software incorporates many state-of-the-art MAPF or, in general, optimization technologies, such as prioritized planning, large neighborhood search, safe interval path planning, minimum communication policies, parallel computing, and simulated annealing. It can plan collision-free paths for thousands of trains within a few minutes and deliver deadlock-free actions in real-time during execution.

SoCS Conference 2021 Conference Paper

Scalable Rail Planning and Replanning: Winning the 2020 Flatland Challenge

Jiaoyang Li 0001
Zhe Chen 0016
Yi Zheng 0010
Shao-Hung Chan
Daniel Harabor
Peter J. Stuckey
Hang Ma 0001
Sven Koenig

Multi-Agent Path Finding (MAPF) is the combinatorial problem of finding collision-free paths for multiple agents on a graph. This paper describes MAPF-based software for solving train planning and replanning problems on large-scale railway networks under uncertainty. The software recently won the 2020 Flatland Challenge, a NeurIPS competition trying to determine how to efficiently manage dense traffic on rail networks. The software incorporates many state-of-the-art MAPF, or in general, optimization technologies, such as prioritized planning, large neighborhood search, safe interval path planning, minimum communication policies, parallel computing, and simulated annealing. It can plan collision-free paths for thousands of trains within a few minutes and deliver deadlock-free actions in real-time during execution.

SoCS Conference 2020 Conference Paper

Moving Agents in Formation in Congested Environments

Jiaoyang Li 0001
Kexuan Sun 0002
Hang Ma 0001
Ariel Felner
T. K. Satish Kumar
Sven Koenig

In this paper, we formalize and study the Moving Agents in Formation (MAiF) problem, that combines the tasks of finding short collision-free paths for multiple agents and keeping them in close adherence to a desired formation. Previous work includes controller-based algorithms, swarm-based algorithms, and potential-field-based algorithms. They usually focus on only one or the other of these tasks, solve the problem greedily without systematic search, and thus generate costly solutions or even fail to find solutions in congested environment. In this paper, we develop a two-phase search algorithm, called SWARM-MAPF, whose first phase is inspired by swarm-based algorithms (in open regions) and whose second phase is inspired by multi-agent path-finding (MAPF) algorithms (in congested regions). In the first phase, SWARM-MAPF selects a leader among the agents and finds a path for it that is sufficiently far away from the obstacles so that the other agents can preserve the desired formation around it. It also identifies the critical segments of the leader

ICAPS Conference 2020 Conference Paper

New Techniques for Pairwise Symmetry Breaking in Multi-Agent Path Finding

Jiaoyang Li 0001
Graeme Gange
Daniel Harabor
Peter J. Stuckey
Hang Ma 0001
Sven Koenig

We consider two new classes of pairwise path symmetries which appear in the context of Multi-Agent Path Finding (MAPF). The first of them, corridor symmetry, arises when two agents attempt to pass through the same narrow passage in opposite directions. The second, target symmetry, arises when the shortest path of one agent passes through the target location of a second agent after the second agent has already arrived at it. These symmetries can produce an exponential explosion in the space of possible collision resolutions, leading to unacceptable runtimes even for state-of-the-art MAPF algorithms such as Conflict-Based Search (CBS). We propose to break these symmetries using new reasoning techniques that: (1) detect each class of symmetry and (2) resolve them by introducing specialized constraints. We experimentally show that our techniques can, in some cases, more than double the success rate of CBS and improve its runtime by one order of magnitude.

SoCS Conference 2020 Conference Paper

New Techniques for Pairwise Symmetry Breaking in Multi-Agent Path Finding

Jiaoyang Li 0001
Graeme Gange
Daniel Harabor
Peter J. Stuckey
Hang Ma 0001
Sven Koenig

We consider two new types of pairwise path symmetries which appear in the context of Multi-Agent Path Finding (MAPF). The first of them, corridor symmetry, arises when two agents attempt to pass through the same narrow passage but in opposite directions. The second, target symmetry, arises when the shortest path of one agent requires the target location of a second agent after the second agent has already arrived. These symmetries can produce an exponential blowup in the space of possible collision resolutions, leading to timeout failure even for state-of-the-art algorithms such as Conflict-Based Search. We propose to break symmetries using new reasoning techniques that: (1) detect each type of situation and, (2) resolve them by introducing specialized constraints. We implement our ideas in the context of Conflict-Based Search where, in a range of experiments, we report up to an order-of-magnitude improvement in runtime performance and, in some cases, more than a doubling in success rate.

ICAPS Conference 2019 Conference Paper

Disjoint Splitting for Multi-Agent Path Finding with Conflict-Based Search

Jiaoyang Li 0001
Daniel Harabor
Peter J. Stuckey
Hang Ma 0001
Sven Koenig

Multi-Agent Path Finding (MAPF) is the planning problem of finding collision-free paths for a team of agents. We focus on Conflict-Based Search (CBS), a two-level tree-search state-of-the-art MAPF algorithm. The standard splitting strategy used by CBS is not disjoint, i. e. , when it splits a problem into two subproblems, some solutions are shared by both subproblems, which can create duplication of search effort. In this paper, we demonstrate how to improve CBS with disjoint splitting and how to modify the low-level search of CBS to take maximal advantage of it. Experiments show that disjoint splitting increases the success rates and speeds of CBS and its variants by up to 2 orders of magnitude.

SoCS Conference 2019 Conference Paper

Extended Abstract: Lifelong Path Planning with Kinematic Constraintsfor Multi-Agent Pickup and Delivery

Hang Ma 0001
Wolfgang Hönig
T. K. Satish Kumar
Nora Ayanian
Sven Koenig

The Multi-Agent Pickup and Delivery (MAPD) problem models applications where a large number of agents attend to a stream of incoming pickup-and-delivery tasks. Token Passing (TP) is a recent MAPD algorithm that is efficient and effective. We make TP even more efficient and effective by using a novel combinatorial search algorithm, called Safe Interval Path Planning with Reservation Table (SIPPwRT), for single-agent path planning. SIPPwRT uses an advanced data structure that allows for fast updates and lookups of the current paths of all agents in an online setting. The resulting MAPD algorithm TP-SIPPwRT takes kinematic constraints of real robots into account directly during planning, computes continuous agent movements with given velocities that work on non-holonomic robots rather than discrete agent movements with uniform velocity, and is complete for well-formed MAPD instances. We demonstrate its benefits for automated warehouses using both an agent simulator and a standard robot simulator. For example, we demonstrate that it can compute paths for hundreds of agents and thousands of tasks in seconds and is more efficient and effective than existing MAPD algorithms that use a post-processing step to adapt their paths to continuous agent movements with given velocities. This paper was published at AAAI 2019.

SoCS Conference 2019 Conference Paper

Extended Abstract: Searching with Consistent Prioritization for Multi-Agent Path Finding

Hang Ma 0001
Daniel Harabor
Peter J. Stuckey
Jiaoyang Li 0001
Sven Koenig

We study prioritized planning for Multi-Agent Path Finding (MAPF). Existing prioritized MAPF algorithms depend on rule-of-thumb heuristics and random assignment to determine a fixed total priority ordering of all agents a priori. We instead explore the space of all possible partial priority orderings as part of a novel systematic and conflict-driven combinatorial search framework. In a variety of empirical comparisons, we demonstrate state-of-the-art solution qualities and success rates, often with similar runtimes to existing algorithms. We also develop new theoretical results that explore the limitations of prioritized planning, in terms of completeness and optimality, for the first time. This paper was published at AAAI 2019.

SoCS Conference 2019 Conference Paper

Improved Heuristics for Multi-Agent Path Finding with Conflict-Based Search: Preliminary Results

Jiaoyang Li 0001
Eli Boyarski
Ariel Felner
Hang Ma 0001
Sven Koenig

Conflict-Based Search (CBS) and its enhancements are among the strongest algorithms for Multi-Agent Pathfinding. Recent work introduced an admissible heuristic to guide the high-level search of CBS. In this work, we prove the limitation of this heuristic, as it is based on cardinal conflicts only. We then introduce two new admissible heuristics by reasoning about the pairwise dependency between agents. Empirically, CBS with both new heuristics significantly improves the success rate over CBS with the recent heuristic and reduces the number of expanded nodes and runtime by up to a factor of 50, yielding a new state-of-the-art CBS-based algorithm.

SoCS Conference 2019 Conference Paper

Multi-Agent Path Finding for Large Agents

Jiaoyang Li 0001
Pavel Surynek
Ariel Felner
Hang Ma 0001
T. K. Satish Kumar
Sven Koenig

Multi-Agent Path Finding (MAPF) has been widely studied in the AI community. For example, Conflict-Based Search (CBS) is a state-of-the-art MAPF algorithm based on a two-level tree-search. However, previous MAPF algorithms assume that an agent occupies only a single location at any given time, e. g. , a single cell in a grid. This limits their applicability in many real-world domains that have geometric agents in lieu of point agents. Geometric agents are referred to as “large” agents because they can occupy multiple points at the same time. In this paper, we formalize and study LAMAPF, i. e. , MAPF for large agents. We first show how CBS can be adapted to solve LA-MAPF. We then present a generalized version of CBS, called Multi-Constraint CBS (MC-CBS), that adds multiple constraints (instead of one constraint) for an agent when it generates a high-level search node. We introduce three different approaches to choose such constraints as well as an approach to compute admissible heuristics for the high-level search. Experimental results show that all MC-CBS variants outperform CBS by up to three orders of magnitude in terms of runtime. The best variant also outperforms EPEA* (a state-of-the-art A*-based MAPF solver) in all cases and MDD-SAT (a state-of-the-art reduction-based MAPF solver) in some cases.

SoCS Conference 2019 Conference Paper

Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks

Roni Stern
Nathan R. Sturtevant
Ariel Felner
Sven Koenig
Hang Ma 0001
Thayne T. Walker
Jiaoyang Li 0001
Dor Atzmon

The multi-agent pathfinding problem (MAPF) is the fundamental problem of planning paths for multiple agents, where the key constraint is that the agents will be able to follow these paths concurrently without colliding with each other. Applications of MAPF include automated warehouses, autonomous vehicles, and robotics. Research on MAPF has been flourishing in the past couple of years. Different MAPF research papers assume different sets of assumptions, e. g. , whether agents can traverse the same road at the same time, and have different objective functions, e. g. , minimize makespan or sum of agents

SoCS Conference 2019 Conference Paper

Symmetry-Breaking Constraints for Grid-Based Multi-Agent Path Finding

Jiaoyang Li 0001
Daniel Harabor
Peter J. Stuckey
Hang Ma 0001
Sven Koenig

We describe a new way of reasoning about symmetric collisions for Multi-Agent Path Finding (MAPF) on 4-neighbor grids. We also introduce a symmetry-breaking constraint to resolve these conflicts. This specialized technique allows us to identify and eliminate, in a single step, all permutations of two currently assigned but incompatible paths. Each such permutation has exactly the same cost as a current path, and each one results in a new collision between the same two agents. We show that the addition of symmetry-breaking techniques can lead to an exponential reduction in the size of the search space of CBS, a popular framework for MAPF, and report significant improvements in both runtime and success rate versus CBSH and EPEA* – two recent and state-of-the-art MAPF algorithms.

ICAPS Conference 2018 Conference Paper

Adding Heuristics to Conflict-Based Search for Multi-Agent Path Finding

Ariel Felner
Jiaoyang Li 0001
Eli Boyarski
Hang Ma 0001
Liron Cohen 0002
T. K. Satish Kumar
Sven Koenig

Conflict-Based Search (CBS) and its enhancements are among the strongest algorithms for the multi-agent path-finding problem. However, existing variants of CBS do not use any heuristics that estimate future work. In this paper, we introduce different admissible heuristics for CBS by aggregating cardinal conflicts among agents. In our experiments, CBS with these heuristics outperforms previous state-of-the-art CBS variants by up to a factor of five.

IROS Conference 2016 Conference Paper

Formation change for robot groups in occluded environments

Wolfgang Hönig
T. K. Satish Kumar
Hang Ma 0001
Sven Koenig
Nora Ayanian

We study formation change for robot groups in known environments. We are given a team of robots partitioned into groups, where robots in the same group are interchangeable with each other. A formation specifies the locations occupied by each group. The objective is to find collision-free paths that move all robots from a given start formation to a given goal formation. Our algorithm TAPF* has the following features: (a) it incorporates kinematic constraints of robots in form of velocity limits; (b) it maintains a user-specified safety distance between robots; (c) it attempts to minimize the makespan; and (d) it runs efficiently for hundreds of robots and dozens of groups even in dense 3D environments with narrow corridors and other occlusions. We demonstrate the efficiency and effectiveness of TAPF* in simulation and on robots.

ICAPS Conference 2016 Conference Paper

Multi-Agent Path Finding with Kinematic Constraints

Wolfgang Hönig
T. K. Satish Kumar
Liron Cohen 0002
Hang Ma 0001
Hong Xu 0003
Nora Ayanian
Sven Koenig

Multi-Agent Path Finding (MAPF) is well studied in both AI and robotics. Given a discretized environment and agents with assigned start and goal locations, MAPF solvers from AI find collision-free paths for hundreds of agents with user-provided sub-optimality guarantees. However, they ignore that actual robots are subject to kinematic constraints (such as finite maximum velocity limits) and suffer from imperfect plan-execution capabilities. We therefore introduce MAPF-POST, a novel approach that makes use of a simple temporal network to postprocess the output of a MAPF solver in polynomial time to create a plan-execution schedule that can be executed on robots. This schedule works on non-holonomic robots, takes their maximum translational and rotational velocities into account, provides a guaranteed safety distance between them, and exploits slack to absorb imperfect plan executions and avoid time-intensive replanning in many cases. We evaluate MAPF-POST in simulation and on differential-drive robots, showcasing the practicality of our approach.