Multi-Robot Motion Planning

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Latest papers 135

30 papers in the last four weeks, up 329% on the four weeks before. 0.2% of all new papers.

Apr 17, 2026

Scalable Algorithms with Provable Optimality Bounds for the Multiple Watchman Route Problem

In this paper, we tackle the Multiple Watchman Route Problem (MWRP), which aims to find a set of paths that M watchmen can follow such that every location on the map can be seen by at least one watchman. First, we propose multiple methods to reduce the state space over which a search needs to be conducted by pruning map areas that are guaranteed to be seen en route to other areas. Next, we introduce MWRP-CP3, an efficient optimal planner that combines these methods with techniques that improve the quality and calculation time of existing heuristics. We present several suboptimal algorithms with bounds on solution quality, including MxWA*, a general variant of weighted A* for makespan problems. We also present anytime variations of our suboptimal algorithms, as well as techniques to improve an existing suboptimal solution by solving multiple decomposed sub-problems. We show that MWRP-CP3 can reduce the search space by more than 95% and runs more than 200x faster than existing optimal algorithms on 2D grid maps. We also show that our suboptimal algorithms solve maps 3x larger than those solvable by MWRP-CP3. See mwrp-cp3.github.io for the open source codebase and video demonstrations.

Srikar Gouru, Ariel Felner, Jiaoyang Li

Carnegie Mellon University · Ben-Gurion University of the Negev

Multi-Robot Motion Planning Multi-Agent Path Finding

Apr 16, 2026

Trajectory Planning for a Multi-UAV Rigid-Payload Cascaded Transportation System Based on Enhanced Tube-RRT*

This paper presents a two-stage trajectory planning framework for a multi-UAV rigid-payload cascaded transportation system, aiming to address planning challenges in densely cluttered environments. In Stage I, an Enhanced Tube-RRT* algorithm is developed by integrating active hybrid sampling and an adaptive expansion strategy, enabling rapid generation of a safe and feasible virtual tube in environments with dense obstacles. Moreover, a trajectory smoothness cost is explicitly incorporated into the edge cost to reduce excessive turns and thereby mitigate cable-induced oscillations. Simulation results demonstrate that the proposed Enhanced Tube-RRT* achieves a higher success rate and effective sampling rate than mixed-sampling Tube-RRT* (STube-RRT*) and adaptive-extension Tube-RRT* (AETube-RRT*), while producing a shorter optimal path with a smaller cumulative turning angle. In Stage II, a convex quadratic program is formulated by considering payload translational and rotational dynamics, cable tension constraints, and collision-safety constraints, yielding a smooth, collision-free desired payload trajectory. Finally, a centralized geometric control scheme is applied to the cascaded system to validate the effectiveness and feasibility of the proposed planning framework, offering a practical solution for payload attitude maneuvering in densely cluttered environments.

Jianqiao Yu, Jia Li, Tianhua Gao

Beijing Institute of Technology, Beijing , China · University of Tsukuba, Ibaraki, Japan

Multi-Robot Motion Planning Trajectory Optimization Multi-Robot Systems Robot Collision Avoidance Sampling-Based Motion Planning

Apr 8, 2026

Differentiable Environment-Trajectory Co-Optimization for Safe Multi-Agent Navigation

The environment plays a critical role in multi-agent navigation by imposing spatial constraints, rules, and limitations that agents must navigate around. Traditional approaches treat the environment as fixed, without exploring its impact on agents' performance. This work considers environment configurations as decision variables, alongside agent actions, to jointly achieve safe navigation. We formulate a bi-level problem, where the lower-level sub-problem optimizes agent trajectories that minimize navigation cost and the upper-level sub-problem optimizes environment configurations that maximize navigation safety. We develop a differentiable optimization method that iteratively solves the lower-level sub-problem with interior point methods and the upper-level sub-problem with gradient ascent. A key challenge lies in analytically coupling these two levels. We address this by leveraging KKT conditions and the Implicit Function Theorem to compute gradients of agent trajectories w.r.t. environment parameters, enabling differentiation throughout the bi-level structure. Moreover, we propose a novel metric that quantifies navigation safety as a criterion for the upper-level environment optimization, and prove its validity through measure theory. Our experiments validate the effectiveness of the proposed framework in a variety of safety-critical navigation scenarios, inspired from warehouse logistics to urban transportation. The results demonstrate that optimized environments provide navigation guidance, improving both agents' safety and efficiency.

Zhan Gao, Gabriele Fadini, Stelian Coros +1

Department of Computer Science and Technology, University of Cambridge, UK · Zurich University of Applied Sciences, ZHAW, Winterthur, Swizerland · Computational Robotics Lab, Department of Computer Science, ETH Zurich, Switzerland

Robot Navigation Safe Robot Navigation Multi-Robot Motion Planning Trajectory Optimization Bilevel Optimization

Apr 8, 2026

Train-Small Deploy-Large: Leveraging Diffusion-Based Multi-Robot Planning

Learning based multi-robot path planning methods struggle to scale or generalize to changes, particularly variations in the number of robots during deployment. Most existing methods are trained on a fixed number of robots and may tolerate a reduced number during testing, but typically fail when the number increases. Additionally, training such methods for a larger number of agents can be both time consuming and computationally expensive. However, analytical methods can struggle to scale computationally or handle dynamic changes in the environment. In this work, we propose to leverage a diffusion model based planner capable of handling dynamically varying number of agents. Our approach is trained on a limited number of agents and generalizes effectively to larger numbers of agents during deployment. Results show that integrating a single shared diffusion model based planner with dedicated inter-agent attention computation and temporal convolution enables a train small deploy-large paradigm with good accuracy. We validate our method across multiple scenarios and compare the performance with existing multi-agent reinforcement learning techniques and heuristic control based methods.

Siddharth Singh, Soumee Guha, Qing Chang +1

Department of Mechanical & Aerospace Engineering, University of Virginia · Department of Electrical & Computer Engineering, University of Virginia

Multi-Robot Motion Planning Multi-Agent Path Finding Diffusion-Based Planning

Apr 6, 2026

FORMULA: FORmation MPC with neUral barrier Learning for safety Assurance

Multi-robot systems (MRS) are essential for large-scale applications such as disaster response, material transport, and warehouse logistics, yet ensuring robust, safety-aware formation control in cluttered and dynamic environments remains a major challenge. Existing model predictive control (MPC) approaches suffer from limitations in scalability and provable safety, while control barrier functions (CBFs), though principled for safety enforcement, are difficult to handcraft for large-scale nonlinear systems. This paper presents FORMULA, a safe distributed, learning-enhanced predictive control framework that integrates MPC with Control Lyapunov Functions (CLFs) for stability and neural network-based CBFs for decentralized safety, eliminating manual safety constraint design. This scheme maintains formation integrity during obstacle avoidance, resolves deadlocks in dense configurations, and reduces online computational load. Simulation results demonstrate that FORMULA enables scalable, safety-aware, formation-preserving navigation for multi-robot teams in complex environments.

Qintong Xie, Weishu Zhan, Peter Chin

Thayer School of Engineering, Dartmouth College, NH 03784. · Department of Computer Science, The University of Manchester, Manchester M13 9PL, United Kingdom.

Multi-Robot Formation Control Model Predictive Control Multi-Robot Motion Planning Control Barrier Functions

Apr 1, 2026

Collaborative Task and Path Planning for Heterogeneous Robotic Teams using Multi-Agent PPO

Efficient robotic extraterrestrial exploration requires robots with diverse capabilities, ranging from scientific measurement tools to advanced locomotion. A robotic team enables the distribution of tasks over multiple specialized subsystems, each providing specific expertise to complete the mission. The central challenge lies in efficiently coordinating the team to maximize utilization and the extraction of scientific value. Classical planning algorithms scale poorly with problem size, leading to long planning cycles and high inference costs due to the combinatorial growth of possible robot-target allocations and possible trajectories. Learning-based methods are a viable alternative that move the scaling concern from runtime to training time, setting a critical step towards achieving real-time planning. In this work, we present a collaborative planning strategy based on Multi-Agent Proximal Policy Optimization (MAPPO) to coordinate a team of heterogeneous robots to solve a complex target allocation and scheduling problem. We benchmark our approach against single-objective optimal solutions obtained through exhaustive search and evaluate its ability to perform online replanning in the context of a planetary exploration scenario.

Matthias Rubio, Julia Richter, Hendrik Kolvenbach +1

Robotic Systems Lab (RSL), ETH Zürich, Zürich, Switzerland

Multi-Robot Task Allocation Multi-Robot Motion Planning Multi-Agent Reinforcement Learning Heterogeneous MARL

Mar 27, 2026

Line-of-Sight-Constrained Multi-Robot Mapless Navigation via Polygonal Visible Regions

Multi-robot systems rely on underlying connectivity to ensure reliable communication and timely coordination. This paper studies the line-of-sight (LoS) connectivity maintenance problem in multi-robot navigation with unknown obstacles. Prior works typically assume known environment maps to formulate LoS constraints between robots, which hinders their practical deployment. To overcome this limitation, we propose an inherently distributed approach where each robot only constructs an egocentric visible region based on its real-time LiDAR scans, instead of endeavoring to build a global map online. The individual visible regions are shared through distributed communication to establish inter-robot LoS constraints, which are then incorporated into a multi-robot navigation framework to ensure LoS-connectivity. Moreover, we enhance the robustness of connectivity maintenance by proposing a more accurate LoS-distance metric, which further enables flexible topology optimization that eliminates redundant and effort-demanding connections. The proposed framework is evaluated through extensive multi-robot navigation and exploration tasks in both simulation and real-world experiments. Results show that it reliably maintains LoS-connectivity between robots in challenging environments cluttered with obstacles, even under large visible ranges and fragile minimal topologies, where existing methods consistently fail. Ablation studies also reveal that topology optimization boosts navigation efficiency by around

20\%

, demonstrating the framework's potential for efficient navigation under connectivity constraints.

Ruofei Bai, Shenghai Yuan, Xinhang Xu +5

Robot Navigation Multi-Robot Coordination Multi-Robot Motion Planning

Mar 26, 2026

Integrated Multi-Drone Task Allocation, Sequencing, and Optimal Trajectory Generation in Obstacle-Rich 3D Environments

Coordinating teams of aerial robots in cluttered three-dimensional (3D) environments requires a principled integration of discrete mission planning-deciding which robot serves which goals and in what order -- with continuous-time trajectory synthesis that enforces collision avoidance and dynamic feasibility. This paper introduces IMD-TAPP (Integrated Multi-Drone Task Allocation and Path Planning), an end-to-end framework that jointly addresses multi-goal allocation, tour sequencing, and safe trajectory generation for quadrotor teams operating in obstacle-rich spaces. IMD--TAPP first discretizes the workspace into a 3D navigation graph and computes obstacle-aware robot-to-goal and goal-to-goal travel costs via graph-search-based pathfinding. These costs are then embedded within an Injected Particle Swarm Optimization (IPSO) scheme, guided by multiple linear assignment, to efficiently explore coupled assignment/ordering alternatives and to minimize mission makespan. Finally, the resulting waypoint tours are transformed into time-parameterized minimum-snap trajectories through a generation-and-optimization routine equipped with iterative validation of obstacle clearance and inter-robot separation, triggering re-planning when safety margins are violated. Extensive MATLAB simulations across cluttered 3D scenarios demonstrate that IMD--TAPP consistently produces dynamically feasible, collision-free trajectories while achieving competitive completion times. In a representative case study with two drones serving multiple goals, the proposed approach attains a minimum mission time of 136~s while maintaining the required safety constraints throughout execution.

Yunes Alqudsi, Murat Makaraci

Aerospace Engineering Department, Faculty of Aeronautics and Astronautics, Kocaeli University, Kocaeli, Turkiye · Interdisciplinary Research Center for Aviation and Space Exploration, KFUPM, Dhahran, Saudi Arabia · Mechanical Engineering Department, Faculty of Aeronautics and Astronautics, Kocaeli University, Kocaeli, Turkiye

Multi-Robot Motion Planning Aerial Robotics Multi-Robot Task Allocation Safe Motion Planning Trajectory Generation

Mar 25, 2026

Enhancing Drone Light Shows Performances: Optimal Allocation and Trajectories for Swarm Drone Formations

Drone light shows (DLShows) represent a rapidly growing application of swarm robotics, creating captivating aerial displays through the synchronized flight of hundreds or thousands of unmanned aerial vehicles (UAVs) as environmentally friendly and reusable alternatives to traditional pyrotechnics. This domain presents unique challenges in optimally assigning drones to visual waypoints and generating smooth, collision-free trajectories at a very large scale. This article introduces the Unified Assignment and Trajectory Generation (UATG) framework. The proposed approach concurrently solves two core problems: the optimal assignment of drones to designated goal locations and the generation of dynamically feasible, collision-free, time-parameterized trajectories. The UATG framework is specifically designed for DLShows, ensuring minimal transition times between formations and guaranteeing inter-drone collision avoidance. A key innovation is its exceptional computational efficiency, enabling the coordination of large-scale in real-time; for instance, it computes the optimal assignment and trajectories for 1008 drones in approximately one second on a standard laptop. Extensive simulations in realistic environments validate the framework's performance, demonstrating its capability to orchestrate complex formations, from alphanumeric characters to intricate 3D shapes, with precision and visual smoothness. This work provides a critical advancement for the DLShow industry, offering a practical and scalable solution for generating complex aerial choreography and establishing a valuable benchmark for ground control station software designed for the efficient coordination of multiple UAVs. A supplemental animated simulation of this work is available at https://youtu.be/-Fjrhw03594.

Yunes Alqudsi

Trajectory Generation Multi-Robot Motion Planning Aerial Robotics Multi-Robot Formation Control Multi-Robot Task Allocation

Mar 25, 2026

Wireless communication empowers online scheduling of partially-observable transportation multi-robot systems in a smart factory

Achieving agile and reconfigurable production flows in smart factories depends on online multi-robot task assignment (MRTA), which requires online collision-free and congestion-free route scheduling of transportation multi-robot systems (T-MRS), e.g., collaborative automatic guided vehicles (AGVs). Due to the real-time operational requirements and dynamic interactions between T-MRS and production MRS, online scheduling under partial observability in dynamic factory environments remains a significant and under-explored challenge. This paper proposes a novel communication-enabled online scheduling framework that explicitly couples wireless machine-to-machine (M2M) networking with route scheduling, enabling AGVs to exchange intention information, e.g., planned routes, to overcome partial observations and assist complex computation of online scheduling. Specifically, we determine intelligent AGVs' intention and sensor data as new M2M traffic and tailor the retransmission-free multi-link transmission networking to meet real-time operation demands. This scheduling-oriented networking is then integrated with a simulated annealing-based MRTA scheme and a congestion-aware A*-based route scheduling method. The integrated communication and scheduling scheme allows AGVs to dynamically adjust collision-free and congestion-free routes with reduced computational overhead. Numerical experiments shows the impacts from wireless communication on the performance of T-MRS and suggest that the proposed integrated scheme significantly enhances scheduling efficiency compared to other baselines, even under high AGV load conditions and limited channel resources. Moreover, the results reveal that the scheduling-oriented wireless M2M communication design fundamentally differs from human-to-human communications, implying new technological opportunities in a wireless networked smart factory.

Yaxin Liao, Qimei Cui, Kwang-Cheng Chen +5

National Engineering Research Center of Mobile Network Technologies, Beijing University of Posts and Telecommunications, Beijing 100876, China · Department of Electrical Engineering, University of South Florida, Tampa, FL 33620 USA · School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Multi-Robot Coordination Multi-Robot Motion Planning Multi-Robot Task Allocation Manufacturing

Mar 24, 2026

Learning Multi-Agent Local Collision-Avoidance for Collaborative Carrying tasks with Coupled Quadrupedal Robots

Robotic collaborative carrying could greatly benefit human activities like warehouse and construction site management. However, coordinating the simultaneous motion of multiple robots represents a significant challenge. Existing works primarily focus on obstacle-free environments, making them unsuitable for most real-world applications. Works that account for obstacles, either overfit to a specific terrain configuration or rely on pre-recorded maps combined with path planners to compute collision-free trajectories. This work focuses on two quadrupedal robots mechanically connected to a carried object. We propose a Reinforcement Learning (RL)-based policy that enables tracking a commanded velocity direction while avoiding collisions with nearby obstacles using only onboard sensing, eliminating the need for precomputed trajectories and complete map knowledge. Our work presents a hierarchical architecture, where a perceptive high-level object-centric policy commands two pretrained locomotion policies. Additionally, we employ a game-inspired curriculum to increase the complexity of obstacles in the terrain progressively. We validate our approach on two quadrupedal robots connected to a bar via spherical joints, benchmarking it against optimization-based and decentralized RL baselines. Our hardware experiments demonstrate the ability of our system to locomote in unknown environments without the need for a map or a path planner. The video of our work is available in the multimedia material.

Francesca Bray, Simone Tolomei, Andrei Cramariuc +2

Robotic Systems Lab, ETH Zurich. · Centro di Ricerca “Enrico Piaggio”, and Dipartimento di Ingegneria dell’Informazione, Universit`a di Pisa, Pisa, Italy.

Multi-Robot Systems Multi-Robot Motion Planning Cooperative MARL Robot Locomotion

Mar 23, 2026

Can a Robot Walk the Robotic Dog: Triple-Zero Collaborative Navigation for Heterogeneous Multi-Agent Systems

We present Triple Zero Path Planning (TZPP), a collaborative framework for heterogeneous multi-robot systems that requires zero training, zero prior knowledge, and zero simulation. TZPP employs a coordinator--explorer architecture: a humanoid robot handles task coordination, while a quadruped robot explores and identifies feasible paths using guidance from a multimodal large language model. We implement TZPP on Unitree G1 and Go2 robots and evaluate it across diverse indoor and outdoor environments, including obstacle-rich and landmark-sparse settings. Experiments show that TZPP achieves robust, human-comparable efficiency and strong adaptability to unseen scenarios. By eliminating reliance on training and simulation, TZPP offers a practical path toward real-world deployment of heterogeneous robot cooperation. Our code and video are provided at: https://github.com/triple-zeropp/Triple-zero-robot-agent

Yaxuan Wang, Yifan Xiang, Ke Li +5

Yuanpei College, Peking University · School of Computer Science, Peking University · School of Computer Science, Beijing University of Posts and Telecommunications +2

Robot Navigation Multi-Robot Systems Multi-Robot Coordination Multi-Robot Motion Planning Large Language Model-Based Robot Planning

Mar 23, 2026

Auction-Based Task Allocation with Energy-Conscientious Trajectory Optimization for AMR Fleets

This paper presents a hierarchical two-stage framework for multi-robot task allocation and trajectory optimization in asymmetric task spaces: (1) a sequential auction allocates tasks using closed-form bid functions, and (2) each robot independently solves an optimal control problem for energy-minimal trajectories with a physics-based battery model, followed by a collision avoidance refinement step using pairwise proximity penalties. Event-triggered warm-start rescheduling with bounded trigger frequency handles robot faults, priority arrivals, and energy deviations. Across 505 scenarios with 2-20 robots and up to 100 tasks on three factory layouts, both energy- and distance-based auction variants achieve 11.8% average energy savings over nearest-task allocation, with rescheduling latency under 10 ms. The central finding is that bid-metric performance is regime-dependent: in uniform workspaces, distance bids outperform energy bids by 3.5% (p < 0.05, Wilcoxon) because a 15.7% closed-form approximation error degrades bid ranking accuracy to 87%; however, when workspace friction heterogeneity is sufficient (r < 0.85 energy-distance correlation), a zone-aware energy bid outperforms distance bids by 2-2.4%. These results provide practitioner guidance: use distance bids in near-uniform terrain and energy-aware bids when friction variation is significant.

Jiachen Li, Soovadeep Bakshi, Jian Chu +2

All authors are with the Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.

Multi-Robot Task Allocation Multi-Robot Motion Planning Trajectory Optimization

Mar 20, 2026

Multi-Robot Learning-Informed Task Planning Under Uncertainty

We want a multi-robot team to complete complex tasks in minimum time where the locations of task-relevant objects are not known. Effective task completion requires reasoning over long horizons about the likely locations of task-relevant objects, how individual actions contribute to overall progress, and how to coordinate team efforts. Planning in this setting is extremely challenging: even when task-relevant information is partially known, coordinating which robot performs which action and when is difficult, and uncertainty introduces a multiplicity of possible outcomes for each action, which further complicates long-horizon decision-making and coordination. To address this, we propose a multi-robot planning abstraction that integrates learning to estimate uncertain aspects of the environment with model-based planning for long-horizon coordination. We demonstrate the efficient multi-stage task planning of our approach for 1, 2, and 3 robot teams over competitive baselines in large ProcTHOR household environments. Additionally, we demonstrate the effectiveness of our approach with a team of two LoCoBot mobile robots in real household settings.

Abhish Khanal, Abhishek Paudel, Hung Pham +1

Department of Computer Science, George Mason University, Fairfax, VA, 22030, USA.

Multi-Robot Coordination Decision-Making under Uncertainty Multi-Robot Motion Planning Model-Based Planning

Mar 19, 2026

Unlabeled Multi-Robot Motion Planning with Improved Separation Trade-offs

We study unlabeled MRMP for unit-disk robots in a polygonal environment. Although the problem is hard in general, polynomial-time solutions exist under appropriate separation assumptions on start and target positions. Banyassady et al.(SoCG'22) guarantee feasibility in simple polygons under start--start and target--target distances of at least

4

, and start--target distances of at least

3

, but without optimality guarantees. Solovey et al.(RSS'15) provide a near-optimal solution in general polygonal domains, under stricter conditions: start/target positions must have pairwise distance at least

4

, and at least

\sqrt{5}\approx2.236

from obstacles. This raises the question of whether polynomial-time algorithms can be obtained in even more densely packed environments. In this paper we present a generalized algorithm that achieve different tradeoffs on the robots-separation

ρ

and obstacles-separation

ω

, all significantly improving upon the state of the art. Specifically, we obtain polynomial-time constant-approximation algorithms to minimize the total path length when (i)

ρ=2\frac{2}{3}

and

ω=1\frac{2}{3}

, or (ii)

ρ\approx3.291

and

ω\approx1.354

. These solutions are weakly-monotone; we also provide a monotone solution requiring

ω=\approx1.614

and

ρ=4

. We prove that monotone plans may not exist when

ω<1.614

, and weakly-monotone plans may not exist when

ω<1.354

. We then present tradeoffs between the separation bounds and the approximation factor, specifically achieving an (almost) optimal bound of

ρ=2

at the cost of a linear approximation factor and requiring

ω=2

. This applies also for the labeled variant of MRMP, in which case we show a tight bound on

ω

. Finally, we show that without any robots-separation assumption, obstacles-separation of at least

1.5

may be necessary for a solution to exist.

Tsuri Farhana, Omrit Filtser, Shalev Goldshtein

Department of Computer Science, Ben Gurion University, Beer Sheva, Israel. · Department of Mathematics and Computer Science, The Open University of Israel, Ra’anana, Israel

Multi-Robot Motion Planning Approximation Algorithms

Mar 19, 2026

ADMM-Based Distributed MPC with Control Barrier Functions for Safe Multi-Robot Quadrupedal Locomotion

This paper proposes a fully decentralized model predictive control (MPC) framework with control barrier function (CBF) constraints for safety-critical trajectory planning in multi-robot legged systems. The incorporation of CBF constraints introduces explicit inter-agent coupling, which prevents direct decomposition of the resulting optimal control problems. To address this challenge, we reformulate the centralized safety-critical MPC problem using a structured distributed optimization framework based on the alternating direction method of multipliers (ADMM). By introducing a novel node-edge splitting formulation with consensus constraints, the proposed approach decomposes the global problem into independent node-local and edge-local quadratic programs that can be solved in parallel using only neighbor-to-neighbor communication. This enables fully decentralized trajectory optimization with symmetric computational load across agents while preserving safety and dynamic feasibility. The proposed framework is integrated into a hierarchical locomotion control architecture for quadrupedal robots, combining high-level distributed trajectory planning, mid-level nonlinear MPC enforcing single rigid body dynamics, and low-level whole-body control enforcing full-order robot dynamics. The effectiveness of the proposed approach is demonstrated through hardware experiments on two Unitree Go2 quadrupedal robots and numerical simulations involving up to four robots navigating uncertain environments with rough terrain and external disturbances. The results show that the proposed distributed formulation achieves performance comparable to centralized MPC while reducing the average per-cycle planning time by up to 51% in the four-agent case, enabling efficient real-time decentralized implementation.

Yicheng Zeng, Ruturaj S. Sambhus, Basit Muhammad Imran +3

Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA 24061, USA · The University of Texas at El Paso, El Paso, TX 79968, USA

Model Predictive Control Multi-Robot Motion Planning Multi-Robot Coordination Quadruped Locomotion

Mar 16, 2026

Multi-robot Graph Traversal with Support Coordination under Stochastically Moving Adversaries

Cooperative multi-robot missions require team of robots to traverse environments where adversaries or hazards with stochastic dynamics induce time-varying traversal risk. While support coordination--where robots assist teammates in traversing risky regions--can significantly reduce mission costs, its effectiveness depends on the team's ability to anticipate future risk. We formulate support-based multi-robot graph traversal problem with stochastically moving adversaries, where future risky regions become uncertain as adversaries move through the environment. When adversaries remain stationary, our formulation reduces to the static risky-edge setting. To address the stochastic case, we model individual adversaries as first-order Markov stay-move processes over graph edges and propagate their occupancy distributions over a finite planning horizon to obtain time-indexed edge-risk forecasts. These forecasts inform the support candidate selection and joint robot path planning. Experimental results show that forecast-informed support decisions consistently lower expected team cost relative to evaluated baselines in stochastic motion settings.

Manshi Limbu, Xuan Wang, Gregory J. Stein +2

George Mason University

Multi-Robot Motion Planning Multi-Robot Coordination Graph-Based Planning Safe Motion Planning

Mar 14, 2026

Multi-Robot Coordination for Planning under Context Uncertainty

Real-world robots often operate in settings where objective priorities depend on the underlying context of operation. When the underlying context is unknown apriori, multiple robots may have to coordinate to gather informative observations to infer the context, since acting based on an incorrect context can lead to misaligned and unsafe behavior. Once the underlying true context is inferred, the robots optimize their task-specific objectives in the preference order induced by the context. We formalize this problem as a Multi-Robot Context-Uncertain Stochastic Shortest Path (MR-CUSSP), which captures context-relevant information at landmark states through joint observations. Our two-stage solution approach is composed of: (1) CIMOP (Coordinated Inference for Multi-Objective Planning) to compute plans that guide robots toward informative landmarks to efficiently infer the true context, and (2) LCBS (Lexicographic Conflict-Based Search) for collision-free multi-robot path planning with lexicographic objective preferences, induced by the context. We evaluate the algorithms using three simulated domains and demonstrate its practical applicability using five mobile robots in the salp domain setup.

Pulkit Rustagi, Kyle Hollins Wray, Sandhya Saisubramanian

Collaborative Robotics and Intelligent Systems (CoRIS) Institute, Oregon State University, Corvallis, OR 97331, USA · Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA

Multi-Robot Motion Planning Multi-Robot Coordination Active Information Gathering Conflict-Based Search

Mar 13, 2026

Route Fragmentation Based on Resource-centric Prioritisation for Efficient Multi-Robot Path Planning in Agricultural Environments

Agricultural environments present high proportions of spatially dense navigation bottlenecks for long-term navigation and operational planning of agricultural mobile robots. The existing agent-centric multi-robot path planning (MRPP) approaches resolve conflicts from the perspective of agents, rather than from the resources under contention. Further, the density of such contentions limits the capabilities of spatial interleaving, a concept that many planners rely on to achieve high throughput. In this work, two variants of the priority-based Fragment Planner (FP) are presented as resource-centric MRPP algorithms that leverage route fragmentation to enable partial route progression and limit the impact of binary-based waiting. These approaches are evaluated in lifelong simulation over a 3.6km topological map representing a commercial polytunnel environment. Their performances are contrasted against 5 baseline algorithms with varying robotic fleet sizes. The Fragment Planners achieved significant gains in throughput compared with Prioritised Planning (PP) and Priority-Based Search (PBS) algorithms. They further demonstrated a task throughput of 95% of the optimal task throughput over the same time period. This work shows that, for long-term deployment of agricultural robots in corridor-dominant agricultural environments, resource-centric MRPP approaches are a necessity for high-efficacy operational planning.

James R. Heselden, Gautham P. Das

Lincoln Centre for Autonomous Systems, University of Lincoln, UK. · Lincoln Institute for Agri-food Technology, University of Lincoln, UK.

Multi-Robot Motion Planning Robust MAPF Agricultural Robotics

Mar 13, 2026

Conflict Mitigation in Shared Environments using Flow-Aware Multi-Agent Path Finding

Deploying multi-robot systems in environments shared with dynamic and uncontrollable agents presents significant challenges, especially for large robot fleets. In such environments, individual robot operations can be delayed due to unforeseen conflicts with uncontrollable agents. While existing research primarily focuses on preserving the completeness of Multi-Agent Path Finding (MAPF) solutions considering delays, there is limited emphasis on utilizing additional environmental information to enhance solution quality in the presence of other dynamic agents. To this end, we propose Flow-Aware Multi-Agent Path Finding (FA-MAPF), a novel framework that integrates learned motion patterns of uncontrollable agents into centralized MAPF algorithms. Our evaluation, conducted on a diverse set of benchmark maps with simulated uncontrollable agents and on a real-world map with recorded human trajectories, demonstrates the effectiveness of FA-MAPF compared to state-of-the-art baselines. The experimental results show that FA-MAPF can consistently reduce conflicts with uncontrollable agents, up to 55%, without compromising task efficiency.

Lukas Heuer, Yufei Zhu, Luigi Palmieri +4

L. Heuer, Y. Zhu and M. Magnusson - Örebro University · L. Palmieri, A. Rudenko and A. Mannucci - Bosch Research · A. Rudenko - Technical University of Munich +1

Multi-Robot Motion Planning Multi-Agent Path Finding Multi-Robot Systems

Mar 11, 2026

GRACE: A Unified 2D Multi-Robot Path Planning Simulator & Benchmark for Grid, Roadmap, And Continuous Environments

Advancing Multi-Agent Pathfinding (MAPF) and Multi-Robot Motion Planning (MRMP) requires platforms that enable transparent, reproducible comparisons across modeling choices. Existing tools either scale under simplifying assumptions (grids, homogeneous agents) or offer higher fidelity with less comparable instrumentation. We present GRACE, a unified 2D simulator+benchmark that instantiates the same task at multiple abstraction levels (grid, roadmap, continuous) via explicit, reproducible operators and a common evaluation protocol. Our empirical results on public maps and representative planners enable commensurate comparisons on a shared instance set. Furthermore, we quantify the expected representation-fidelity trade-offs (MRMP solves instances at higher fidelity but lower speed, while grid/roadmap planners scale farther). By consolidating representation, execution, and evaluation, GRACE thereby aims to make cross-representation studies more comparable and provides a means to advance multi-robot planning research and its translation to practice.

Chuanlong Zang, Anna Mannucci, Isabelle Barz +3

Robert Bosch GmbH, Corporate Research, Stuttgart, Germany. · Technical University of Berlin, Berlin, Germany.

Multi-Robot Motion Planning Multi-Agent Path Finding Roadmap-Based Motion Planning

Mar 11, 2026

Interleaving Scheduling and Motion Planning with Incremental Learning of Symbolic Space-Time Motion Abstractions

Task and Motion Planning combines high-level task sequencing (what to do) with low-level motion planning (how to do it) to generate feasible, collision-free execution plans. However, in many real-world domains, such as automated warehouses, tasks are predefined, shifting the challenge to if, when, and how to execute them safely and efficiently under resource, time and motion constraints. In this paper, we formalize this as the Scheduling and Motion Planning problem for multi-object navigation in shared workspaces. We propose a novel solution framework that interleaves off-the-shelf schedulers and motion planners in an incremental learning loop. The scheduler generates candidate plans, while the motion planner checks feasibility and returns symbolic feedback, i.e., spatial conflicts and timing adjustments, to guide the scheduler towards motion-feasible solutions. We validate our proposal on logistics and job-shop scheduling benchmarks augmented with motion tasks, using state-of-the-art schedulers and sampling-based motion planners. Our results show the effectiveness of our framework in generating valid plans under complex temporal and spatial constraints, where synchronized motion is critical.

Elisa Tosello, Arthur Bit-Monnot, Davide Lusuardi +2

Fondazione Bruno Kessler, Trento, Italy · LAAS-CNRS, Université de Toulouse, CNRS, INSA, Toulouse, France

Motion Planning Constrained Motion Planning Multi-Robot Motion Planning

Mar 10, 2026

Robust Spatiotemporal Motion Planning for Multi-Agent Autonomous Racing via Topological Gap Identification and Accelerated MPC

High-speed multi-agent autonomous racing demands robust spatiotemporal planning and precise control under strict computational limits. Current methods often oversimplify interactions or abandon strict kinematic constraints. We resolve this by proposing a Topological Gap Identification and Accelerated MPC framework. By predicting opponent behaviors via SGPs, our method constructs dynamic occupancy corridors to robustly select optimal overtaking gaps. We ensure strict kinematic feasibility using a Linear Time-Varying MPC powered by a customized Pseudo-Transient Continuation (PTC) solver for high-frequency execution. Experimental results on the F1TENTH platform show that our method significantly outperforms state-of-the-art baselines: it reduces total maneuver time by 51.6% in sequential scenarios, consistently maintains an overtaking success rate exceeding 81% in dense bottlenecks, and lowers average computational latency by 20.3%, pushing the boundaries of safe and high-speed autonomous racing.

Mingyi Zhang, Cheng Hu, Yiqin Wang +3

Department of Control Science and Engineering, Zhejiang University. · Center for Project-Based Learning, D-ITET, ETH Zurich.

Autonomous Racing Multi-Robot Motion Planning Constrained Motion Planning Model Predictive Control Dynamic Obstacle Avoidance

Mar 9, 2026

Scale-Plan: Scalable Language-Enabled Task Planning for Heterogeneous Multi-Robot Teams

Long-horizon task planning for heterogeneous multi-robot systems is essential for deploying collaborative teams in real-world environments; yet, it remains challenging due to the large volume of perceptual information, much of which is irrelevant to task objectives and burdens planning. Traditional symbolic planners rely on manually constructed problem specifications, limiting scalability and adaptability, while recent large language model (LLM)-based approaches often suffer from hallucinations and weak grounding-i.e., poor alignment between generated plans and actual environmental objects and constraints-in object-rich settings. We present Scale-Plan, a scalable LLM-assisted framework that generates compact, task-relevant problem representations from natural language instructions. Given a PDDL domain specification, Scale-Plan constructs an action graph capturing domain structure and uses shallow LLM reasoning to guide a structured graph search that identifies a minimal subset of relevant actions and objects. By filtering irrelevant information prior to planning, Scale-Plan enables efficient decomposition, allocation, and long-horizon plan generation. We evaluate our approach on complex multi-agent tasks and introduce MAT2-THOR, a cleaned benchmark built on AI2-THOR for reliable evaluation of multi-robot planning systems. Scale-Plan outperforms pure LLM and hybrid LLM-PDDL baselines across all metrics, improving scalability and reliability. Project website: https://github.com/honda-research-institute/Scale_Plan

Piyush Gupta, Sangjae Bae, Jiachen Li +1

Honda Research Institute, San Jose, CA 95134, USA. · Georgia Institute of Technology, Atlanta, GA 30332, USA.

Large Language Model-Based Robot Planning Symbolic Planning Multi-Robot Task Allocation Multi-Robot Motion Planning

Mar 9, 2026

Perception-Aware Communication-Free Multi-UAV Coordination in the Wild

We present a communication-free method for safe multi-robot coordination in complex environments such as forests with dense canopy cover, where GNSS is unavailable. Our approach relies on an onboard anisotropic 3D LiDAR sensor used for SLAM as well as for detecting obstacles and neighboring robots. We develop a novel perception-aware 3D navigation framework that enables robots to safely and effectively progress toward a goal region despite limited sensor field-of-view. The approach is evaluated through extensive simulations across diverse scenarios and validated in real-world field experiments, demonstrating its scalability, robustness, and reliability.

Manuel Boldrer, Michal Kamler, Afzal Ahmad +1

Department of Cybernetics, Czech Technical University in Prague, Karlovo namesti 13, 121 35 Prague 2, Czechia.

Multi-Robot Coordination UAV Navigation Safe Robot Navigation Multi-Robot Motion Planning

Mar 9, 2026

VORL-EXPLORE: A Hybrid Learning Planning Approach to Multi-Robot Exploration in Dynamic Environments

Hierarchical multi-robot exploration commonly decouples frontier allocation from local navigation, which can make the system brittle in dense and dynamic environments. Because the allocator lacks direct awareness of execution difficulty, robots may cluster at bottlenecks, trigger oscillatory replanning, and generate redundant coverage. We propose VORL-EXPLORE, a hybrid learning and planning framework that addresses this limitation through execution fidelity, a shared estimate of local navigability that couples task allocation with motion execution. This fidelity signal is incorporated into a fidelity-coupled Voronoi objective with inter-robot repulsion to reduce contention before it emerges. It also drives a risk-aware adaptive arbitration mechanism between global A* guidance and a reactive reinforcement learning policy, balancing long-range efficiency with safe interaction in confined spaces. The framework further supports online self-supervised recalibration of the fidelity model using pseudo-labels derived from recent progress and safety outcomes, enabling adaptation to non-stationary obstacles without manual risk tuning. We evaluate this capability separately in a dedicated severe-traffic ablation. Extensive experiments in randomized grids and a Gazebo factory scenario show high success rates, shorter path length, lower overlap, and robust collision avoidance. The source code will be made publicly available upon acceptance.

Ning Liu, Sen Shen, Zheng Li +4

The University of Western Australia · The Chinese University of Hong Kong · Karlsruhe Institute of Technology +1

Multi-Robot Exploration Multi-Robot Motion Planning Multi-Robot Task Allocation Robot Policy Learning

Mar 6, 2026

Collaborative Planning with Concurrent Synchronization for Operationally Constrained UAV-UGV Teams

Collaborative planning under operational constraints is an essential capability for heterogeneous robot teams tackling complex large-scale real-world tasks. Unmanned Aerial Vehicles (UAVs) offer rapid environmental coverage, but flight time is often limited by energy constraints, whereas Unmanned Ground Vehicles (UGVs) have greater energy capacity to support long-duration missions, but movement is constrained by traversable terrain. Individually, neither can complete tasks such as environmental monitoring. Effective UAV-UGV collaboration therefore requires energy-constrained multi-UAV task planning, traversability-constrained multi-UGV path planning, and crucially, synchronized concurrent co-planning to ensure timely in-mission recharging. To enable these capabilities, we propose Collaborative Planning with Concurrent Synchronization (CoPCS), a learning-based approach that integrates a heterogeneous graph transformer for operationally constrained task encoding with a transformer decoder for joint, synchronized co-planning that enables UAVs and UGVs to act concurrently in a coordinated manner. CoPCS is trained end-to-end under a unified imitation learning paradigm. We conducted extensive experiments to evaluate CoPCS in both robotic simulations and physical robot teams. Experimental results demonstrate that our method provides the novel multi-robot capability of synchronized concurrent co-planning and substantially improves team performance. More details of this work are available on the project website: https://hcrlab.gitlab.io/project/CoPCS.

Zihao Deng, Qianhuang Li, Peng Gao +4

Zihao Deng, Qianhuang Li, Donghyun Kim, and Hao Zhang are with the University of Massachusetts Amherst, Amherst, MA, 01002, USA. · Peng Gao is with North Carolina State University, Raleigh, NC, 27695, USA. · Maggie Wigness and John Rogers are with the U.S. Army DEVCOM Army Research Laboratory (ARL), Adelphi, MD, 20783, USA.

Multi-Robot Coordination Multi-Robot Motion Planning Multi-Robot Task Allocation

Mar 6, 2026

Multi-Robot Trajectory Planning via Constrained Bayesian Optimization and Local Cost Map Learning with STL-Based Conflict Resolution

We address multi-robot motion planning under Signal Temporal Logic (STL) specifications with kinodynamic constraints. Exact approaches face scalability bottlenecks and limited adaptability, while conventional sampling-based methods require excessive samples to construct optimal trajectories. We propose a two-stage framework integrating sampling-based online learning with formal STL reasoning. At the single-robot level, our constrained Bayesian Optimization-based Tree search (cBOT) planner uses a Gaussian process as a surrogate model to learn local cost maps and feasibility constraints, generating shorter collision-free trajectories with fewer samples. At the multi-robot level, our STL-enhanced Kinodynamic Conflict-Based Search (STL-KCBS) algorithm incorporates STL monitoring into conflict detection and resolution, ensuring specification satisfaction while maintaining scalability and probabilistic completeness. Benchmarking demonstrates improved trajectory efficiency and safety over existing methods. Real-world experiments with autonomous surface vehicles validate robustness and practical applicability in uncertain environments. The STLcBOT Planner will be released as an open-source package, and videos of real-world and simulated experiments are available at https://stlbot.github.io/.

Sourav Raxit, Abdullah Al Redwan Newaz, Jose Fuentes +3

Department of Computer Science, University of New Orleans, New Orleans, LA 70148, USA · Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USA · Providence College, Department of Mathematics & Computer Science, Providence, RI 02918, USA

Multi-Robot Motion Planning Constrained Motion Planning Kinodynamic Motion Planning Conflict-Based Search Bayesian Optimization

Mar 4, 2026

GIANT - Global Path Integration and Attentive Graph Networks for Multi-Agent Trajectory Planning

This paper presents a novel approach to multi-robot collision avoidance that integrates global path planning with local navigation strategies, utilizing attentive graph neural networks to manage dynamic interactions among agents. We introduce a local navigation model that leverages pre-planned global paths, allowing robots to adhere to optimal routes while dynamically adjusting to environmental changes. The models robustness is enhanced through the introduction of noise during training, resulting in superior performance in complex, dynamic environments. Our approach is evaluated against established baselines, including NH-ORCA, DRL-NAV, and GA3C-CADRL, across various structurally diverse simulated scenarios. The results demonstrate that our model achieves consistently higher success rates, lower collision rates, and more efficient navigation, particularly in challenging scenarios where baseline models struggle. This work offers an advancement in multi-robot navigation, with implications for robust performance in complex, dynamic environments with varying degrees of complexity, such as those encountered in logistics, where adaptability is essential for accommodating unforeseen obstacles and unpredictable changes.

Jonas le Fevre Sejersen, Toyotaro Suzumura, Erdal Kayacan

Artificial Intelligence in Robotics Laboratory (AiR Lab), Department of Electrical and Computer Engineering, Aarhus University, 8000 Aarhus C, Denmark · Foundation Models for Artificial Intelligence group, Department of Information and Communication Engineering, Tokyo University, Tokyo, Japan · Automatic Control Group, Department of Electrical Engineering and Information Technology, Paderborn University, Paderborn, Germany

Multi-Robot Motion Planning Robot Collision Avoidance Dynamic Obstacle Avoidance

Mar 3, 2026

SPARC: Spatial-Aware Path Planning via Attentive Agent Communication

Efficient communication is critical for decentralized Multi-Robot Path Planning (MRPP), yet existing learned communication methods treat all neighboring robots equally regardless of their spatial proximity, leading to diluted attention in congested regions where coordination matters most. We propose Relation enhanced Multi Head Attention (RMHA), a communication mechanism that explicitly embeds pairwise Manhattan distances into the attention weight computation, enabling each robot to dynamically prioritize messages from spatially relevant neighbors. Combined with a distance-constrained attention mask and GRU gated message fusion, RMHA integrates seamlessly with MAPPO for stable end-to-end training. In zero-shot generalization from 8 training robots to 128 test robots on 40x40 grids, RMHA achieves approximately 75 percent success rate at 30 percent obstacle density outperforming the best baseline by over 25 percentage points. Ablation studies confirm that distance-relation encoding is the key contributor to success rate improvement in high-density environments. Index Terms-Multi-robot path planning, graph attention mechanism, multi-head attention, communication optimization, cooperative decision-making

Sayang Mu, Xiangyu Wu, Bo An

Nanyang Technological University Singapore

Multi-Robot Motion Planning Multi-Agent Path Finding Multi-Agent Communication Multi-Agent Reinforcement Learning