Robotic Path Planning Algorithms

Últimos artículos

• From Language Priors to Field Adaptation: Preference Learning for Traversability Estimation

  Simon Schwaiger, David Seyser, Alessandro Scherl, Zlatan Ajanovi\'c, Wilfried W\"ober, Gerald Steinbauer-Wagner · 5 de octubre de 2026

  Image-based traversability estimation is inherently dependent on the robot platform, deployment domain, and mission preferences, which limits the applicability of purpose-trained models. To facilitate domain adaptation, this work aims to reduce the number of required annotations in the target domain…

• Optimal Planning in a Dynamic World

  Devin Wild Thomas (University of New Hampshire, USA), Solomon Eyal Shimony (Ben-Gurion University of the Negev, Israel), Wheeler Ruml (University of New Hampshire, USA), Erez Karpas (Technion - Israel Institute of Technology, Israel), Shahaf S. Shperberg (Ben-Gurion University of the Negev, Israel), Andrew Coles (King's College London, UK) · 5 de octubre de 2026

  Background: We address the problem of planning when the set of feasible states or actions changes over time. For example, in the problem of path planning among moving obstacles (sometimes known as SIPP), the feasibility of being at a particular location can change as the obstacles move. Or, the acti…

• Belief-Aware Multi-Agent Path Finding under Map Uncertainty

  Viraj Parimi, Shao-Hung Chan, Han Zhang, Jingkai Chen, Brian Williams · 1 de octubre de 2026

  Multi-Agent Path Finding (MAPF) aims to find collision-free paths for multiple agents in a shared environment. Classical MAPF assumes that all static obstacles are known in advance, but real-world environments can change unexpectedly due to fallen objects, spills, or other local disturbances. When s…

• Divide and Collapse: MAPF-Collapse via Exact Decomposition into Independent Sub-Instances

  Oren Salzman · 1 de octubre de 2026

  In this work we study the problem of MAPFC, a post-optimization step for Multi-Agent Path Finding (MAPF) plans where we are given a feasible plan produced by a modern MAPF solver and are tasked with removing avoidable moves while preserving feasibility. This NP-hard problem naturally arises when usi…

• Beyond the Remembered World: Predictive 4D Belief for Persistent Navigation in Evolving Worlds

  Mingjian Gao, Zhaocheng Li, Haoyang Huang, Wenqiao Zhang, Yingjie Niu, Hao Zhou, Chao Li, Juncheng Li, Siliang Tang, Yueting Zhuang · 1 de octubre de 2026

  Persistent spatial memory enables embodied agents to navigate familiar environments across repeated visits. However, targets may move while unobserved, including during navigation, making remembered locations unreliable by the time an agent arrives. Despite advances in memory retrieval and state pre…

• From Semantic Decisions to Feasible Trajectories: Self-Evolving LLM-Guided Optimal Control for Narrow-Space Parking

  Zhengbao Yao, Yuanfu Luo, Kehan Xue · 30 de septiembre de 2026

  Autonomous parking in nonconvex and narrow environments remains challenging. Although optimal-control methods can explicitly enforce vehicle dynamics and collision constraints, nonconvexity compromises solver robustness and can cause failures. Large language models (LLMs) exhibit strong semantic rea…

• Semantic Map Sharing and Capability-Aware Coverage Planning for AI-Native 6G Robotic Coordination

  Abdulqader Dhafer, Qi Wang, Zhou Daniel Hao · 30 de septiembre de 2026

  Search and Rescue (SAR) operations increasingly deploy heterogeneous teams of aerial and ground robots. However, conventional coverage methods typically do not translate perceived terrain into platform-specific reachability, while continuous image exchange imposes a high communication cost. We propo…

• Systematic Multi-Agent Vision-and-Language Navigation: Formulation, Benchmark, and Method

  Yunzhe Xu, Zhe Liu · 30 de septiembre de 2026

  Vision-and-Language Navigation (VLN) has largely focused on a single agent following a single instruction, yet many real-world applications require teams of robots to tackle tasks beyond the capabilities of any individual agent. We present Systematic Multi-Agent Vision-and-Language Navigation, provi…

• HorizonFlow: Variable-Length Planning for Offline Goal-Conditioned RL

  JunHyeok Oh, Zian Jang, Byung-Jun Lee · 30 de septiembre de 2026

  Recent advances in generative planning have made trajectory inpainting a promising approach to offline goal-conditioned reinforcement learning. However, these methods typically specify the planning horizon before generating plan content, even though the appropriate horizon depends on the route itsel…

• Optimizing H-Graph Hybridization for Diffusion-Guided RRT

  Omer Talmi · 29 de septiembre de 2026

  Sampling-based motion planners guided by diffusion models produce high-quality trajectories in a single run, yet the stochastic diversity available at inference time is left largely unexploited. We present two inference-time diversification strategies for a fixed, pretrained DiTree model, combined v…

• RECAST: Recasting Vision-Language Semantics into an Actionable Cost Map for Robot Navigation

  Incheol Cho, Jintae Park, Jinkyu Kim, Jungbeom Lee, Jaegul Choo, Seokha Moon · 29 de septiembre de 2026

  Safe and robust robot navigation across diverse environments requires a high-level understanding of complex scenes and the ability to carry it into stable motion. Recent works tackle this with learning-based models trained at scale and with approaches built on vision-language models (VLMs). However,…

• Affordance-Conditioned Decision Making: Bridging the Semantic-Spatial Gap in Zero-Shot Cross-Floor Vision-and-Language Navigation

  Xuekang Yang, Lu Chen, Shuang Luo, Jialing Zhu, Qi Zhang, Yue Gao, Xiang Zhang · 29 de septiembre de 2026

  Vision-and-language navigation increasingly relies on general-purpose semantic planners, yet translating correct high-level intent into reliable physical execution remains difficult in spatially constrained transitions. Reaching a staircase, doorway, or narrow passage does not ensure traversal; the …

• Calibrated Uncertainty for Informative Path Planning in Aquatic Environmental Monitoring

  Samuel Yanes Luis, Alejandro Casado P\'erez, Alejandro Mendoza Barrionuevo, Dame Seck Diop, Sergio Toral Mar\'in, Saniel Guti\'errez Reina · 29 de septiembre de 2026

  Informative Path Planning for scalar field reconstruction uses predictive uncertainty to direct sensing vehicles toward maximally informative locations. Gaussian Processes provide this signal but their stationary isotropic kernels are misspecified for non-homogeneous phenomena such as oil spills, pr…

• Decentralized Master-Mind: Joint Action Refinement through Iterative Intent Denoising in Multi-Agent Pathfinding

  Valeriy Vyaltsev, Anton Andreychuk, Taisia Zlotnikova, Konstantin Yakovlev, Aleksandr Panov, Alexey Skrynnik · 29 de septiembre de 2026

  Decentralized multi-agent path finding (MAPF) with communication requires agents to reach individual goals without collisions under partial observability. Learnable policies trained on expert data provide an effective approach to this problem. However, when several coordinated joint actions are vali…

• LEAP-CBF: A Safety Filter for Uncertain Systems with Least-Effort Adversarial Potentials

  Oswin So, Eric Yu, Chuchu Fan · 24 de septiembre de 2026

  Control barrier functions (CBF) are a popular safety filter to ensure safety for nonlinear dynamical systems. However, when the system is subject to uncertainties and disturbances, this requires the use of robust variants of CBFs, which can be difficult to construct and can be overly conservative, e…

• Real-time autonomous magnetic microrobot navigation across dynamic and biologically relevant environments

  Yanda Yang, Max Sokolich, Fatma Ceren Kirmizitas, Baylen Ravenscraft, Sambeeta Das, Andreas A. Malikopoulos · 23 de septiembre de 2026

  Autonomous microrobots could enable minimally invasive interventions in confined biological environments, but their operation requires real-time navigation among moving obstacles and environmental disturbances. Here we present a closed-loop framework for autonomous magnetic microrobot navigation tha…

• Skytopia: Monocular Drone Navigation with Action-Conditioned Latent World Models

  Yuhang Zhang, Rangya Zhang, Yujing Shang, Zhuoyuan Yu, Weiying Wang, Steven Yang, Qingsong Yan, Chao Yan, Mir Feroskhan · 23 de septiembre de 2026

  Monocular drone navigation requires reaching a goal in an unseen environment from a single forward-facing camera, which offers few cues for depth and scale. World models address this by modelling how observations evolve under actions, but they are built to be executed: the prediction is produced at …

• Towards Omni-dimensional GUI Agent Navigation with Masked Trajectory Prediction

  Yan Zhang, Pei Fu, Daiqing Wu, Huawen Shen, Ruoceng Zhang, Shaojie Zhang, Jiahui Yang, Yu Zhou, Can Ma, Zhenbo Luo, Jian Luan · 23 de septiembre de 2026

  Graphical User Interface (GUI) Agents autonomously interact with software to fulfill user requests, where GUI navigation stands out as the most critical and challenging capability. Mastering this capability demands a complex synergy of step-wise decision-making, state-action alignment, and long-hori…

• Visual Navigation Transformer with Pose Attention

  Beiming Li, Jaime Romero, Jonathan Diller, Vijay Kumar, Alejandro Ribeiro · 21 de septiembre de 2026

  Learned navigation policies typically consume observations as a temporally ordered history, with positional encodings tying each observation to when it was seen, making it difficult to reuse experience from earlier traversals of an environment. Systems that do reuse such experience usually construct…

• DynaWeightPnP: Toward global real-time 3D-2D solver in PnP without correspondences

  Jingwei Song, Maani Ghaffari · 18 de septiembre de 2026

  This paper addresses a special Perspective-n-Point (PnP) problem: estimating the optimal pose to align 3D and 2D shapes in real-time without correspondences, termed as correspondence-free PnP. While several studies have focused on 3D and 2D shape registration, achieving both real-time and accurate p…

• Navi-Agent: Unlocalized Monocular Navigation Agent

  Wenyuan Xie, Mengyang Hong, Yongzhong Wang, Yanbiao Ji, Yijin Zhou, Shaokai Wu, Shalayiding Sirejiding, Huayi Zhou, Yi-Chao Chen, Ma Ling, Yue Ding, Hongtao Lu · 18 de septiembre de 2026

  Vision-Language Navigation in Continuous Environments (VLN-CE) requires an embodied agent to execute long-horizon instructions in unknown environments. Existing zero-shot VLN-CE systems typically maintain spatial states through geometric localization or coordinate-based representations. Recent geome…

• Feeling Terrain Before Crossing: World Models for Off-Road Navigation

  E-In Son, Dong-Wook Kim, Ji-Hoon Hwang, Kangsun Lee, Jisung Bae, Jung-Taak Kim, Seung-Woo Seo · 18 de septiembre de 2026

  Navigation world models plan by foresight, predicting the future that each candidate action sequence produces and selecting the best, rather than mapping observations to actions directly. Unlike urban settings where a predicted scene is a sufficient proxy, off-road navigation hinges on the robot--te…

• Customizable and Jointly Optimized Route Planning: A Deep Architecture Enabling Differentiable Shortest-Path Search

  Rui Zhao, Chao Chen, Longfei Xu, Chenguang Ji, Hengbin Cui, Kaikui Liu, Xiaolong Li · 18 de septiembre de 2026

  With the widespread use of online navigation and ride-hailing services, achieving optimal route planning for diverse user preferences has recently attracted increasing attention. Classic graph algorithms for pathfinding use heuristic cost functions to define edge weight, thus providing no optimality…

• Continual Learning for Traversability Prediction with Uncertainty-Aware Adaptation

  Hojin Lee, Yunho Lee, Daniel A Duecker, Cheolhyeon Kwon · 16 de septiembre de 2026

  Traversability prediction is a critical component of autonomous navigation in unstructured environments, where complex and uncertain robot-terrain interactions pose significant challenges such as traction loss and dynamic instability. Despite recent progress in learning-based traversability predicti…

• Seeing What Matters: Visual Cue Guided Video Planning for Generalizable Robot Navigation

  Hojin Lee, Sizhe Lester Li, Maximilian Hilger, Susie Lu, Achim J. Lilienthal, Vincent Sitzmann, Daniel A. Duecker · 16 de septiembre de 2026

  Generative video models can serve as a promising backbone for robot navigation by predicting future observations as video plans. Recent approaches often condition video planning on short-horizon guidance and recover geometric waypoints through scene reconstruction, leaving longer-horizon planning an…