The proposed platform supports virtual evaluation of robotic operations prior to deployment and provides a foundation for BIM-informed robotic systems in facility management and graph-theoretic algorithms to compute efficient and collision-free navigation paths while avoiding obstacles.
Abstract
Indoor robots are increasingly employed for facility management tasks such as cleaning and inspection. These applications primarily rely on navigation and can be effectively supported by predefined routes or perception-driven Simultaneous Localization and Mapping (SLAM) techniques. However, more complex tasks, such as locating and repairing leaking pipes, require not only navigation but also access to building information, including the location, geometry, material, and operational attributes of components. Existing navigation approaches provide only limited environmental understanding and cannot readily supply such information. In contrast, Building Information Modeling (BIM) contains rich geometric, semantic, and operational information that remains largely underutilized in robotic applications. This study proposes a BIM-enabled, agent-based simulation platform for knowledge-driven indoor robot navigation and operation planning. Within the framework, indoor environments are discretized into grid cells that are mapped to graph nodes and classified as target, obstacle, or regular nodes according to their spatial relationships with building elements. Traversal costs are assigned to edges connecting neighboring nodes, enabling graph-theoretic algorithms to compute efficient and collision-free navigation paths while avoiding obstacles. Simulation results demonstrate that the proposed graph representation enables efficient and collision-free navigation. A key limitation associated with coarse discretization, namely overlap between target-occupied and obstacle-occupied cells, is identified and mitigated through grid refinement, improving spatial accuracy and path feasibility. The proposed platform supports virtual evaluation of robotic operations prior to deployment and provides a foundation for BIM-informed robotic systems in facility management.
Construction sites are beginning to deploy material transport robots to relieve labor constraints; however, planning and validating robot operations within complex, evolving building layouts remain difficult. This study proposes a BIM-enabled simulation framework that connects building information to enable an objective pre-deployment evaluation. The method extracts navigable spaces, delivery points and material quantities from BIM, formulates dispatching as a capacity-constrained vehicle routing problem, and couples it with an A* algorithm for pathfinding. A greedy next-stop decision rule with a tunable correction factor governs whether the robot continues visiting additional stops or returns to the staging area. The experimental results show a more than 20% improvement in the travel efficiency of the robot compared with conventional algorithms. These results conclude that the framework offers a practical tool for what-if analysis and data-driven planning of robotized construction logistics and is readily extensible to diverse materials and project types.
Tae Hun Hong, Jaeho Cho, Jin Gang Lee· Buildings· 0 citations
The study argues that scene perception and task semantic understanding constitute a continuous intelligent chain from "environment recognition" to "task execution" so as to enhance their practical performance and operational reliability.
Haoxiang Huang· Applied and Computational En...· 0 citations
Mobile robots operating in human-populated environments must navigate complex, multi-room spaces while ensuring safety, i.e., generating collision-free motion. In this study, we present a sensor-based model predictive control (MPC) scheme designed for safe crowd navigation in such non-convex environments. The proposed framework decomposes the free space into a set of overlapping convex regions to construct a topological graph, enabling a high-level planner to compute optimal sequences of traversable areas. To effectively perceive the crowd, the system employs a robust perception pipeline that fuses 2D LiDAR data with semantic information from an RGB-D camera, utilizing Kalman filters (KFs) to estimate and predict human motion. These predictions are integrated into an MPC controller which generates robot commands by enforcing safety through discrete-time control barrier function (DT-CBF), ensuring that the robot avoids collisions while remaining within navigable regions. The approach is validated through high-fidelity simulations and real-world experiments using the TIAGo mobile manipulator. The results demonstrate that integrating vision-based semantic data with geometric constraints significantly improves collision avoidance and success rates in cluttered, multi-room scenarios.
Giovanbattista Gravina, Francesco D'Orazio, Michele Cipriano et al.· Frontiers in Robotics and AI· 0 citations
Path planning is a core technology for enabling inspection robots to operate safely and efficiently in complex industrial spaces, including smart manufacturing sites where sensor reliability, wireless communication continuity, and electromagnetic interference may affect autonomous navigation. Although the classical A-star algorithm is widely used in global path planning because of its completeness and optimality guarantees, it often suffers from computational inefficiency, excessive node expansion, and non-smooth trajectories that are unsuitable for real robot motion. To address these limitations, this work proposes an enhanced A-star algorithm for inspection robots. The heuristic function is redesigned with an adaptive weighting mechanism, balancing search speed and path optimality and reducing the number of expanded nodes. A safety-aware strategy is further introduced by adding virtual safety buffers around obstacles, allowing the robot to maintain a minimum safe distance during navigation. Finally, Bézier curve interpolation is used to improve path smoothness and kinematic feasibility by generating continuous and differentiable trajectories. Simulation experiments show that, compared with the standard A-star algorithm, the proposed method reduces searched nodes by nearly 50% and redundant turns by more than 60%. Overall inspection efficiency is improved by up to 90%, confirming the method’s practicality for robot navigation in complex engineering environments.
Cheng-Wei Li, Yuan Zhou, Shoubin Wang et al.· Advanced Electromagnetics· 0 citations
: This paper provides a thorough survey and integrative presentation of cooperative path planning for multi-robot systems operating in dynamic, cluttered, and partially observable environments. People synthesise algorithmic foundations ranging from heuristic graph search to sampling-based motion planners, including A*, D* Lite, and Safe Interval Path Planning for discrete/time-augmented spaces, as well as RRT, RRT*, and Informed RRT* for continuous configuration spaces. Multi-agent coordination techniques are reviewed, covering reciprocal collision avoidance (ORCA) and centralised Multi-Agent Path Finding (MAPF) solvers such as Conflict-Based Search (CBS) and bounded-suboptimal variants (ECBS). The paper also examine control and safety layers like Model Predictive Control and Control Barrier Functions that translate plans into dynamically feasible commands with safety guarantees. Recent progress in cooperative multi-agent reinforcement learning (MAPPO, QMIX, MADDPG) is evaluated for adaptability under partial observability and nonstationary environments. Applications in warehousing, intelligent transportation, and disaster response are used to illustrate practical trade-offs and integration patterns, referencing real-world systems such as Kiva-style warehouse fleets and autonomous driving pipelines. The paper concludes with a focused discussion on open challenges — scalability with guarantees, safety under uncertainty, sim-to-real transfer, and planning – control interface fragility — and proposes research directions including learning-augmented heuristics, unified safety-aware planning, adaptive MPC – CBF filters, and more informative benchmarks to drive reproducible progress.
Yun Pan· Proceedings of the 3rd Inter...· 0 citations
This paper presents the integration and evaluation of an autonomous navigation system for a four-wheel steering (4WS) agricultural robot operating in opposite-phase steering mode within simulated blueberry plantation environments. The system combines opposite-phase steering kinematic modeling with wheel-based odometry fused via an Extended Kalman Filter, graph-based SLAM for environment mapping, Adaptive Monte Carlo Localization for pose estimation, Hybrid-A* path planning for kinematically-constrained trajectory generation, and Model Predictive Path Integral control for local motion execution, all orchestrated through a behavior tree framework within the ROS 2 Navigation Stack (Nav2). Three navigation tasks: perimeter traversal, inter-row serpentine coverage, and an extended multi-region circuit were conducted in a Gazebo-based simulation replicating blueberry row-crop geometry. The system completed all navigation goals without triggering recovery behaviors, validating the feasibility of the Nav2 framework for structured agricultural navigation.
Andrés Alamo, Ricardo Jesus Huaman Kemper, S. P. Prado Gardini· 2026 23rd International Conf...· 0 citations
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