Jul 2026· IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications· pp. 57-62· 0 citations· 24 references
Computer Science
TL;DR
ZONDA, a zero-shot object navigation with dynamic avoidance framework, integrates three core components and can maintain robust navigation on the dynamic benchmark HM3D-DYNA compared to the existing baseline.
Abstract
In Object Goal Navigation task, existing methods are typically restricted to static and single-floor environments, ignoring cross-floor topologies and dynamic pedestrian, which limits their real-world deployment. To address these limitations, we propose ZONDA, a zero-shot object navigation with dynamic avoidance framework. In particular, ZONDA integrates three core components: (i) Heuristic multi-floor planning: from height-difference traversable maps, enables stair traversal and cross-floor exploration without a platform-specific learned controller; (ii) Multi-view target verification: cross-checks multiscale observations with a vision-language model, significantly reducing false positives; and (iii) Dynamic pedestrian avoidance: explicitly tracks and predicts moving pedestrians to generate anticipatory behaviors. Evaluated on a real Direct Drive Tech TITA biped robot and extensive simulations on HM3D and MP3D, ZONDA achieves significantly improved results. Moreover, ZONDA can maintain robust navigation on the dynamic benchmark HM3D-DYNA compared to the existing baseline.
The proposed DevGRU navigation system employs an action predictor that generates collision-aware future trajectories, enabling effective avoidance of immediate obstacles and has a relatively small number of trainable parameters, resulting in the fastest inference time among the baselines.
Kyung Min Han, Eunsom Kim, Young J. Kim· IEEE Robotics and Automation...· 0 citations
Collision avoidance for industrial AGVs operating in dynamic, shared environments remains challenging because reactive local planners treat moving obstacles as static, leading to conservative or unsafe behavior. We address this by enabling the local planner to reason over predicted obstacle motion rather than instantaneous positions. Specifically, an Ensemble Kalman Filter (EnKF)-based multi-object tracker provides filtered position and velocity estimates that a modified Dynamic Window Approach (DWA) uses to evaluate candidate trajectories against projected future obstacle states. A radius-informed ensemble generation scheme adapts filter uncertainty to the observed object size from 2D LiDAR, and a geometric-center representation provides stable bounding estimates under partial occlusion. The system is implemented as three modular, pipelined ROS 2 nodes. Experiments in Gazebo, Stage (up to five concurrent robots), and on a real industrial forklift demonstrate collision-free navigation in same-direction, crossing, and head-on scenarios with sub-10 ms controller response times.
Bruk Gebregziabher, Hadush Hailu· 2026 IEEE International Conf...· 0 citations
Mobile robots typically rely on geometric maps for obstacle avoidance and path planning, but the resulting obstacle representation does not always match how an object should affect navigation. A low lying cable may be missed, a flexible curtain may create spurious blockage, and a traffic cone may require an exclusion region larger than its observed footprint. We present NavPatch, an object level correction layer that assigns ADD, REMOVE, or EXTEND to navigation relevant object categories through periodic scene understanding with a vision-language model. Open vocabulary grounding localizes object instances, and LiDAR and RGB-D observations provide 3D support. Observation quality filtering and cross frame maintenance determine when each correction patch is committed, replaced, or revoked. In 50 real robot trials across five layouts, NavPatch achieves an overall success rate of 86.0%. An ablation study of four configurations with 200 runs in total shows that NavPatch improves the success rate from 70.0% to 86.0% and reduces the false commit rate from 68.4% to 40.7% compared with updates based only on the current observation.
Shi-Jie Sun, Xing-Yu Tao, Hao Wang et al.· 0 citations
Long-term navigation for service robots faces crit- ical challenges like the accumulation of odometry drift and sensor error, which progressively degrade 2D maps and renders traditional path planning algorithms (e.g., A*, RRT*, DiPPer, ViT-A*) ineffective over time. To address this, we propose a user-friendly, interactive framework that eliminates the reliance on globally consistent maps. Our approach integrates visual perception with Large Language Models (LLM) to interpret user commands via text or voice. Instead of relying on a drift- prone global map, the system generates a sequential action plan based on local visual cues and egocentric geometric instructions. These action plans are executed sequentially, allowing the robot to navigate known and unknown environments safely. By reset- ting localization relative to immediate targets, our framework effectively works with a minimum accumulation drift strategy, ensuring accurate, efficient, and collision-free navigation without the maintenance overhead of traditional mapping. Experiments on real-world and simulated data have shown significant improve- ments over other methods. Our source code is publicly accessible at https://github.com/PraveenSingh24/VL-Navigation.
Praveen Kumar, K. Guruprasad, Tushar Sandhan· 0 citations
Object-goal navigation has made substantial progress in semantic perception and exploration, yet persistent memory for multi-object navigation and cross-floor navigation are still commonly addressed separately. We present LifelongCrossNav, a framework for sequential multi-object ObjectNav in unknown multi-floor indoor environments. Within each episode, the agent receives an ordered sequence of object-goal queries while continuously maintaining a shared sparse 3D semantic voxel memory. This memory incrementally accumulates geometric structure, traversability states, and vision-language features, allowing subsequent object-goal queries to retrieve previously acquired scene information without rebuilding the map. To support persistent search across floors, LifelongCrossNav combines support-aware 3D traversability mapping, stair-specific perception, and direction-aware stair traversal. A unified navigation policy coordinates same-floor frontier exploration, live and historical point-of-interest retrieval, stair navigation, and target-object search and approach. We further introduce HM3D-MFMON, a benchmark for sequential Multi-Floor Multi-Object Navigation built on HM3D scenes, including a dedicated subset in which completing the full sequence of object-goal subtasks requires at least one floor transition. Experimental results show that LifelongCrossNav consistently outperforms a representative planar persistent semantic-map baseline on HM3D-MFMON, demonstrating that persistent 3D semantic memory and cross-floor traversability modeling effectively support sequential multi-object navigation in multi-floor environments. Project page: https://flageval-baai.github.io/LifelongCrossNavPage.
Zehui Li, Zihao Sun, Jia-Wei Xu et al.· 0 citations
Autonomous navigation in unknown, complex indoor environments remains challenging due to limited sensing range and severe partial observability. Conventional methods rely on local maps without foresight, causing dead-ends and long detours, while local goal selection based on Euclidean distance or frontier coverage fails to balance efficiency with directionality. To address these challenges, we propose ForexNav, a foresight-enabled exploratory navigation framework. To handle structural ambiguity in unseen regions, we introduce Foresight Hypothesis Fusion (FHF), which maintains multiple WGAN-based map predictions and reweights them via temporal evidence accumulation. A Traversability-aware A* search then quantifies predictive traversability on the fused map, enabling a multi-objective planner to synthesize path feasibility, kinodynamic conformity, monotonic-progress consistency, and geometric distance for optimal intermediate goal selection and dynamically consistent trajectory generation. Experiments in four simulated indoor scenes of up to 3,300 m<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> demonstrate navigation success while reducing total travel time by 25.0% and improving average velocity by 13.3% over the strongest baseline, with path ratio improvements of 22.2% on average in large-scale environments (<inline-formula><tex-math notation="LaTeX">$\geq$</tex-math></inline-formula>2,000 m<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>). Real-world deployment on a quadruped robot supports practical feasibility, and extension to a fixed-altitude micro-UAV further suggests preliminary cross-platform transferability.
Hong-Yu Song, Yun-Fang Ren, Ji-Gui Miao et al.· IEEE Robotics and Automation...· 0 citations
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