Track A*: Fast Visibility-Aware Trajectory Planning for Active Target Tracking

• URL: http://arxiv.org/abs/2605.05338v1
• Date: Wed, 06 May 2026 18:09:15 GMT
• Title: Track A*: Fast Visibility-Aware Trajectory Planning for Active Target Tracking
• Abstract summary: We present Track A star (TA star), an offline search-based trajectory planner for visibility-aware target tracking.<n>TA star combines a layered Acyclic Graph (D) search with three engineering optimizations: cross-time obstacle distance caching against a Bounding Volume, per-layer beam pruning, and a multi-ray visibility evaluator.<n>Our empirical results demonstrate robust, near-baseline visibility performance at a fraction of the computational cost.
• Score: 1.8297494098768168
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Offline reference trajectories for active target tracking are needed both for building multi-modal tracking datasets and for benchmarking online tracking planners under repeatable conditions. We present Track A star (TA star), an offline search-based trajectory planner that targets the visibility-aware target tracking objective on a discretized four-dimensional spatio-temporal grid (x, y, z, t). TA star combines a layered Directed Acyclic Graph (DAG) search with three engineering optimizations: cross-time obstacle distance caching against a Bounding Volume Hierarchy (BVH), per-layer beam pruning, and a configurable multi-ray visibility evaluator. TA star employs a beam-pruned heuristic search on this discrete graph to efficiently find high-quality tracking trajectories. While it trades strict theoretical optimality for practical scalability, our empirical results demonstrate robust, near-baseline visibility performance at a fraction of the computational cost. On a 1000-scenario stress test across eight CARLA Optimized maps, TA star converges on all scenarios and completes in 45 s using 32 workers; on a 248-scenario controlled comparison against an unoptimized priority-queue A star baseline (BinaryHeap implementation) under identical scenario inputs and a 5 x 10^6 expansion cap, TA star reduces mean planning time by 23.0x and worst-case planning time by 11.8x, while raising convergence from 56.9% to 100%. On the n=141 baseline-converged subset, TA star changes average visibility by only -0.15 percentage points (pp), with no scenario exceeding a 5 pp drop. We position TA star as a practical offline reference planner under these specific conditions, with limitations and failure cases discussed for environments such as Town07 dense vegetation.

Related papers

• TRACE: Ergodic Trajectory Optimization for Active Scene Reconstruction [7.636751274394083]
  We study active reconstruction as an ergodic coverage problem.<n>Our approach derives this target distribution online from uncertainty and visibility.<n>We evaluate TRACE on the Replica dataset against the Next-Best-View (NBV) baselines, improving PSNR by 1.5 dB.
  arXiv  Detail & Related papers  (2026-08-03T14:30:20Z)
• From Distances to Trajectories: Real-Time Signed Distance Function Mapping and Distance-Accelerated Motion Planning for UAVs [8.393016378205752]
  We argue that mapping and planning should be co-designed around a single representation: a signed distance function (SDF)<n>In tandem, we develop Bubble$star$, a search-based planner that exploits the distance information to grow maximal collision-free balls.<n>We demonstrate the integrated OREN-Bubble$star$ approach onboard a quadrotor, navigating unseen indoor environments in real time under tight compute constraints.
  arXiv  Detail & Related papers  (2026-07-21T17:18:46Z)
• DRIFT: Drift and Aggregation for Motion Planning [20.309189828710362]
  DRIFT is a fixed-depth trajectory planner that combines one-step drifting in a compact trajectory latent space with scene-aware proposal aggregation.<n>A lightweight Aggregation Head integrates these features with scene, navigation, and ego-state information and directly predicts the final trajectory.<n>Its output is trained with expert-trajectory imitation and a map-derived boundary regularizer that penalizes waypoints outside the drivable polygon.
  arXiv  Detail & Related papers  (2026-07-16T02:43:13Z)
• LocalNav: Distilling Frontier VLMs and Embodied RL for On-Device Object Goal Navigation [53.88286427894783]
  Vision Language Models (VLMs) have emerged in the robotic domain as a powerful tool that enables environmental perception with language context.<n>Yet, their computational footprint typically confines them to cloud execution, hindering low-latency inference with local deployment on resource-constrained robots.<n>We present a distillation strategy that transfers complex spatial-semantic reasoning from large frontier models into a lightweight, 4B- parameter local VLM for edge execution on embedded GPU devices.
  arXiv  Detail & Related papers  (2026-06-26T09:11:59Z)
• CosFly-Track: A Large-Scale Multi-Modal Dataset for UAV Visual Tracking via Multi-Constraint Trajectory Optimization [5.936588226093352]
  We introduce CosFlyTrack, a large-scale multi-modal dataset for UAV visual tracking in urban environments.<n>The dataset provides approximately 12,000 expert and perturbed UAV trajectories generated from 6,000 pedestrian paths.<n>CosFlyTrack improves tracking performance to 78.3 to 95.6% SR@1 meter, a 53 to 69 percentage point gain over zero-shot baselines.
  arXiv  Detail & Related papers  (2026-05-18T02:49:58Z)
• Instance-level Visual Active Tracking with Occlusion-Aware Planning [61.982298426203165]
  Visual Active Tracking (VAT) aims to control cameras to follow a target in 3D space.<n>VAT faces two key bottlenecks in real-world deployment: confusion from visually similar distractors and severe failure under occlusions.<n>We propose OA-VAT, a unified pipeline with three complementary modules.
  arXiv  Detail & Related papers  (2026-04-23T09:11:50Z)
• UAV-Track VLA: Embodied Aerial Tracking via Vision-Language-Action Models [14.433739259355406]
  Embodied visual tracking is crucial for Unmanned Aerial Vehicles (UAVs) executing complex real-world tasks.<n>To benchmark multimodal tracking in such environments, we construct a large-scale dataset encompassing over 890K frames, 176 tasks, and 85 diverse objects.<n>We propose an improved VLA tracking model, UAV-Track VLA, built upon the $_0.5$ architecture.
  arXiv  Detail & Related papers  (2026-04-02T16:33:38Z)
• Critic-Free Deep Reinforcement Learning for Maritime Coverage Path Planning on Irregular Hexagonal Grids [1.2891210250935148]
  Maritime surveillance missions rely on the efficient allocation of sensing assets over vast and geometrically complex areas.<n>Traditional Coverage Path Planning approaches struggle with irregular coastlines, islands, and exclusion zones.<n>We propose a Deep Reinforcement Learning framework to solve CPP on hexagonal grid representations of irregular maritime areas.
  arXiv  Detail & Related papers  (2026-03-30T12:56:38Z)
• Amortizing Trajectory Diffusion with Keyed Drift Fields [2.5352713493505785]
  Keyed Drifting Policies is a one-step trajectory generator trained with a drift-field objective.<n>It repels generated trajectories toward condition-matched dataset windows.<n>It achieves strong performance with one-step inference and substantially lower planning latency than diffusion sampling.
  arXiv  Detail & Related papers  (2026-03-14T17:56:15Z)
• MapGCLR: Geospatial Contrastive Learning of Representations for Online Vectorized HD Map Construction [44.37176678126708]
  Autonomous vehicles rely on map information to understand the world around them.<n>A more scalable alternative lies in online HD map construction, which only requires map annotations at training time.<n>This work focuses on improving the latent birds-eye-view (BEV) feature grid representation within a vectorized online HD map construction model.<n>We train the same model supervised using a reduced set of single-traversal labeled data and self-supervised on a broader unlabeled set of data following our multi-traversal requirements.
  arXiv  Detail & Related papers  (2026-03-11T11:58:23Z)
• CoWTracker: Tracking by Warping instead of Correlation [53.834673070954494]
  We propose a dense point tracker that eschews cost volumes in favor of warping.<n>Inspired by recent advances in optical flow, our approach iteratively refines track estimates by warping features from the target frame to the query frame based on the current estimate.<n>Our model is simple and achieves state-of-the-art performance on standard dense point tracking benchmarks, including TAP-Vid-DAVIS, TAP-Vid-Kinetics, and Robo-TAP.
  arXiv  Detail & Related papers  (2026-02-04T18:58:59Z)
• SCoTT: Strategic Chain-of-Thought Tasking for Wireless-Aware Robot Navigation in Digital Twins [78.53885607559958]
  We propose SCoTT, a wireless-aware path planning framework.<n>We show that SCoTT achieves path gains within 2% of DP-WA* while consistently generating shorter trajectories.<n>We also show the practical viability of our approach by deploying SCoTT as a ROS node within Gazebo simulations.
  arXiv  Detail & Related papers  (2024-11-27T10:45:49Z)
• Affordances-Oriented Planning using Foundation Models for Continuous Vision-Language Navigation [64.84996994779443]
  We propose a novel Affordances-Oriented Planner for continuous vision-language navigation (VLN) task. Our AO-Planner integrates various foundation models to achieve affordances-oriented low-level motion planning and high-level decision-making. Experiments on the challenging R2R-CE and RxR-CE datasets show that AO-Planner achieves state-of-the-art zero-shot performance.
  arXiv  Detail & Related papers  (2024-07-08T12:52:46Z)
• BEVTrack: A Simple and Strong Baseline for 3D Single Object Tracking in Bird's-Eye View [54.48052449493636]
  3D Single Object Tracking (SOT) is a fundamental task in computer vision and plays a critical role in applications like autonomous driving.<n>We propose BEVTrack, a simple yet effective motion-based tracking method.<n>We show that BEVTrack achieves state-of-the-art results while operating at 200 FPS, enabling real-time applicability.
  arXiv  Detail & Related papers  (2023-09-05T12:42:26Z)
• Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark [97.07865343576361]
  We construct a benchmark with a new drone-captured largescale dataset, named as DroneCrowd. We annotate 20,800 people trajectories with 4.8 million heads and several video-level attributes. We design the Space-Time Neighbor-Aware Network (STNNet) as a strong baseline to solve object detection, tracking and counting jointly in dense crowds.
  arXiv  Detail & Related papers  (2021-05-06T04:46:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.