ReSPIRe: Informative and Reusable Belief Tree Search for Robot Probabilistic Search and Tracking in Unknown Environments

• URL: http://arxiv.org/abs/2512.24680v1
• Date: Wed, 31 Dec 2025 07:13:14 GMT
• Title: ReSPIRe: Informative and Reusable Belief Tree Search for Robot Probabilistic Search and Tracking in Unknown Environments
• Abstract summary: This paper proposes an informative trajectory planning approach, namely ReSPIRe, for target search and tracking (SAT)<n>We first develop a novel point-based approximation approach to fast and accurately estimate mutual information reward under non-Gaussian belief distributions.<n>To tackle significant uncertainty associated with inadequate prior target information, we propose the hierarchical particle structure in ReSPIRe.
• Score: 25.261065989248877
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Target search and tracking (SAT) is a fundamental problem for various robotic applications such as search and rescue and environmental exploration. This paper proposes an informative trajectory planning approach, namely ReSPIRe, for SAT in unknown cluttered environments under considerably inaccurate prior target information and limited sensing field of view. We first develop a novel sigma point-based approximation approach to fast and accurately estimate mutual information reward under non-Gaussian belief distributions, utilizing informative sampling in state and observation spaces to mitigate the computational intractability of integral calculation. To tackle significant uncertainty associated with inadequate prior target information, we propose the hierarchical particle structure in ReSPIRe, which not only extracts critical particles for global route guidance, but also adjusts the particle number adaptively for planning efficiency. Building upon the hierarchical structure, we develop the reusable belief tree search approach to build a policy tree for online trajectory planning under uncertainty, which reuses rollout evaluation to improve planning efficiency. Extensive simulations and real-world experiments demonstrate that ReSPIRe outperforms representative benchmark methods with smaller MI approximation error, higher search efficiency, and more stable tracking performance, while maintaining outstanding computational efficiency.

Related papers

• SLIDER: Sparse History-Guided Aerial Robot Target Search using Sliding Local Maps [8.942263751725413]
  This paper presents SLIDER, a lightweight framework that avoids reliance on globally dense maps by combining a local sliding map with sparse global history information.<n>A novel observation quality evaluation method is proposed, leveraging historical poses and sensor models to assess point cloud data in real-time.<n>To support scalable and responsive planning, an incremental viewpoint clustering strategy dynamically adapts to local updates.<n>A sparse global topological map is incrementally maintained to assist global planning and cost evaluation.
  arXiv  Detail & Related papers  (2026-07-12T03:55:51Z)
• An Efficient Beam Search Algorithm for Active Perception in Mobile Robotics [10.002920418375007]
  Active perception is a fundamental problem in autonomous robotics in which the robot must decide where to move.<n>Existing approaches either solve a computationally expensive traveling problem overly selected informative nodes, or adopt a more efficient but overly constrained shortest path tree formulation.<n>We propose a node-wise beam search (NBS) algorithm, which maintains top-B candidates per node to enable more effective exploration of the solution space.<n>As a second contribution, we integrate the concept of frontiers into the path selection criterion, introducing the expected gain metric.<n>Our third contribution proposes the rapidly-exploring random annulus graph (R
  arXiv  Detail & Related papers  (2026-04-25T14:35:11Z)
• Heuristic-inspired Reasoning Priors Facilitate Data-Efficient Referring Object Detection [53.988759250627425]
  HeROD (Heuristic-inspired ROD) is a lightweight, model-agnostic framework that injects explicit, interpretable spatial and semantic reasonings.<n>HeROD consistently outperforms strong grounding baselines in scarcelabel regimes.
  arXiv  Detail & Related papers  (2026-03-25T10:33:22Z)
• Informative Path Planning with Guaranteed Estimation Uncertainty [7.2097330104931325]
  Environmental monitoring budget robots need to reconstruct spatial fields under tight distance and energy constraints.<n>We present methods with provable approximation guarantees for sensing-location selection and for the joint selection-and-routing problem under a travel path.<n> Experiments on real-world data show that our planners meet the uncertainty target using fewer sensing locations and shorter travel distances.
  arXiv  Detail & Related papers  (2026-02-05T01:51:38Z)
• Rethinking Evaluation of Infrared Small Target Detection [105.59753496831739]
  This paper introduces a hybrid-level metric incorporating pixel- and target-level performance, proposing a systematic error analysis method, and emphasizing the importance of cross-dataset evaluation.<n>An open-source toolkit has be released to facilitate standardized benchmarking.
  arXiv  Detail & Related papers  (2025-09-21T02:45:07Z)
• Probing Deep into Temporal Profile Makes the Infrared Small Target Detector Much Better [63.567886330598945]
  Infrared small target (IRST) detection is challenging in simultaneously achieving precise, universal, robust and efficient performance.<n>Current learning-based methods attempt to leverage more" information from both the spatial and the short-term temporal domains.<n>We propose an efficient deep temporal probe network (DeepPro) that only performs calculations in the time dimension for IRST detection.
  arXiv  Detail & Related papers  (2025-06-15T08:19:32Z)
• Explorable INR: An Implicit Neural Representation for Ensemble Simulation Enabling Efficient Spatial and Parameter Exploration [25.58241100888983]
  Current surrogate models fall short in the flexibility of point- or region-based predictions.<n>We propose Explorable INR, a novel implicit neural representation-based surrogate model.<n>We demonstrate that the Explorable INR with the proposed approach for spatial and parameter exploration can significantly reduce computation and memory costs.
  arXiv  Detail & Related papers  (2025-04-01T15:33:28Z)
• Next-Best-Trajectory Planning of Robot Manipulators for Effective Observation and Exploration [0.26999000177990923]
  Next-Best-Trajectory principle is developed for a robot manipulator operating in dynamic environments.<n>We employ a voxel map for environment modeling and utilize raycasting from perspectives around a point of interest to estimate the information gain.<n>A global ergodic trajectory planner provides an optional reference trajectory to the local planner, improving exploration and helping to avoid local minima.
  arXiv  Detail & Related papers  (2025-03-28T16:34:29Z)
• MEXGEN: An Effective and Efficient Information Gain Approximation for Information Gathering Path Planning [3.195234044113248]
  Planning algorithms for autonomous robots need to solve sequential decision making problems under uncertainty. We develop a computationally efficient and effective approximation for the difficult problem of predicting the likely sensor measurements from uncertain belief states. We demonstrate improved performance gains in radio-source tracking and localization problems using extensive simulated and field experiments with a multirotor aerial robot.
  arXiv  Detail & Related papers  (2024-05-04T08:09:16Z)
• RTracker: Recoverable Tracking via PN Tree Structured Memory [71.05904715104411]
  We propose a recoverable tracking framework, RTracker, that uses a tree-structured memory to dynamically associate a tracker and a detector to enable self-recovery. Specifically, we propose a Positive-Negative Tree-structured memory to chronologically store and maintain positive and negative target samples. Our core idea is to use the support samples of positive and negative target categories to establish a relative distance-based criterion for a reliable assessment of target loss.
  arXiv  Detail & Related papers  (2024-03-28T08:54:40Z)
• ROME: Robustifying Memory-Efficient NAS via Topology Disentanglement and Gradient Accumulation [106.04777600352743]
  Differentiable architecture search (DARTS) is largely hindered by its substantial memory cost since the entire supernet resides in the memory. The single-path DARTS comes in, which only chooses a single-path submodel at each step. While being memory-friendly, it also comes with low computational costs. We propose a new algorithm called RObustifying Memory-Efficient NAS (ROME) to give a cure.
  arXiv  Detail & Related papers  (2020-11-23T06:34:07Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)
• Rethinking Performance Estimation in Neural Architecture Search [191.08960589460173]
  We provide a novel yet systematic rethinking of performance estimation (PE) in a resource constrained regime. By combining BPE with various search algorithms including reinforcement learning, evolution algorithm, random search, and differentiable architecture search, we achieve 1, 000x of NAS speed up with a negligible performance drop.
  arXiv  Detail & Related papers  (2020-05-20T09:01:44Z)

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.