Ergodic Trajectory Planning with Dynamic Sensor Footprints

• URL: http://arxiv.org/abs/2512.08661v1
• Date: Tue, 09 Dec 2025 14:47:14 GMT
• Title: Ergodic Trajectory Planning with Dynamic Sensor Footprints
• Abstract summary: This paper addresses the problem of trajectory planning for information gathering with a dynamic and resolution-varying sensor footprint.<n>Existing ergodic planning often oversimplifies the sensing model by assuming a point sensor or a footprint with constant shape and resolution.<n>We propose a new metric that accounts for dynamic sensor footprints, analyze the theoretic local optimality conditions, and propose numerical trajectory optimization algorithms.
• Score: 19.00429186592942
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper addresses the problem of trajectory planning for information gathering with a dynamic and resolution-varying sensor footprint. Ergodic planning offers a principled framework that balances exploration (visiting all areas) and exploitation (focusing on high-information regions) by planning trajectories such that the time spent in a region is proportional to the amount of information in that region. Existing ergodic planning often oversimplifies the sensing model by assuming a point sensor or a footprint with constant shape and resolution. In practice, the sensor footprint can drastically change over time as the robot moves, such as aerial robots equipped with downward-facing cameras, whose field of view depends on the orientation and altitude. To overcome this limitation, we propose a new metric that accounts for dynamic sensor footprints, analyze the theoretic local optimality conditions, and propose numerical trajectory optimization algorithms. Experimental results show that the proposed approach can simultaneously optimize both the trajectories and sensor footprints, with up to an order of magnitude better ergodicity than conventional methods. We also deploy our approach in a multi-drone system to ergodically cover an object in 3D space.

Related papers

• A Height-Constrained 2-Point Minimal Solver for Pose Estimation from Active LED Markers with Event Cameras [51.7262911415172]
  We develop an event-based active marker system to collect real-world data with ground truth from a motion capture system.<n>Experiments on both synthetic and real data demonstrate improved accuracy over the state-of-the-art P2P solver.
  arXiv  Detail & Related papers  (2026-08-10T12:19:24Z)
• FLAP: FOV-Constrained Active Perception Planning for Prior-Map-Free 3D Navigation [11.226202958671502]
  We propose a novel planning framework that directly integrates active perception into trajectory optimization.<n>We introduce an active perception sub-trajectory segment with parametric start-time optimization, mitigating collision risks from late obstacle detection.<n>Our formulation enables active perception during arbitrary 3D maneuvers, extending beyond prior methods designed mainly for horizontal motion.
  arXiv  Detail & Related papers  (2026-06-16T07:40:30Z)
• EXACT-MPPI: Exact Signed-Distance Navigation for Arbitrary-Footprint Robots from Point Clouds via Path Integral Control [11.31921914039084]
  EXACT-MPPI is a framework that maps local navigation directly to motion commands.<n>It embeds an analytic, exact signed-distance evaluator into a Model Predictive Path Integral (MPPI) controller.<n>The same framework deploys on differential-drive, Ackermann, and hybrid-mode platforms.
  arXiv  Detail & Related papers  (2026-05-28T09:23:54Z)
• Ultrafast Sampling-based Kinodynamic Planning via Differential Flatness [15.37007491684187]
  AkinoPDF is a fast parallelized sampling-based kinodynamic motion planning technique for a broad class of differentially flat robot systems.<n>Our method is fast, exact, and compatible with any sampling-based motion planner.
  arXiv  Detail & Related papers  (2026-03-17T01:53:10Z)
• OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising [49.86409475232849]
  Trajectory prediction is fundamental in computer vision and autonomous driving. Existing approaches in this field often assume precise and complete observational data. We present a novel method for out-of-sight trajectory prediction that leverages a vision-positioning technique.
  arXiv  Detail & Related papers  (2024-04-02T18:30:29Z)
• DynST: Dynamic Sparse Training for Resource-Constrained Spatio-Temporal Forecasting [31.398965880415492]
  Earth science systems rely heavily on the extensive deployment of sensors.<n>Traditional approaches to sensor deployment utilize specific algorithms to design and deploy sensors.<n>We introduce for the first time the concept of sparse-temporal data dynamic sparse training and are committed to adaptively, dynamically filtering important distributions sensor.
  arXiv  Detail & Related papers  (2024-03-05T12:31:24Z)
• Point Cloud Forecasting as a Proxy for 4D Occupancy Forecasting [58.45661235893729]
  One promising self-supervised task is 3D point cloud forecasting from unannotated LiDAR sequences. We show that this task requires algorithms to implicitly capture (1) sensor extrinsics (i.e., the egomotion of the autonomous vehicle), (2) sensor intrinsics (i.e., the sampling pattern specific to the particular LiDAR sensor), and (3) the shape and motion of other objects in the scene. We render point cloud data from 4D occupancy predictions given sensor extrinsics and intrinsics, allowing one to train and test occupancy algorithms with unannotated LiDAR sequences.
  arXiv  Detail & Related papers  (2023-02-25T18:12:37Z)
• DDPEN: Trajectory Optimisation With Sub Goal Generation Model [70.36888514074022]
  In this paper, we produce a novel Differential Dynamic Programming with Escape Network (DDPEN) We propose to utilize a deep model that takes as an input map of the environment in the form of a costmap together with the desired position. The model produces possible future directions that will lead to the goal, avoiding local minima which is possible to run in real time conditions.
  arXiv  Detail & Related papers  (2023-01-18T11:02:06Z)
• Environmental Sensor Placement with Convolutional Gaussian Neural Processes [65.13973319334625]
  It is challenging to place sensors in a way that maximises the informativeness of their measurements, particularly in remote regions like Antarctica. Probabilistic machine learning models can suggest informative sensor placements by finding sites that maximally reduce prediction uncertainty. This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues.
  arXiv  Detail & Related papers  (2022-11-18T17:25:14Z)
• PUCK: Parallel Surface and Convolution-kernel Tracking for Event-Based Cameras [4.110120522045467]
  Event-cameras can guarantee fast visual sensing in dynamic environments, but require a tracking algorithm that can keep up with the high data rate induced by the robot ego-motion. We introduce a novel tracking method that leverages the Exponential Reduced Ordinal Surface (EROS) data representation to decouple event-by-event processing and tracking. We propose the task of tracking the air hockey puck sliding on a surface, with the future aim of controlling the iCub robot to reach the target precisely and on time.
  arXiv  Detail & Related papers  (2022-05-16T13:23:52Z)
• Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphs [5.043563227694137]
  We consider an autonomous exploration problem in which a range-sensing mobile robot is tasked with accurately mapping the landmarks in an a priori unknown environment efficiently in real-time. We propose a novel approach that uses graph neural networks (GNNs) in conjunction with deep reinforcement learning (DRL), enabling decision-making over graphs containing exploration information to predict a robot's optimal sensing action in belief space.
  arXiv  Detail & Related papers  (2020-07-24T16:50:41Z)
• Deep Soft Procrustes for Markerless Volumetric Sensor Alignment [81.13055566952221]
  In this work, we improve markerless data-driven correspondence estimation to achieve more robust multi-sensor spatial alignment. We incorporate geometric constraints in an end-to-end manner into a typical segmentation based model and bridge the intermediate dense classification task with the targeted pose estimation one. Our model is experimentally shown to achieve similar results with marker-based methods and outperform the markerless ones, while also being robust to the pose variations of the calibration structure.
  arXiv  Detail & Related papers  (2020-03-23T10:51:32Z)

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.