Observability-Aware Trajectory Optimization: Theory, Viability, and State of the Art
Space & Terrestrial Autonomous Robotics Systems (STARS) Laboratory at the University of Toronto Institute for Aerospace Studies and Robotics Institute（多伦多大学航空航天研究所与机器人研究所空间与地面自主机器人系统实验室）；University of Toronto Institute for Aerospace Studies（多伦多大学航空航天研究所）；Robotics Institute（机器人研究所）；Vector Institute（向量研究所）
Christopher Grebe, Emmett Wise, Jonathan Kelly
备注：
In Proceedings of the IEEE International Conference on Multisensor Fusion and Integration (MFI'21), Karlsruhe, Germany, Sep. 23-25, 2021
Ideally, robots should move in ways that maximize the knowledge gained about the state of both their internal system and the external operating environment. Trajectory design is a challenging problem that has been investigated from a variety of perspectives, ranging from information-theoretic analyses to leaning-based approaches. Recently, observability-based metrics have been proposed to find trajectories that enable rapid and accurate state and parameter estimation. The viability and efficacy of these methods is not yet well understood in the literature. In this paper, we compare two state-of-the-art methods for observability-aware trajectory optimization and seek to add important theoretical clarifications and valuable discussion about their overall effectiveness. For evaluation, we examine the representative task of sensor-to-sensor extrinsic self-calibration using a realistic physics simulator. We also study the sensitivity of these algorithms to changes in the information content of the exteroceptive sensor measurements.
University of Southern California（南加州大学）；Alpen-Adria-Universitat Klagenfurt（克拉根福阿尔卑斯-亚德里亚大学）
We study the nonlinear observability of a systems states in view of how well they are observable and what control inputs would improve the convergence of their estimates. We use these insights to develop an observability-aware trajectory-optimization framework for nonlinear systems that produces trajectories well suited for self-calibration. Common trajectory-planning algorithms tend to generate motions that lead to an unobservable subspace of the system state, causing suboptimal state estimation. We address this problem with a method that reasons about the quality of observability while respe…
Observability-Aware Active Calibration of Multi-Sensor Extrinsics for Ground Robots via Online Trajectory Optimization
Shenzhen Key Laboratory of Control Theory and Intelligent Systems（深圳控制理论与智能系统重点实验室）；Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology（广东省全驱动系统控制理论与技术省级重点实验室）；Southern University of Science and Technology（南方科技大学）
As autonomous systems increasingly rely on onboard sensing for localization and perception, the parallel tasks of motion planning and state estimation become more strongly coupled. This coupling is well-captured by augmenting the planning objective with a posterior-covariance penalty -- however, prediction of the estimator covariance is challenging when the observation model depends on unknown landmarks, as is the case in Simultaneous Localization and Mapping (SLAM). This paper addresses these challenges in the case of landmark- and SLAM-based estimators, enabling efficient prediction (and ult…
Norwegian University of Science and Technology（挪威科技大学）；SINTEF DIGITAL（SINTEF数字研究所）
In this article we show how the model predictive path following controller allows robotic manipulators to stop at obstructions in a way that model predictive trajectory tracking controllers cannot. We present both controllers as applied to robotic manipulators, simulations for a two-link manipulator using an interior point solver, consider discretization of the optimal control problem using collocation or Runge-Kutta, and discuss the real-time viability of our implementation of the model predictive path following controller.
SafeMove-RL: A Certifiable Reinforcement Learning Framework for Dynamic Motion Constraints in Trajectory Planning
Sino-German College of Intelligent Manufacturing, Shenzhen Technology University（深圳技术大学中德智能制造学院）；Faculty of Science and Technology, University of Macau（澳门大学科技学院）
University of Klagenfurt（克拉根福大学）；ENS（巴黎高等师范学院）；Univ Rennes（雷恩大学）；Inria（法国国家信息与自动化研究所）；IRISA（法国信息与随机系统研究所）；CNRS（法国国家科学研究中心）
In this research, we aim to answer the question: How to combine Closed-Loop State and Input Sensitivity-based with Observability-aware trajectory planning? These possibly opposite optimization objectives can be used to improve trajectory control tracking and, at the same time, estimation performance. Our proposed novel Control & Observability-aware Planning (COP) framework is the first that uses these possibly opposing objectives in a Single-Objective Optimization Problem (SOOP) based on the Augmented Weighted Tchebycheff method to perform the balancing of them and generation of Bézier curve-b…
Road user behavior prediction is one of the most critical components in trajectory planning for autonomous driving, especially in urban scenarios involving traffic signals. In this paper, a hierarchical framework is proposed to predict vehicle behaviors at a signalized intersection, using the traffic signal information of the intersection. The framework is composed of two phases: a discrete intention prediction phase and a continuous trajectory prediction phase. In the discrete intention prediction phase, a Bayesian network is adopted to predict the vehicle's high-level intention, after that, …
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所模式识别国家重点实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Odometer-aided visual-inertial SLAM systems typically have a good performance for navigation of wheeled platforms, while they usually suffer from degenerate cases before the first turning. In this paper, firstly we perform an observability analysis w.r.t. the extrinsic parameters before the first turning, which is a complement of the existing results of observability analyses. Secondly, inspired by the above observability analyses, we propose a bidirectional trajectory computation method, by which the poses before the first turning are refined in the backward computation thread, and the real-t…
Agentic Reward Modeling: Verifying GUI Agent via Progressive Trajectory-Grounded Interaction
MAIS, Institute of Automation, Chinese Academy of Sciences；School of Artificial Intelligence, University of Chinese Academy of Sciences；School of Computer Science \& Technology, Beijing Jiaotong University
Autonomous Systems Lab, ETH Zürich（苏黎世联邦理工学院自主系统实验室）；Alibaba Group（阿里巴巴集团）
External effects such as shocks and temperature variations affect the calibration of visual-inertial sensor systems and thus they cannot fully rely on factory calibrations. Re-calibrations performed on short user-collected datasets might yield poor performance since the observability of certain parameters is highly dependent on the motion. Additionally, on resource-constrained systems (e.g mobile phones), full-batch approaches over longer sessions quickly become prohibitively expensive. In this paper, we approach the self-calibration problem by introducing information theoretic metrics to asse…
In this paper, we introduce a novel method to capture visual trajectories for navigating an indoor robot in dynamic settings using streaming image data. First, an image processing pipeline is proposed to accurately segment trajectories from noisy backgrounds. Next, the captured trajectories are used to design, train, and compare two neural network architectures for predicting acceleration and steering commands for a line following robot over a continuous space in real time. Lastly, experimental results demonstrate the performance of the neural networks versus human teleoperation of the robot a…
Positioning with one inertial measurement unit and one ranging sensor is commonly thought to be feasible only when trajectories are in certain patterns ensuring observability. For this reason, to pursue observable patterns, it is required either exciting the trajectory or searching key nodes in a long interval, which is commonly highly nonlinear and may also lack resilience. Therefore, such a positioning approach is still not widely accepted in real-world applications. To address this issue, this work first investigates the dissipative nature of flying robots considering aerial drag effects an…
Joint Optimization-based Targetless Extrinsic Calibration for Multiple LiDARs and GNSS-Aided INS of Ground Vehicles
Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems, Macau University of Science and Technology（系统工程研究院和智能科学与系统联合实验室，澳门科学技术大学）；Institute for AI Industry Research (AIR), Tsinghua University（人工智能产业研究院（AIR），清华大学）
