An Integrated Localisation, Motion Planning and Obstacle Avoidance Algorithm in Belief Space

• URL: http://arxiv.org/abs/2101.11566v1
• Date: Wed, 27 Jan 2021 17:47:45 GMT
• Title: An Integrated Localisation, Motion Planning and Obstacle Avoidance Algorithm in Belief Space
• Authors: Antony Thomas and Fulvio Mastrogiovanni and Marco Baglietto
• Abstract summary: Noisy sensors and actuation errors compound to the errors introduced while estimating features of the environment. We present a novel approach to incorporate these uncertainties for robot state estimation. We compute the probability of collision pertaining to the estimated robot configurations.
• Score: 1.3535770763481902
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As robots are being increasingly used in close proximity to humans and objects, it is imperative that robots operate safely and efficiently under real-world conditions. Yet, the environment is seldom known perfectly. Noisy sensors and actuation errors compound to the errors introduced while estimating features of the environment. We present a novel approach (1) to incorporate these uncertainties for robot state estimation and (2) to compute the probability of collision pertaining to the estimated robot configurations. The expression for collision probability is obtained as an infinite series and we prove its convergence. An upper bound for the truncation error is also derived and the number of terms required is demonstrated by analyzing the convergence for different robot and obstacle configurations. We evaluate our approach using two simulation domains which use a roadmap-based strategy to synthesize trajectories that satisfy collision probability bounds.

Related papers

• Safe and Efficient Path Planning under Uncertainty via Deep Collision Probability Fields [21.741354016294476]
  Estimating collision probabilities is crucial to ensure safety during path planning. Deep Collision Probability Fields is a neural-based approach for computing collision probabilities of arbitrary objects. Our approach relegates the computationally intensive estimation of collision probabilities via sampling at the training step.
  arXiv  Detail & Related papers  (2024-09-06T14:28:41Z)
• A Causal Bayesian Network and Probabilistic Programming Based Reasoning Framework for Robot Manipulation Under Uncertainty [4.087774077861305]
  We propose a flexible and generalisable physics-informed causal Bayesian network (CBN) based framework for a robot. We demonstrate our framework's ability to: (1) predict manipulation outcomes with high accuracy (Pred Acc: 88.6%); and, (2) perform greedy next-best action selection with 94.2% task success rate.
  arXiv  Detail & Related papers  (2024-03-21T15:36:26Z)
• NeuPAN: Direct Point Robot Navigation with End-to-End Model-based Learning [67.53972459080437]
  This paper presents NeuPAN: a real-time, highly-accurate, robot-agnostic, and environment-invariant robot navigation solution. Leveraging a tightly-coupled perception-locomotion framework, NeuPAN has two key innovations compared to existing approaches. We evaluate NeuPAN on car-like robot, wheel-legged robot, and passenger autonomous vehicle, in both simulated and real-world environments.
  arXiv  Detail & Related papers  (2024-03-11T15:44:38Z)
• Safe motion planning with environment uncertainty [1.4824891788575418]
  We present an approach for safe motion planning under robot state and environment uncertainties. We first develop an approach that accounts for the landmark uncertainties during robot localization. We then extend the state-of-the-art by computing an exact expression for the collision probability.
  arXiv  Detail & Related papers  (2023-05-10T09:29:41Z)
• Distributional Instance Segmentation: Modeling Uncertainty and High Confidence Predictions with Latent-MaskRCNN [77.0623472106488]
  In this paper, we explore a class of distributional instance segmentation models using latent codes. For robotic picking applications, we propose a confidence mask method to achieve the high precision necessary. We show that our method can significantly reduce critical errors in robotic systems, including our newly released dataset of ambiguous scenes.
  arXiv  Detail & Related papers  (2023-05-03T05:57:29Z)
• Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
  We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform. We produce a closed-loop controller to reactively push objects in a continuous action space. We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
  arXiv  Detail & Related papers  (2021-11-15T18:50:04Z)
• Exact and Bounded Collision Probability for Motion Planning under Gaussian Uncertainty [1.3535770763481902]
  We present an approach for computing the collision probability under Gaussian distributed motion. The collision condition is formulated as the distance between ellipsoids. We provide a tight upper bound that can be computed much faster during online planning.
  arXiv  Detail & Related papers  (2021-10-12T20:45:18Z)
• SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots [112.2491765424719]
  We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal. We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution. A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
  arXiv  Detail & Related papers  (2021-08-03T02:56:21Z)
• Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidance [110.63037190641414]
  We propose to learn congestion patterns explicitly and devise a novel "Sense--Learn--Reason--Predict" framework. By decomposing the learning phases into two stages, a "student" can learn contextual cues from a "teacher" while generating collision-free trajectories. In experiments, we demonstrate that the proposed model is able to generate collision-free trajectory predictions in a synthetic dataset.
  arXiv  Detail & Related papers  (2021-03-26T02:42:33Z)
• DiffCo: Auto-Differentiable Proxy Collision Detection with Multi-class Labels for Safety-Aware Trajectory Optimization [5.35056843248536]
  We present DiffCo, the first, fully auto-differentiable, non-parametric model for collision detection. It provides robust gradients for trajectory optimization via backpropagation and is often 10-100x faster to compute than its geometric counterparts.
  arXiv  Detail & Related papers  (2021-02-15T09:31:26Z)
• Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance [95.86944752753564]
  We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
  arXiv  Detail & Related papers  (2020-07-28T07:34:30Z)

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.