Piecewise linear regressions for approximating distance metrics
- URL: http://arxiv.org/abs/2002.12466v1
- Date: Thu, 27 Feb 2020 22:23:58 GMT
- Title: Piecewise linear regressions for approximating distance metrics
- Authors: Josiah Putman, Lisa Oh, Luyang Zhao, Evan Honnold, Galen Brown, Weifu
Wang, Devin Balkcom
- Abstract summary: This paper presents a data structure that summarizes distances between configurations across a robot configuration space.
The paper explores the use of the data structure constructed for a single robot to provide a for challenging multi-robot motion planning problems.
- Score: 1.1241621778067437
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper presents a data structure that summarizes distances between
configurations across a robot configuration space, using a binary space
partition whose cells contain parameters used for a locally linear
approximation of the distance function. Querying the data structure is
extremely fast, particularly when compared to the graph search required for
querying Probabilistic Roadmaps, and memory requirements are promising. The
paper explores the use of the data structure constructed for a single robot to
provide a heuristic for challenging multi-robot motion planning problems.
Potential applications also include the use of remote computation to analyze
the space of robot motions, which then might be transmitted on-demand to robots
with fewer computational resources.
Related papers
- Learning Object Properties Using Robot Proprioception via Differentiable Robot-Object Interaction [52.12746368727368]
Differentiable simulation has become a powerful tool for system identification.
Our approach calibrates object properties by using information from the robot, without relying on data from the object itself.
We demonstrate the effectiveness of our method on a low-cost robotic platform.
arXiv Detail & Related papers (2024-10-04T20:48:38Z) - Multi-Robot Relative Pose Estimation in SE(2) with Observability
Analysis: A Comparison of Extended Kalman Filtering and Robust Pose Graph
Optimization [1.0485739694839669]
We focus on cooperative localization and observability analysis of relative pose estimation.
For ROS/Gazebo simulations, we explore four sensing and communication structures.
In hardware experiments, two Turtlebot3 equipped with UWB modules are used for real-world inter-robot relative pose estimation.
arXiv Detail & Related papers (2024-01-27T06:09:56Z) - JRDB-Traj: A Dataset and Benchmark for Trajectory Forecasting in Crowds [79.00975648564483]
Trajectory forecasting models, employed in fields such as robotics, autonomous vehicles, and navigation, face challenges in real-world scenarios.
This dataset provides comprehensive data, including the locations of all agents, scene images, and point clouds, all from the robot's perspective.
The objective is to predict the future positions of agents relative to the robot using raw sensory input data.
arXiv Detail & Related papers (2023-11-05T18:59:31Z) - Neural Potential Field for Obstacle-Aware Local Motion Planning [46.42871544295734]
We propose a neural network model that returns a differentiable collision cost based on robot pose, obstacle map, and robot footprint.
Our architecture includes neural image encoders, which transform obstacle maps and robot footprints into embeddings.
Experiment on Husky UGV mobile robot showed that our approach allows real-time and safe local planning.
arXiv Detail & Related papers (2023-10-25T05:00:21Z) - 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) - Relative Localization of Mobile Robots with Multiple Ultra-WideBand
Ranging Measurements [15.209043435869189]
We propose an approach to estimate the relative pose between a group of robots by equipping each robot with multiple UWB ranging nodes.
To improve the localization accuracy, we propose to utilize the odometry constraints through a sliding window-based optimization.
arXiv Detail & Related papers (2021-07-19T12:57:02Z) - Task-relevant Representation Learning for Networked Robotic Perception [74.0215744125845]
This paper presents an algorithm to learn task-relevant representations of sensory data that are co-designed with a pre-trained robotic perception model's ultimate objective.
Our algorithm aggressively compresses robotic sensory data by up to 11x more than competing methods.
arXiv Detail & Related papers (2020-11-06T07:39:08Z) - Laser2Vec: Similarity-based Retrieval for Robotic Perception Data [7.538482310185135]
This paper implements a system for storing 2D LiDAR data from many deployments cheaply and evaluating top-k queries for complete or partial scans efficiently.
We generate compressed representations of laser scans via a convolutional variational autoencoder and store them in a database.
We find our system accurately and efficiently identifies similar scans across a number of episodes where the robot encountered the same location.
arXiv Detail & Related papers (2020-07-30T21:11:50Z) - 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) - Combinatorics of a Discrete Trajectory Space for Robot Motion Planning [4.477410849696538]
complexity of the problem is directly related to the dimension of the robot's configuration space.
We present a discrete robot model based on the fundamental hardware specifications of a robot.
arXiv Detail & Related papers (2020-05-25T12:14:20Z)
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.