SABER: Data-Driven Motion Planner for Autonomously Navigating
Heterogeneous Robots
- URL: http://arxiv.org/abs/2108.01262v1
- Date: Tue, 3 Aug 2021 02:56:21 GMT
- Title: SABER: Data-Driven Motion Planner for Autonomously Navigating
Heterogeneous Robots
- Authors: Alexander Schperberg, Stephanie Tsuei, Stefano Soatto, Dennis Hong
- Abstract summary: 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.
- Score: 112.2491765424719
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 while avoiding obstacles in uncertain environments. First, we use
stochastic model predictive control (SMPC) to calculate control inputs that
satisfy robot dynamics, and consider uncertainty during obstacle avoidance with
chance constraints. Second, recurrent neural networks are used to provide a
quick estimate of future state uncertainty considered in the SMPC finite-time
horizon solution, which are trained on uncertainty outputs of various
simultaneous localization and mapping algorithms. When two or more robots are
in communication range, these uncertainties are then updated using a
distributed Kalman filtering approach. Lastly, 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. Our complete
methods are demonstrated on a ground and aerial robot simultaneously (code
available at: https://github.com/AlexS28/SABER).
Related papers
- Generalizability of Graph Neural Networks for Decentralized Unlabeled Motion Planning [72.86540018081531]
Unlabeled motion planning involves assigning a set of robots to target locations while ensuring collision avoidance.
This problem forms an essential building block for multi-robot systems in applications such as exploration, surveillance, and transportation.
We address this problem in a decentralized setting where each robot knows only the positions of its $k$-nearest robots and $k$-nearest targets.
arXiv Detail & Related papers (2024-09-29T23:57:25Z) - LPAC: Learnable Perception-Action-Communication Loops with Applications
to Coverage Control [80.86089324742024]
We propose a learnable Perception-Action-Communication (LPAC) architecture for the problem.
CNN processes localized perception; a graph neural network (GNN) facilitates robot communications.
Evaluations show that the LPAC models outperform standard decentralized and centralized coverage control algorithms.
arXiv Detail & Related papers (2024-01-10T00:08:00Z) - 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) - Intelligent Trajectory Design for RIS-NOMA aided Multi-robot
Communications [59.34642007625687]
The goal is to maximize the sum-rate of whole trajectories for multi-robot system by jointly optimizing trajectories and NOMA decoding orders of robots.
An integrated machine learning (ML) scheme is proposed, which combines long short-term memory (LSTM)-autoregressive integrated moving average (ARIMA) model and dueling double deep Q-network (D$3$QN) algorithm.
arXiv Detail & Related papers (2022-05-03T17:14:47Z) - Graph Neural Networks for Decentralized Multi-Robot Submodular Action
Selection [101.38634057635373]
We focus on applications where robots are required to jointly select actions to maximize team submodular objectives.
We propose a general-purpose learning architecture towards submodular at scale, with decentralized communications.
We demonstrate the performance of our GNN-based learning approach in a scenario of active target coverage with large networks of robots.
arXiv Detail & Related papers (2021-05-18T15:32:07Z) - Learning Interaction-Aware Trajectory Predictions for Decentralized
Multi-Robot Motion Planning in Dynamic Environments [10.345048137438623]
We introduce a novel trajectory prediction model based on recurrent neural networks (RNN)
We then incorporate the trajectory prediction model into a decentralized model predictive control (MPC) framework for multi-robot collision avoidance.
arXiv Detail & Related papers (2021-02-10T11:11:08Z) - 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) - 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)
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.