Chrome Extension
WeChat Mini Program
Use on ChatGLM

Prescient Collision-Free Navigation of Mobile Robots with Iterative Multimodal Motion Prediction of Dynamic Obstacles

IEEE Robotics and Automation Letters(2024)SCI 2区

Chalmers University of Technology | Magna International

Cited 1|Views17
Abstract
To explore safe interactions between a mobile robot and dynamic obstacles, this letter presents a comprehensive approach to collision-free navigation in dynamic indoor environments. The approach integrates Multimodal Motion Predictions (MMPs) of dynamic obstacles with predictive control for obstacle avoidance. MMP is achieved by a deep-learning method that predicts multiple plausible future positions. By repeating the MMP for each time offset in the future, multi-time-step MMPs are obtained. A nonlinear Model Predictive Control (MPC) solver uses the prediction outcomes to achieve collision-free trajectory tracking for the mobile robot. The proposed integration of multimodal motion prediction and trajectory tracking outperforms other non-deep-learning methods in complex scenarios. The approach enables safe interaction between the mobile robot and stochastic dynamic obstacles.
More
Translated text
Key words
Collision avoidance,autonomous agents,deep learning methods
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper

要点】:本文提出了一种基于迭代多模态运动预测的移动机器人无碰撞导航方法,通过融合动态障碍物的多模态运动预测与预测控制,实现移动机器人在动态室内环境中的安全行驶。

方法】:采用深度学习方法实现多模态运动预测(MMP),并通过非线性模型预测控制(MPC)求解器利用预测结果,生成无碰撞的机器人轨迹。

实验】:文中未具体提及实验细节和数据集名称,但指出该方法在处理复杂场景时优于其他非深度学习方法,并能够实现移动机器人与随机动态障碍物的安全交互。