用自监督学习实现机器人在未知环境中的实时避障路径规划
Self-Supervised Learning-Based Path Planning and Obstacle Avoidance Using PPO and B-Splines in Unknown Environments
- 结合PPO与CNN，从有限激光雷达数据生成空间决策概率
- 通过优化路径曲率、终点距离和避障成本提升规划效果
- 适合需要实时自主导航的移动机器人研究者参考
本文提出SmartBSP，一种基于自监督学习的先进框架，用于在复杂环境中实现自主机器人实时路径规划与障碍物避让。系统融合近端策略优化（PPO）与卷积神经网络（CNN）及演员-评论家架构，处理有限激光雷达输入并计算空间决策概率。将机器人的感知区域离散化为网格，由CNN分析以生成空间概率分布。训练过程中最小化包含路径曲率、终点接近度和避障要求的综合代价函数。不同场景下的仿真结果验证了算法在多样化操作环境中的鲁棒性与适应性。随后，基于机器人操作系统（ROS）开展实时实验，评估所提算法的有效性。
原文摘要 · Abstract (English)
This paper introduces SmartBSP, an advanced self-supervised learning framework for real-time path planning and obstacle avoidance in autonomous robotics navigating through complex environments. The proposed system integrates Proximal Policy Optimization (PPO) with Convolutional Neural Networks (CNN) and Actor-Critic architecture to process limited LIDAR inputs and compute spatial decision-making probabilities. The robot's perceptual field is discretized into a grid format, which the CNN analyzes to produce a spatial probability distribution. During the training process a nuanced cost function is minimized that accounts for path curvature, endpoint proximity, and obstacle avoidance. Simulations results in different scenarios validate the algorithm's resilience and adaptability across diverse operational scenarios. Subsequently, Real-time experiments, employing the Robot Operating System (ROS), were carried out to assess the efficacy of the proposed algorithm.
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