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.

翻译：本文提出SmartBSP——一种用于自主机器人在复杂环境中实现实时路径规划与避障的先进自监督学习框架。该系统将近端策略优化（PPO）与卷积神经网络（CNN）及执行器-评判器架构相结合，以处理有限的激光雷达输入并计算空间决策概率。机器人的感知场被离散化为网格格式，由CNN进行分析并生成空间概率分布。训练过程中通过最小化综合考虑路径曲率、终点接近度与避障要求的精细化代价函数来实现优化。多场景仿真结果验证了该算法在不同操作情境下的鲁棒性与适应性。随后，研究基于机器人操作系统（ROS）开展了实时实验，以评估所提算法的实际效能。