RoboNav-Arm：杂乱环境中机器人操纵器的智能AI驱动导航与避障
RoboNav-Arm: Agentic AI-Driven Navigation and Obstacle Avoidance for Robotic Manipulator in Cluttered Environments
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中文总结 AI 辅助
针对杂乱环境中机器人操纵器面临的挑战，提出含环境、中央协调和规划模块的安全任务执行框架，能实时检测与定位障碍物，依环境选算法规划轨迹并优化，在Gazebo Classic中评估，展现动态场景鲁棒性。
中文摘要 AI 辅助
在非结构化环境中运行的机器人操纵器，由于动态和不可预见的障碍物，在安全执行目标导向任务上面临重大挑战，而传统方法依赖先验知识或固定感知管道，限制了适应性。我们提出了一个具有有效避障功能的安全任务执行框架。环境模块执行实时障碍物检测、3D定位和地面几何估计，生成结构化语义报告。中央协调模块管理整个系统，规划模块根据当前环境配置和目标要求选择合适的运动规划算法，生成的轨迹会进一步分析和优化以确保安全无碰撞任务执行。该方法在Gazebo Classic中进行了评估，证明了在动态场景中的鲁棒性。
英文摘要
Robotic manipulators operating in unstructured environments face significant challenges in safely executing goal-directed tasks due to dynamic and unforeseen obstacles, while traditional methods rely on prior knowledge or fixed perception pipelines, limiting adaptability. We propose a framework for safe task execution with effective obstacle avoidance. The environment module performs real-time obstacle detection, 3D localization, and ground surface geometry estimation. It then generates a structured semantic report that includes obstacle positions, object geometry and shape, and whether obstacles lie inside, outside, or within critical interaction zones. A central coordination module manages the overall system by handling tool invocation (e.g., memory and MoveIt collision scene updates), facilitating communication between modules, and continuously monitoring task progress until completion. Furthermore, a planning module selects an appropriate motion planning algorithm, such as RRTConnect, RRT*, or BiTRRT, based on the current environment configuration and goal requirements. The trajectory generated by the planner is further analyzed and refined to ensure safe and collision-free task execution. The proposed approach is evaluated in Gazebo Classic , demonstrating robustness in dynamic scenarios.
发表机构
- Centre for Artificial Intelligence and Robotics (CAIR), Indian Institute of Technology Mandi（人工智能与机器人中心（CAIR），印度曼迪理工学院）
机构由 AI 辅助整理，请以论文原文为准。
