RoboNav Arm Agentic AI Driven Navigation and Obstacle Avoidance for Robotic Manipulator in Cluttered Environments

Jul 16, 2026

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Robotic manipulators operating in cluttered, unstructured environments face safety challenges when dynamic obstacles disrupt goal-directed tasks. RoboNav-Arm proposes an agentic AI framework for safe execution and effective obstacle avoidance by performing real-time obstacle detection, 3D localization, and ground geometry estimation, then producing semantic obstacle reports with zone-aware positioning. A coordination module manages tool invocation and inter-module communication, while a planning module selects RRTConnect, RRT*, or BiTRRT according to configuration and goal constraints, refining trajectories for collision-free operation.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages6

Reading Time15 min

Introduction

Background and challenges in cluttered manipulation

Limitations of classical and learning-based approaches

Motivation for agentic AI and LLM integration

RoboNav-Arm: Agentic AI-Driven Navigation and Obstacle Avoidance for Robotic Manipulator in Cluttered Environments Aachal Sharma 1 and Narendra Kumar Dhar1 arXiv :2607 .09716v1 [ cs .RO] 25 Jun 2026 Abstract—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. I. INTRODUCTION Robotic manipulation has witnessed significant advancements in recent years, allowing robots to deal with more complex tasks with better accuracy and efficiency. However, working effectively in cluttered, obstacle-rich environment remains a difficult problem. In such situations, robots have to adjust their movements carefully, especially when performing tasks like pick-and-place, where they must move around obstacles while dealing with changing and uncertain conditions. Obstacle avoidance is not only about preventing collisions, but also about keeping a safe distance from obstacles, since even getting too close can affect both safety and reliability. At the same time, changes in the environment and uncertainties during execution make things more challenging, so robots need to keep adjusting their actions as they operate. For this reason, obstacle avoidance is better viewed as a dynamic and context-dependent problem rather than a fixed planning task. Classical motion planning methods, including sampling-based approaches for instance, methods such as Rapidly-exploring Random Trees (RRT) and Probabilistic Roadmaps (PRM) along with their improved versions and task-motion planning frameworks, have been widely used for obstacle avoidance in robotic manipulation [1]–[3] . However, they assume full prior environmental knowledge and predefined planning strategies, limiting their use in unknown environments. 1Centre for Artificial Intelligence and Robotics (CAIR), Indian Institute of Technology Mandi, Himachal Pradesh 175005, India. Fig. 1: An overview of RoboNav-Arm To deal with these limitations, learning-based approaches such as reinforcement learning (RL) and imitation learning have been studied. RL-based methods allow robots to learn through interaction, which is useful for complex manipulation tasks, but they often struggle with low sample efficiency and the challenge of defining suitable reward functions [4], [5] . On the other hand, imitation learning makes use of expert demonstrations and has performed well in more structured manipulation settings [6] . Even so, these approaches typically require large amounts of training data, involve significant computational cost, and may not always be reliable in safety-critical scenarios. In addition, they do not explicitly account for safety margins or the risks associated with operating in close proximity to obstacles. Over the past few years, large language mo

FAQs

为什么在杂乱环境中执行抓取等任务会更难？

动态且不可预见的障碍会影响机器人动作可行性与安全距离；环境变化与执行不确定性要求机器人持续调整策略。

传统运动规划方法主要受哪些假设限制？

采样式方法如RRT/PRM通常假设拥有完整先验环境信息和预设规划策略，因此在未知环境中的适应性受到限制。

RoboNav-Arm如何把高层推理与低层避障执行结合起来？

框架包含环境理解模块、中央协调模块与规划模块：环境模块生成带语义与分区信息的障碍报告，规划模块根据配置与目标选择合适算法并对轨迹做安全无碰撞的分析与细化，再由协调模块监控任务进展。

Jul 16, 2026

Upload by Hazel