Interest in agricultural robotics has increased considerably in recent years due to benefits such as improvement in productivity and labor reduction. However, current problems associated with unstructured environments make the development of robotic harvesters challenging. Most research in agricultural robotics focuses on single arm manipulation. Here, we propose a dual-arm approach. We present a dual-arm fruit harvesting robot equipped with a RGB-D camera, cutting and collecting tools. We exploit the cooperative task description to maximize the capabilities of the dual-arm robot. We designed a Hierarchical Quadratic Programming based control strategy to fulfill the set of hard constrains related to the robot and environment: robot joint limits, robot self-collisions, robot-fruit and robot-tree collisions. We combine deep learning and standard image processing algorithms to detect and track fruits as well as the tree trunk in the scene. We validate our perception methods on real-world RGB-D images and our control method on simulated experiments.

翻译：近年来，由于提高生产率和减少劳动力等优势，农业机器人引起了极大关注。然而，当前与非结构化环境相关的问题使得采摘机器人的开发充满挑战。大多数农业机器人研究侧重于单臂操作。在此，我们提出一种双臂方法。我们介绍了一种配备RGB-D相机、切割和收集工具的双臂水果采摘机器人。我们利用协作任务描述来最大化双臂机器人的能力。我们设计了一种基于分层二次规划的控制策略，以满足与机器人和环境相关的一组硬约束：机器人关节限制、机器人自碰撞、机器人与水果及机器人与树木的碰撞。我们结合深度学习与标准图像处理算法，用于检测和跟踪场景中的水果及树干。我们通过真实世界RGB-D图像验证了感知方法，并通过仿真实验验证了控制方法。