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，由于农业机器人能提升生产力并减少劳动力投入，相关研究兴趣显著增长。然而，非结构化环境带来的现有问题使得采摘机器人的开发面临挑战。目前农业机器人研究大多集中于单臂操作，本文提出了一种双臂方案。我们设计了一款配备RGB-D摄像头、切割与收集工具的双臂水果采摘机器人，通过协同任务描述最大化双臂机器人的能力。为满足机器人与环境相关的硬约束（关节限位、自碰撞、机器人-果实碰撞及机器人-树干碰撞），我们设计了基于分层二次规划的控制策略。结合深度学习与标准图像处理算法，实现了对场景中果实及树干的检测与追踪。通过真实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。