Résumé
Interest in agricultural robotics has significantly increased, due to the potential benefits of improved productivity and labor reduction. Yet, developing robotic harvesters for unstructured environments presents many challenges for robot perception, planning and action. Here, we propose a dual-arm approach to fruit harvesting. Our dual-arm fruit harvesting robots are equipped with an RGB-D camera, a cutting and a collecting tool. The vision system combines fruit detection and tracking. We detect branches and the tree trunk as obstacles, to ensure efficient and safe harvesting. To speed up the process, we implement a scheduling algorithm based on the traveling salesperson problem (TSP) to minimize the distance required to harvest all visible fruits. Finally, we use global motion planning to ensure collision-free operation. We validate our methods using two distinct dual-arm robots: Baxter, a cost-effective and widely available option, and BAZAR, a custom high precision dualarm platform. Furthermore, our experiments are conducted on two types of fruit: apples and oranges. In lab trials, the systems harvested 5 oranges and 6 apples, with average perfruit times of 20.6 s and 8.2 s and no collisions. Perception achieved mAP@50 = 0.886 on a 3,600-image dataset, MOTA = 89.8% with 3 ID switches, and 3D localization MAE of 7.8-21.1 mm. The results demonstrate the effectiveness of our dual-arm approach increasing adaptability of robotic fruit harvesting. The results demonstrate the effectiveness of our dual-arm approach in enhancing the efficiency and adaptability of robotic fruit harvesting.