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GO-VMP: Global Optimization for View Motion Planning in Fruit Mapping
EvidMTL: Evidential Multi-Task Learning for Uncertainty-Aware Semantic Surface Mapping from Monocular RGB Images
Safe Leaf Manipulation for Accurate Shape and Pose Estimation of Occluded Fruits
Safe Multi-Agent Reinforcement Learning for Behavior-Based Cooperative Navigation
DM-OSVP++: One-Shot View Planning Using 3D Diffusion Models for Active RGB-Based Object Reconstruction
DawnIK: Decentralized Collision-Aware Inverse Kinematics Solver for Heterogeneous Multi-Arm Systems
Fruit Tracking Over Time Using High-Precision Point Clouds
Graph-based View Motion Planning for Fruit Detection
NBV-SC: Next Best View Planning based on Shape Completion for Fruit Mapping and Reconstruction
Deep Reinforcement Learning for Next-Best-View Planning in Agricultural Applications
Fruit Mapping with Shape Completion for Autonomous Crop Monitoring
Viewpoint Planning for Fruit Size and Position Estimation
PATHoBot: A Robot for Glasshouse Crop Phenotyping and Intervention.
Online Object-Oriented Semantic Mapping and Map Updating
Combining Local and Global Viewpoint Planning for Fruit Coverage
Gradient and Log-based Active Learning for Semantic Segmentation of Crop and Weed for Agricultural Robots
Exploiting Priors from 3D Diffusion Models for RGB-Based One-Shot View Planning
HortiBot: An Adaptive Multi-Arm System for Robotic Horticulture of Sweet Peppers
Horticultural tasks such as pruning and selective harvesting are labor intensive and horticultural staff are hard to find. Automating these tasks is challenging due to the semi-structured greenhouse workspaces, changing environmental conditions such as lighting, dense plant growth with many occlusions, and the need for gentle manipulation of non-rigid plant organs. In this work, we present the three-armed system HortiBot, with two arms for manipulation and a third arm as an articulated head for active perception using stereo cameras. Its perception system detects not only peppers, but also peduncles and stems in real time, and performs online data association to build a world model of pepper plants. Collision-aware online trajectory generation allows all three arms to safely track their respective targets for observation, grasping, and cutting. We integrated perception and manipulation to perform selective harvesting of peppers and evaluated the system in lab experiments. Using active perception coupled with end-effector force torque sensing for compliant manipulation, HortiBot achieves high success rates.