632 research outputs found
Autonomous Sweet Pepper Harvesting for Protected Cropping Systems
In this letter, we present a new robotic harvester (Harvey) that can
autonomously harvest sweet pepper in protected cropping environments. Our
approach combines effective vision algorithms with a novel end-effector design
to enable successful harvesting of sweet peppers. Initial field trials in
protected cropping environments, with two cultivar, demonstrate the efficacy of
this approach achieving a 46% success rate for unmodified crop, and 58% for
modified crop. Furthermore, for the more favourable cultivar we were also able
to detach 90% of sweet peppers, indicating that improvements in the grasping
success rate would result in greatly improved harvesting performance
Panoptic Mapping with Fruit Completion and Pose Estimation for Horticultural Robots
Monitoring plants and fruits at high resolution play a key role in the future
of agriculture. Accurate 3D information can pave the way to a diverse number of
robotic applications in agriculture ranging from autonomous harvesting to
precise yield estimation. Obtaining such 3D information is non-trivial as
agricultural environments are often repetitive and cluttered, and one has to
account for the partial observability of fruit and plants. In this paper, we
address the problem of jointly estimating complete 3D shapes of fruit and their
pose in a 3D multi-resolution map built by a mobile robot. To this end, we
propose an online multi-resolution panoptic mapping system where regions of
interest are represented with a higher resolution. We exploit data to learn a
general fruit shape representation that we use at inference time together with
an occlusion-aware differentiable rendering pipeline to complete partial fruit
observations and estimate the 7 DoF pose of each fruit in the map. The
experiments presented in this paper evaluated both in the controlled
environment and in a commercial greenhouse, show that our novel algorithm
yields higher completion and pose estimation accuracy than existing methods,
with an improvement of 41% in completion accuracy and 52% in pose estimation
accuracy while keeping a low inference time of 0.6s in average. Codes are
available at: https://github.com/PRBonn/HortiMapping.Comment: 8 pages, IROS 202
Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control
This paper provides an overview of the current state-of-the-art in selective
harvesting robots (SHRs) and their potential for addressing the challenges of
global food production. SHRs have the potential to increase productivity,
reduce labour costs, and minimise food waste by selectively harvesting only
ripe fruits and vegetables. The paper discusses the main components of SHRs,
including perception, grasping, cutting, motion planning, and control. It also
highlights the challenges in developing SHR technologies, particularly in the
areas of robot design, motion planning and control. The paper also discusses
the potential benefits of integrating AI and soft robots and data-driven
methods to enhance the performance and robustness of SHR systems. Finally, the
paper identifies several open research questions in the field and highlights
the need for further research and development efforts to advance SHR
technologies to meet the challenges of global food production. Overall, this
paper provides a starting point for researchers and practitioners interested in
developing SHRs and highlights the need for more research in this field.Comment: Preprint: to be appeared in Journal of Field Robotic
Graph-based View Motion Planning for Fruit Detection
Crop monitoring is crucial for maximizing agricultural productivity and
efficiency. However, monitoring large and complex structures such as sweet
pepper plants presents significant challenges, especially due to frequent
occlusions of the fruits. Traditional next-best view planning can lead to
unstructured and inefficient coverage of the crops. To address this, we propose
a novel view motion planner that builds a graph network of viable view poses
and trajectories between nearby poses, thereby considering robot motion
constraints. The planner searches the graphs for view sequences with the
highest accumulated information gain, allowing for efficient pepper plant
monitoring while minimizing occlusions. The generated view poses aim at both
sufficiently covering already detected and discovering new fruits. The graph
and the corresponding best view pose sequence are computed with a limited
horizon and are adaptively updated in fixed time intervals as the system
gathers new information. We demonstrate the effectiveness of our approach
through simulated and real-world experiments using a robotic arm equipped with
an RGB-D camera and mounted on a trolley. As the experimental results show, our
planner produces view pose sequences to systematically cover the crops and
leads to increased fruit coverage when given a limited time in comparison to a
state-of-the-art single next-best view planner.Comment: 7 pages, 10 figures, accepted at IROS 202
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