2,671 research outputs found
RoboTSP - A Fast Solution to the Robotic Task Sequencing Problem
In many industrial robotics applications, such as spot-welding,
spray-painting or drilling, the robot is required to visit successively
multiple targets. The robot travel time among the targets is a significant
component of the overall execution time. This travel time is in turn greatly
affected by the order of visit of the targets, and by the robot configurations
used to reach each target. Therefore, it is crucial to optimize these two
elements, a problem known in the literature as the Robotic Task Sequencing
Problem (RTSP). Our contribution in this paper is two-fold. First, we propose a
fast, near-optimal, algorithm to solve RTSP. The key to our approach is to
exploit the classical distinction between task space and configuration space,
which, surprisingly, has been so far overlooked in the RTSP literature. Second,
we provide an open-source implementation of the above algorithm, which has been
carefully benchmarked to yield an efficient, ready-to-use, software solution.
We discuss the relationship between RTSP and other Traveling Salesman Problem
(TSP) variants, such as the Generalized Traveling Salesman Problem (GTSP), and
show experimentally that our method finds motion sequences of the same quality
but using several orders of magnitude less computation time than existing
approaches.Comment: 6 pages, 7 figures, 1 tabl
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
Collision-free inverse kinematics of the redundant seven-link manipulator used in a cucumber picking robot
The paper presents results of research on an inverse kinematics algorithm that has been used in a functional model of a cucumber-harvesting robot consisting of a redundant P6R manipulator. Within a first generic approach, the inverse kinematics problem was reformulated as a non-linear programming problem and solved with a Genetic Algorithm (GA). Although solutions were easily obtained, the considerable calculation time needed to solve the problem prevented on-line implementation. To circumvent this problem, a second, less generic, approach was developed which consisted of a mixed numerical-analytic solution of the inverse kinematics problem exploiting the particular structure of the P6R manipulator. Using the latter approach, calculation time was considerably reduced. During the early stages of the cucumber-harvesting project, this inverse kinematics algorithm was used off-line to evaluate the ability of the robot to harvest cucumbers using 3D-information obtained from a cucumber crop in a real greenhouse. Thereafter, the algorithm was employed successfully in a functional model of the cucumber harvester to determine if cucumbers were hanging within the reachable workspace of the robot and to determine a collision-free harvest posture to be used for motion control of the manipulator during harvesting. The inverse kinematics algorithm is presented and demonstrated with some illustrative examples of cucumber harvesting, both off-line during the design phase as well as on-line during a field test
Robots in Agriculture: State of Art and Practical Experiences
The presence of robots in agriculture has grown significantly in recent years, overcoming some of the challenges and complications of this field. This chapter aims to collect a complete and recent state of the art about the application of robots in agriculture. The work addresses this topic from two perspectives. On the one hand, it involves the disciplines that lead the automation of agriculture, such as precision agriculture and greenhouse farming, and collects the proposals for automatizing tasks like planting and harvesting, environmental monitoring and crop inspection and treatment. On the other hand, it compiles and analyses the robots that are proposed to accomplish these tasks: e.g. manipulators, ground vehicles and aerial robots. Additionally, the chapter reports with more detail some practical experiences about the application of robot teams to crop inspection and treatment in outdoor agriculture, as well as to environmental monitoring in greenhouse farming
TossingBot: Learning to Throw Arbitrary Objects with Residual Physics
We investigate whether a robot arm can learn to pick and throw arbitrary
objects into selected boxes quickly and accurately. Throwing has the potential
to increase the physical reachability and picking speed of a robot arm.
However, precisely throwing arbitrary objects in unstructured settings presents
many challenges: from acquiring reliable pre-throw conditions (e.g. initial
pose of object in manipulator) to handling varying object-centric properties
(e.g. mass distribution, friction, shape) and dynamics (e.g. aerodynamics). In
this work, we propose an end-to-end formulation that jointly learns to infer
control parameters for grasping and throwing motion primitives from visual
observations (images of arbitrary objects in a bin) through trial and error.
Within this formulation, we investigate the synergies between grasping and
throwing (i.e., learning grasps that enable more accurate throws) and between
simulation and deep learning (i.e., using deep networks to predict residuals on
top of control parameters predicted by a physics simulator). The resulting
system, TossingBot, is able to grasp and throw arbitrary objects into boxes
located outside its maximum reach range at 500+ mean picks per hour (600+
grasps per hour with 85% throwing accuracy); and generalizes to new objects and
target locations. Videos are available at https://tossingbot.cs.princeton.eduComment: Summary Video: https://youtu.be/f5Zn2Up2RjQ Project webpage:
https://tossingbot.cs.princeton.ed
Right Place, Right Time:Proactive Multi-Robot Task Allocation Under Spatiotemporal Uncertainty
For many multi-robot problems, tasks are announced during execution, where task announcement times and locations are uncertain. To synthesise multi-robot behaviour that is robust to early announcements and unexpected delays, multi-robot task allocation methods must explicitly model the stochastic processes that govern task announcement. In this paper, we model task announcement using continuous-time Markov chains which predict when and where tasks will be announced. We then present a task allocation framework which uses the continuous-time Markov chains to allocate tasks proactively, such that robots are near or at the task location upon its announcement. Our method seeks to minimise the expected total waiting duration for each task, i.e. the duration between task announcement and a robot beginning to service the task. Our framework can be applied to any multi-robot task allocation problem where robots complete spatiotemporal tasks which are announced stochastically. We demonstrate the efficacy of our approach in simulation, where we outperform baselines which do not allocate tasks proactively, or do not fully exploit our task announcement models
Robotic Crop Interaction in Agriculture for Soft Fruit Harvesting
Autonomous tree crop harvesting has been a seemingly attainable, but elusive, robotics goal for the past several decades. Limiting grower reliance on uncertain seasonal labour is an economic driver of this, but the ability of robotic systems to treat each plant individually also has environmental benefits, such as reduced emissions and fertiliser use. Over the same time period, effective grasping and manipulation (G&M) solutions to warehouse product handling, and more general robotic interaction, have been demonstrated.
Despite research progress in general robotic interaction and harvesting of some specific crop types, a commercially successful robotic harvester has yet to be demonstrated. Most crop varieties, including soft-skinned fruit, have not yet been addressed. Soft fruit, such as plums, present problems for many of the techniques employed for their more robust relatives and require special focus when developing autonomous harvesters. Adapting existing robotics tools and techniques to new fruit types, including soft skinned varieties, is not well explored. This thesis aims to bridge that gap by examining the challenges of autonomous crop interaction for the harvesting of soft fruit.
Aspects which are known to be challenging include mixed obstacle planning with both hard and soft obstacles present, poor outdoor sensing conditions, and the lack of proven picking motion strategies. Positioning an actuator for harvesting requires solving these problems and others specific to soft skinned fruit. Doing so effectively means addressing these in the sensing, planning and actuation areas of a robotic system. Such areas are also highly interdependent for grasping and manipulation tasks, so solutions need to be developed at the system level.
In this thesis, soft robotics actuators, with simplifying assumptions about hard obstacle planes, are used to solve mixed obstacle planning. Persistent target tracking and filtering is used to overcome challenging object detection conditions, while multiple stages of object detection are applied to refine these initial position estimates. Several picking motions are developed and tested for plums, with varying degrees of effectiveness. These various techniques are integrated into a prototype system which is validated in lab testing and extensive field trials on a commercial plum crop.
Key contributions of this thesis include
I. The examination of grasping & manipulation tools, algorithms, techniques and challenges for harvesting soft skinned fruit
II. Design, development and field-trial evaluation of a harvester prototype to validate these concepts in practice, with specific design studies of the gripper type, object detector architecture and picking motion for this
III. Investigation of specific G&M module improvements including:
o Application of the autocovariance least squares (ALS) method to noise covariance matrix estimation for visual servoing tasks, where both simulated and real experiments demonstrated a 30% improvement in state estimation error using this technique.
o Theory and experimentation showing that a single range measurement is sufficient for disambiguating scene scale in monocular depth estimation for some datasets.
o Preliminary investigations of stochastic object completion and sampling for grasping, active perception for visual servoing based harvesting, and multi-stage fruit localisation from RGB-Depth data.
Several field trials were carried out with the plum harvesting prototype. Testing on an unmodified commercial plum crop, in all weather conditions, showed promising results with a harvest success rate of 42%. While a significant gap between prototype performance and commercial viability remains, the use of soft robotics with carefully chosen sensing and planning approaches allows for robust grasping & manipulation under challenging conditions, with both hard and soft obstacles
Fast path planning for precision weeding
Agricultural robots have the potential to reduce herbicide use in agriculture and horticulture through autonomous precision weeding. One of the main challenges is how to efficiently plan paths for a robot arm such that many individual weeds can be processed quickly. This paper considers an abstract weeding task among obstacles and proposes an efficient online path planning algorithm for an industrial manipulator mounted to a mobile robot chassis. The algorithm is based on a multi-query approach, inspired by industrial bin-picking, where a database of high-quality paths is computed offline and paths are then selected and adapted online. We present a preliminary implementation using a 6-DOF arm and report results from simulation experiments designed to evaluate system performance with varying database and obstacle sizes. We also validate the approach using a Universal Robots UR5 manipulator and ROS interface
- …