1,140 research outputs found
A review of aerial manipulation of small-scale rotorcraft unmanned robotic systems
Small-scale rotorcraft unmanned robotic systems (SRURSs) are a kind of unmanned rotorcraft with manipulating devices. This review aims to provide an overview on aerial manipulation of SRURSs nowadays and promote relative research in the future. In the past decade, aerial manipulation of SRURSs has attracted the interest of researchers globally. This paper provides a literature review of the last 10 years (2008–2017) on SRURSs, and details achievements and challenges. Firstly, the definition, current state, development, classification, and challenges of SRURSs are introduced. Then, related papers are organized into two topical categories: mechanical structure design, and modeling and control. Following this, research groups involved in SRURS research and their major achievements are summarized and classified in the form of tables. The research groups are introduced in detail from seven parts. Finally, trends and challenges are compiled and presented to serve as a resource for researchers interested in aerial manipulation of SRURSs. The problem, trends, and challenges are described from three aspects. Conclusions of the paper are presented, and the future of SRURSs is discussed to enable further research interests
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
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Automated tracking and grasping of a moving object with a robotic hand-eye system
An attempt to achieve a high level of interaction between a real-time vision system capable of tracking moving objects in 3-D and a robot arm with gripper that can be used to pick up a moving object is described. The interplay of hand-eye coordination in dynamic grasping tasks such as grasping of parts on a moving conveyor system, assembly of articulated parts, or for grasping from a mobile robotic system is explored. The goal is to build an integrated sensing and actuation system that can operate in dynamic as opposed to static environments. The system built addresses three distinct problems in using robotic hand-eye coordination for grasping moving objects: fast computation of 3-D motion parameters from vision, predictive control of a moving robotic arm to track a moving object, and interception and grasping. The system operates at approximately human arm movement rates. Experimental results in which a moving model train is tracked, stably grasped, and picked up by the system are presented. The algorithms developed to relate sensing to actuation are quite general and applicable to a variety of complex robotic tasks
Reactive Base Control for On-The-Move Mobile Manipulation in Dynamic Environments
We present a reactive base control method that enables high performance
mobile manipulation on-the-move in environments with static and dynamic
obstacles. Performing manipulation tasks while the mobile base remains in
motion can significantly decrease the time required to perform multi-step
tasks, as well as improve the gracefulness of the robot's motion. Existing
approaches to manipulation on-the-move either ignore the obstacle avoidance
problem or rely on the execution of planned trajectories, which is not suitable
in environments with dynamic objects and obstacles. The presented controller
addresses both of these deficiencies and demonstrates robust performance of
pick-and-place tasks in dynamic environments. The performance is evaluated on
several simulated and real-world tasks. On a real-world task with static
obstacles, we outperform an existing method by 48\% in terms of total task
time. Further, we present real-world examples of our robot performing
manipulation tasks on-the-move while avoiding a second autonomous robot in the
workspace. See https://benburgesslimerick.github.io/MotM-BaseControl for
supplementary materials
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
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