51 research outputs found
On Aerial Robots with Grasping and Perching Capabilities: A Comprehensive Review
Over the last decade, there has been an increased interest in developing aerial robotic platforms that exhibit grasping and perching capabilities not only within the research community but also in companies across different industry sectors. Aerial robots range from standard multicopter vehicles/drones, to autonomous helicopters, and fixed-wing or hybrid devices. Such devices rely on a range of different solutions for achieving grasping and perching. These solutions can be classified as: 1) simple gripper systems, 2) arm-gripper systems, 3) tethered gripping mechanisms, 4) reconfigurable robot frames, 5) adhesion solutions, and 6) embedment solutions. Grasping and perching are two crucial capabilities that allow aerial robots to interact with the environment and execute a plethora of complex tasks, facilitating new applications that range from autonomous package delivery and search and rescue to autonomous inspection of dangerous or remote environments. In this review paper, we present the state-of-the-art in aerial grasping and perching mechanisms and we provide a comprehensive comparison of their characteristics. Furthermore, we analyze these mechanisms by comparing the advantages and disadvantages of the proposed technologies and we summarize the significant achievements in these two research topics. Finally, we conclude the review by suggesting a series of potential future research directions that we believe that are promising
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Automated re-prefabrication system for buildings using robotics
Prefabrication has the advantages of simplicity, speed and economy but has been inflexible to changes in design
which is a primary reason behind its limited market share in the construction industry. To tackle this drawback,
this study presents a Robotic Prefabrication System (RPS) which employs a new concept called “re-fabrication”:
the automatic disassembly of a prefabricated structure and its reconstruction according to a new design. The RPS
consists of a software module and a hardware module. First, the software employs the 3D model of a prefabricated
structure as input, and returns motor control command output to the hardware. There are two underlying
algorithms developed in the software module. First, a novel algorithm automatically compares the old
and new models and identifies the components which the two models do not have in common in order to enable
disassembly of the original structure and its refabrication into the new design. In addition, an additional novel
algorithm computes the optimal refabrication sequence to transform one model into another according to the
differences identified. Meanwhile, the hardware module takes the motor control commands as input and executes
the appropriate assembly/disassembly operations, and returns the desired refabricated structure in realtime.
Validation tests on two lab-scaled prefabricated structures demonstrate that the system successfully generated
the desired refabrication sequences and performed all assembly operations with acceptable placement
precision
Versatile Multilinked Aerial Robot with Tilting Propellers: Design, Modeling, Control and State Estimation for Autonomous Flight and Manipulation
Multilinked aerial robot is one of the state-of-the-art works in aerial
robotics, which demonstrates the deformability benefiting both maneuvering and
manipulation. However, the performance in outdoor physical world has not yet
been evaluated because of the weakness in the controllability and the lack of
the state estimation for autonomous flight. Thus we adopt tilting propellers to
enhance the controllability. The related design, modeling and control method
are developed in this work to enable the stable hovering and deformation.
Furthermore, the state estimation which involves the time synchronization
between sensors and the multilinked kinematics is also presented in this work
to enable the fully autonomous flight in the outdoor environment. Various
autonomous outdoor experiments, including the fast maneuvering for interception
with target, object grasping for delivery, and blanket manipulation for
firefighting are performed to evaluate the feasibility and versatility of the
proposed robot platform. To the best of our knowledge, this is the first study
for the multilinked aerial robot to achieve the fully autonomous flight and the
manipulation task in outdoor environment. We also applied our platform in all
challenges of the 2020 Mohammed Bin Zayed International Robotics Competition,
and ranked third place in Challenge 1 and sixth place in Challenge 3
internationally, demonstrating the reliable flight performance in the fields
Cooperative transport in swarm robotics. Multi object transportation
Swarm robotics is a research field inspired from the natural behavior of ants, bees or fish in their natural habitat. Each group display swarm behavior in different ways. For example, ants use pheromones to trace one another in order to find a nest, reach a food source or do any operation, while bees use dance moves to attract one another to the desired place. In swarm robotics, small robots attempt to mimic insect behavior. The robotic swarm group collaborate to perform a task and collectively solve a given problem. In the process, the robots use the sensors they are equipped with to move, communicate or avoid obstacles until they collectively do the desired functionality. In this thesis, we propose a modification to the Robotic Darwinian Particle Swarm Optimization (RDPSO) algorithm. In the RDPSO, robots deployed in a rescue operation, transport one object at a time to a desired safe place. In our algorithm, we simultaneously transport multiple objects to safety. We call our algorithm Multi Robotics Darwinian Particle Swarm Optimization (MRDPSO). Our algorithm is developed and implemented on a VREP simulator using ePuck robots as swarm members. We test our algorithm using two different environment sizes complete with obstacles. First implementation is for two simultaneous object transported but can be extended to more than two. We compare our new algorithm to the results of single RDPSO and found our algorithm to be 35 to 41 % faster. We also compared our results to those obtained from three selected papers that are Ghosh, Konar, and Janarthanan [1], TORABI [2], and Kube and Bonabeau [3]. The performance measures we compare to are the accuracy of transporting all objects to desired location, and the time efficiency of transporting all the objects in our new system
Tracking and Grasping of Moving Objects Using Aerial Robotic Manipulators: A Brief Survey
Unmanned Aerial Vehicles (UAV) has evolved in recent years, their features have changed to be more useful to the society, although some years ago the drones had been thought to be teleoperated by humans and to take some pictures from above, which is useful; nevertheless, nowadays the drones are capable of developing autonomous tasks like tracking a dynamic target or even grasping different kind of objects. Some task like transporting heavy loads or manipulating complex shapes are more challenging for a single UAV, but for a fleet of them might be easier. This brief survey presents a compilation of relevant works related to tracking and grasping with aerial robotic manipulators, as well as cooperation among them. Moreover, challenges and limitations are presented in order to contribute with new areas of research. Finally, some trends in aerial manipulation are foreseeing for different sectors and relevant features for these kind of systems are standing out
Hand-worn Haptic Interface for Drone Teleoperation
Drone teleoperation is usually accomplished using remote radio controllers,
devices that can be hard to master for inexperienced users. Moreover, the
limited amount of information fed back to the user about the robot's state,
often limited to vision, can represent a bottleneck for operation in several
conditions. In this work, we present a wearable interface for drone
teleoperation and its evaluation through a user study. The two main features of
the proposed system are a data glove to allow the user to control the drone
trajectory by hand motion and a haptic system used to augment their awareness
of the environment surrounding the robot. This interface can be employed for
the operation of robotic systems in line of sight (LoS) by inexperienced
operators and allows them to safely perform tasks common in inspection and
search-and-rescue missions such as approaching walls and crossing narrow
passages with limited visibility conditions. In addition to the design and
implementation of the wearable interface, we performed a systematic study to
assess the effectiveness of the system through three user studies (n = 36) to
evaluate the users' learning path and their ability to perform tasks with
limited visibility. We validated our ideas in both a simulated and a real-world
environment. Our results demonstrate that the proposed system can improve
teleoperation performance in different cases compared to standard remote
controllers, making it a viable alternative to standard Human-Robot Interfaces.Comment: Accepted at the IEEE International Conference on Robotics and
Automation (ICRA) 202
Path Planning of Mobile Agents using AI Technique
In this paper, we study coordinated motion in a swarm robotic system, called a swarm-bot. A swarm-bot is a self-assembling and self-organizing. Artifact composed of a swarm of s-bots, mobile robots with the ability to connect to and is connect from each other. The swarm-bot concept is particularly suited for tasks that require all-terrain navigation abilities, such as space exploration or rescue in collapsed buildings. As a first step toward the development of more complex control strategies, we investigate the case in which a swarm-bot has to explore an arena while avoiding falling into holes.
In such a scenario, individual s-bots have sensory–motor limitations that prevent them navigating efficiently. These limitations can be overcome if the s-bots are made to cooperate. In particular, we exploit the s-bots’ ability to physically connect to each other. In order to synthesize the s-bots’ controller, we rely on artificial evolution, which we show to be a powerful tool for the production of simple and effective solutions to the hole avoidance task
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