2 research outputs found
MorphoArms: Morphogenetic Teleoperation of Multimanual Robot
Nowadays, there are few unmanned aerial vehicles (UAVs) capable of flying,
walking and grasping. A drone with all these functionalities can significantly
improve its performance in complex tasks such as monitoring and exploring
different types of terrain, and rescue operations. This paper presents
MorphoArms, a novel system that consists of a morphogenetic chassis and a hand
gesture recognition teleoperation system. The mechanics, electronics, control
architecture, and walking behavior of the morphogenetic chassis are described.
This robot is capable of walking and grasping objects using four robotic limbs.
Robotic limbs with four degrees-of-freedom are used as pedipulators when
walking and as manipulators when performing actions in the environment. The
robot control system is implemented using teleoperation, where commands are
given by hand gestures. A motion capture system is used to track the user's
hands and to recognize their gestures. The method of controlling the robot was
experimentally tested in a study involving 10 users. The evaluation included
three questionnaires (NASA TLX, SUS, and UEQ). The results showed that the
proposed system was more user-friendly than 56% of the systems, and it was
rated above average in terms of attractiveness, stimulation, and novelty.Comment: IEEE International Conference on Automation Science and Engineering
(CASE 2023), Cordis, New Zeland, 26-30 August, 2023, in prin
MorphoLander: Reinforcement Learning Based Landing of a Group of Drones on the Adaptive Morphogenetic UAV
This paper focuses on a novel robotic system MorphoLander representing
heterogeneous swarm of drones for exploring rough terrain environments. The
morphogenetic leader drone is capable of landing on uneven terrain, traversing
it, and maintaining horizontal position to deploy smaller drones for extensive
area exploration. After completing their tasks, these drones return and land
back on the landing pads of MorphoGear. The reinforcement learning algorithm
was developed for a precise landing of drones on the leader robot that either
remains static during their mission or relocates to the new position. Several
experiments were conducted to evaluate the performance of the developed landing
algorithm under both even and uneven terrain conditions. The experiments
revealed that the proposed system results in high landing accuracy of 0.5 cm
when landing on the leader drone under even terrain conditions and 2.35 cm
under uneven terrain conditions. MorphoLander has the potential to
significantly enhance the efficiency of the industrial inspections, seismic
surveys, and rescue missions in highly cluttered and unstructured environments.Comment: Accepted paper at the 2023 IEEE Conference on Systems, Man, and
Cybernetic