Multicoptor unmanned aerial vehicles (UAVs) are popular robotics platforms in various research and applications fields. Research in robotics, control, estimation and computer vision relies heavily on open-source softw...
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ISBN:
(纸本)9783030112929;9783030112912
Multicoptor unmanned aerial vehicles (UAVs) are popular robotics platforms in various research and applications fields. Research in robotics, control, estimation and computer vision relies heavily on open-source software and hardware to build custom UAV. This is motivated by lower cost of material and rapid development desire. The presented tool automates the task of obtaining realistic models for simulation and visualization of multicoptors using state-of-the-art Computer Aided Design engineering tools (CAD). Users interact with the software through a desktop application that offers interface to CAD tools, hardware database and simulation files generation. Custom models can be generated for three popular multirotor simulators. Modeling parameters accuracy has been validated using data of IRIS+ quadcopter model.
An assistive robotic system is designed in this research for object manipulation to aid people with physical disabilities. This robotic arm design is imported to a simulated environment and tested in a virtual world. ...
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An assistive robotic system is designed in this research for object manipulation to aid people with physical disabilities. This robotic arm design is imported to a simulated environment and tested in a virtual world. The research included the development of a versatile design and testing platform for robotic applications with joint torque requirements, workspace restrictions, and control tuning parameters. Live user inputs and camera feeds are used to test the movement of the robot in the virtual environment. The simulations run on the mounted single board computer picked for the assistive robot, meaning computational power and speed are also verified. A semi-autonomous hybrid control system is used to generate commands using a limited range of inputs. Having few input messages and an intuitive user interface reduces the amount of training required for a user to operate the system. The simulated environment allows for user testing of individual subsystems of the assistive robot, which is useful when defining which actions to automate for a specific application. Live BCI commands from a trained user were successfully used to pick a goal point from 3D point cloud data and calculate the goal position of the robots' mobile base, placing the goal point in the robot arms workspace. The motion of the robot using this goal location approach is also verified. The motion of joints in the simulated environment is graphed and used for tuning and calculating dynamic loads on selected joints. The design and testing platform created allows for mechanical design changes to be easily made for a desired motion profile or a desired load capacity. Errors due to imperfect modeling and hardware implementation will have to be considered when creating more tests for the physical build of the assistive robot.
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