P300 Brain–Computer Interface-Based Drone Control in Virtual and Augmented Reality

Since the emergence of head-mounted displays (HMDs), researchers have attempted to introduce virtual and augmented reality (VR, AR) in brain–computer interface (BCI) studies. However, there is a lack of studies that incorporate both AR and VR to compare the performance in the two environments. Therefore, it is necessary to develop a BCI application that can be used in both VR and AR to allow BCI performance to be compared in the two environments. In this study, we developed an opensource-based drone control application using P300-based BCI, which can be used in both VR and AR. Twenty healthy subjects participated in the experiment with this application. They were asked to control the drone in two environments and filled out questionnaires before and after the experiment. We found no significant (p > 0.05) difference in online performance (classification accuracy and amplitude/latency of P300 component) and user experience (satisfaction about time length, program, environment, interest, difficulty, immersion, and feeling of self-control) between VR and AR. This indicates that the P300 BCI paradigm is relatively reliable and may work well in various situations.

Ajr Drone Control using the Coupling of the PID Controller and Genetic Algorithm

Due to their strong abilities and easy usage, unmanned aerial vehicles (UAVs) commonly named drones have found a place and merged in the different industrial sectors. These varieties of applications encourage researchers to search for new control algorithms that make drones operate smoothly. In this regard, the present study mains to design a PID controller for four motors drones (quadcopter). For this purpose, a state-space representation of the drone is proposed based on Newton Euler's formularies for a rigid body. For better performance of the control algorithm (PID) the parameters Kp, Ki, and Kd for the controller are established using an optimization search schema based on a genetic algorithm. Various simulations were performed to test the proposed idea.

Enabling Reliable UAV Control by Utilizing Multiple Protocols and Paths for Transmitting Duplicated Control Packets

In the last ten years, supported by the advances in technologies for unmanned aerial vehicles (UAVs), UAVs have developed rapidly and are utilized for a wide range of applications. To operate UAVs safely, by exchanging control packets continuously, operators should be able to monitor UAVs in real-time and deal with any problems immediately. However, due to any networking problems or unstable wireless communications, control packets can be lost or transmissions can be delayed, which causes the unstable drone control. To overcome this limitation, in this paper, we propose MuTran for enabling reliable UAV control. MuTran considers the packet type and duplicates only control packets, not data packets. After that, MuTran transmits the original and duplicate packets through multiple protocols and paths to improve the reliability of control packet transmissions. We designed MuTran and conducted a lot of theoretical analyses to demonstrate the validity of MuTran and analyze it from various aspects. We implemented MuTran on real devices and evaluated MuTran using the devices. We conducted experiments to verify the limitations of the existing systems and demonstrate that control packets can be transmitted more stably by using MuTran. Through the analysis and experimental results, we confirmed that MuTran reduces the control packet transfer delay, which improves the reliability and stability of controlling UAVs.

Drone Control Using EEG

Artificial Intelligence and automation is the future of the world, and with continuous reduction in human effort, The project aims to develop a drone that is controlled entirely by brain waves, based on Brain-Computer Interfaces (BCI). This interface is possible using EEG. Electroencephalography (EEG) is a diagnostic test and monitoring method used to record electrical activities in the brain. The EEG has electrodes in the form of small, metal discs that are attached to the person’s scalp, these detect the changes and abnormalities in the brain waves which are in the form of electrical signals. The received signals are passed through filters and different operations to extract suitable, operational signal features which are segregated on various parameters outlined for drone controls, are fed to the ML model which will classify the input, to be trained repeatedly to properly guide the drone. The desired feature results are delivered to the drone through Arduino. A drone controlled with brain waves is useful during search and rescue operations providing critical information from aerial points. A similar BCI application can be used in bionic prosthetics so that a person lacking a limb may be able to control a prosthetic simply using an EEG and EMG interface. There are limitless applications in human-to-machine interaction that will reduce the need for physical input.