A Monocular Vision Obstacle Avoidance Method Applied to Indoor Tracking Robot

Remote-Controlled Method with Force and Visual Assists Based on Time to Collision for Mobile Robot

Various remote-controlled methods have been developed to improve operability using force or visual assists; however, using only force or visual assists may deteriorate the operability or safety performance. Therefore, a remote-controlled method with both force and visual assists is proposed to improve the operability while maintaining safety performance. The proposed remote-controlled system consists of a wheeled mobile robot, control device, and monitor. The force assist is generated using the time to collision (TTC), which is the predicted time of collision of the mobile robot against an obstacle. This force assist is applied to the operator using a control device to achieve collision avoidance. Using a visual assist, a predicted trajectory for the mobile robot based on the TTC is generated. For operability improvement, this predicted trajectory with color gradation is shown on the monitor. In summary, the achievement of operability improvement while maintaining safety performance is confirmed from experimental results using the proposed method.

A 3D Vision Cone Based Method for Collision Free Navigation of a Quadcopter UAV among Moving Obstacles

In the near future, it’s expected that unmanned aerial vehicles (UAVs) will become ubiquitous surrogates for human-crewed vehicles in the field of border patrol, package delivery, etc. Therefore, many three-dimensional (3D) navigation algorithms based on different techniques, e.g., model predictive control (MPC)-based, navigation potential field-based, sliding mode control-based, and reinforcement learning-based, have been extensively studied in recent years to help achieve collision-free navigation. The vast majority of the 3D navigation algorithms perform well when obstacles are sparsely spaced, but fail when facing crowd-spaced obstacles, which causes a potential threat to UAV operations. In this paper, a 3D vision cone-based reactive navigation algorithm is proposed to enable small quadcopter UAVs to seek a path through crowd-spaced 3D obstacles to the destination without collisions. The proposed algorithm is simulated in MATLAB with different 3D obstacles settings to demonstrate its feasibility and compared with the other two existing 3D navigation algorithms to exhibit its superiority. Furthermore, a modified version of the proposed algorithm is also introduced and compared with the initially proposed algorithm to lay the foundation for future work.