Researchers have given attention to lower limb exoskeletons in recent years. Lower limb exoskeletons have been designed, prototype tested through experiments, and even produced. In general, lower limb exoskeletons have two different objectives: (1) rehabilitation and (2) assisting human work activities. Referring to these objectives, researchers have iteratively improved lower limb exoskeleton designs, especially in the location of actuators. Some of these devices use actuators, particularly on hips, ankles or knees of the users. Additionally, other devices employ a combination of actuators on multiple joints. In order to provide information about which actuator location is more suitable; a review study on the design of actuator locations is presented in this paper. The location of actuators is an important factor because it is related to the analysis of the design and the control system. This factor affects the entire lower limb exoskeleton’s performance and functionality. In addition, the disadvantages of several types of lower limb exoskeletons in terms of actuator locations and the challenges of the lower limb exoskeleton in the future are also presented in this paper.

Studies of object detection have recently attracted increased interest. One of the applications of object detection is robotics. This paper present the real-time object detection integrated to humanoid robot soccer. In order to enhance the vision to detect ball and goal, the You Only Look Once (YOLO) methods is used as deep-learning object detection method. The real-time experiments have been carried out in LINUX OS by using NVIDIA JETSON TX1 controller board. The experimental results show that the proposed method capable to detect and distinguish objects in the different lighting condition, with interference from other objects, also from the different angle of capturing an image.

This paper presents a method to generate assistive torque to a lower limb exoskeleton using a predictive approach. A control scheme is proposed to predict the suitable force of next movement of legs using gait information. By using human walking gait cycle and Center of Pressure (CoP), it is expected that the exoskeleton can provide an assistive force to people and help them to walk normally. In order to predict the next movement for assistive torque given by exoskeleton to the human, a predictive Artificial Neural Network (pANN) is developed. A prototype lower limb exoskeleton has been designed and constructed for experimental study. The controller is implemented by using LabVIEW programming. The experimental results verified that the proposed controller can provide proper assistive torques to hip and knee joints for both legs while wearing the exoskeleton.

During the maneuvering of most unmanned aerial vehicles (UAVs), the GPS is one of the sensors used for navigation. However, this kind of sensor cannot handle indoor navigation applications well. Using a camera might be the answer to performing indoor navigation using its coordinate system. In this study, we considered indoor navigation applications using the ZED2 stereo camera for the quadcopter. To use the ZED 2 camera as a navigation sensor, we first transformed its coordinates into the North, East, down (NED) system to enable the drone to understand its position and maintain stability in a particular position. The experiment was performed using a real-time application to confirm the feasibility of this approach for indoor localization. In the real-time application, we commanded the quadcopter to follow triangular and rectangular paths. The results indicated that the quadcopter was able to follow the paths and maintain its stability in specific coordinate positions.

The study of Unmanned Aerial Vehicles (UAVs) has grown fast recently, and various applications have been developed. This paper presents a design and implementation of attitude control of a co-axial octocopter (CAO). The dynamic model of a CAO is obtained from making the configuration similar to a quadcopter. In order to stabilize the attitude control of CAO, we propose a Fuzzy+PID design of the attitude controller. Both computer simulation and real-time experiments have been carried out by using LabVIEW programming. In the simulation, we investigated the performance of the proposed Fuzzy+PID controller and tuned the controller parameters. NI-MyRio has been chosen to implement the controller onboard the octocopter. The practical experiments show that the proposed Fuzzy+PID controllers can handle the disturbances during flying and have certain robustness against the environmental conditions. The experimental results validate the simulations and verify the effectiveness of the developed controller.

An exoskeleton is a device used for walking rehabilitation. In order to develop a proper rehabilitation exoskeleton, a user’s walking intention needs to be captured as the initial step of work. Moreover, every human has a unique walking gait style. This work introduced a wearable sensor, which aimed to recognize the walking gait phase, as the fundamental step before applying it into the rehabilitation exoskeleton. The sensor used in this work was the IMU sensor, used to recognize the pitch angle generated from the knee joint while the user walks, as information about the walking gait cycle, before doing the investigation on how to identify the walking gait cycle. In order to identify the walking gait cycle, Neural Network has been proposed as a method. The gait cycle identification was generated to recognize the gait cycle on the knee joint. To verify the performance of the proposed method, experiments have been done in real-time application. The experiments were carried out with different processes such as walking on a flat floor, climbing up, and walking down stairs. Five subjects were trained and tested using the system. The experiments showed that the proposed method was able to recognize each gait cycle for all users as they wore the sensor on their knee joints. This study has the potential to be applied on an exoskeleton rehabilitation robot as a further research experiment.