Robotic exoskeletons have drawn much attention recently due to their potential ability to help the stroke and spinal cord injury patients to regain the ability of walking. However, the biggest challenge is the balancing of the exoskeleton and how it can balance is still an open question. Most of the time, patients using such exoskeleton devices require sufficient upper body strength to control upright posture and also manipulate crutches/walking frames to partially support body weight and keep balance. However, high energy cost and the high potential of falling using these devices remains a problem. In this paper, the issues are tackled by virtue of a proposed balance stabilizer mechanism which is able to provide active balance assistance for robotic exoskeletons. The design of a robotic exoskeleton together with balance stabilizer mechanism will be presented and discussed. In addition, a trajectory generation method, which can generate dynamically stable and tunable gait pattern, will also be shown. Finally, clinical trial results with a tetraplegia subject is presented and discussed.

A two-dimensional perching model is proposed first for perching with quadrotors. Then a perching mechanism by means of grasping is designed based on the model. The kinematic specifications of the perching mechanism are optimized to maximize the force transfer ratio so that sufficient grasping force can be generated for reliable perching. A controller of the gripper based on the control strategy from previous development is designed for autonomous perching with a quadrotor. Experiments on the grasping capability and reliability of the mechanism and its effectiveness with the controller for autonomous perching are conducted. Results show that the perching mechanism can generate sufficient grasping force and achieve autonomous perching to a target pole with a quadrotor both effectively and reliably.

Gait pattern planning is an important issue in robotic gait rehabilitation. Gait pattern is known to be related to gait parameters, such as cadence, stride length, and walking speed. Thus, prior before the discussion of gait pattern planning, the planning of gait parameters for natural walking should be addressed. This work utilizes multi-layer perceptron neural network (MLPNN) to predict natural gait parameters for a given subject. The inputs of the MLPNN are age, gender, body height, and body weight of the targeted subject. The MLPNN is trained to output a suitable walking speed and cadence for given subject. Two MLPNNs are trained to study the efficiency and accuracy in predicting the desired outputs, for two different setups. First setup is that the MLPNN is trained specifically for slow speed condition only. In second setup, the MLPNN is trained for both slow and normal speed conditions. The results of the MLPNNs are presented in this paper. The efficiency and accuracy of the MLPNNs are discussed.