When a robotic manipulator is controlled, computing gravity force of the robot is the primary issue. Exact model parameters are not easy to be known in the practical robot system due to the uncertainty of the robot dynamics. Hence, the gravity force is presented by a combination of gravity regressor and robot dynamic parameters and is compensated by the estimation of uncertain robot dynamic parameters. Previous researches conducted estimation by using transpose of gravity regressor and full form of dynamic parameters which is not general form however this paper estimates the gravity force using the generalized gravity regressor which is regardless of the dimension and structure of the robot under the quasi-static state. Once the estimation is completed, the estimated value can be used to compute the gravitational force and control the robot. It is shown that the generalized decomposition of gravity regressor and estimation process. The results are validated through an experiment by implementing the algorithm on an upper-body dual arm robot.

This paper presents a simple method to control the motion of a quadruped robot in unknown rough terrains using a full dynamic model. First, using typical stiffness control method, the four legs' behavior is approximated to 3D spring damper systems. By this way, we can easily derive the dynamic equations of motion in Cartesian space. Based on these equations, a control strategy is proposed to obtain the asymptotical stability of the system. In addition, a reflex signal is also introduced to control the rotational disturbance of the robot body. Finally, dynamic simulation of a quadruped walking robot is performed on several unknown rough terrains to verify the proposed method.

Improvement of the adaptability of a quadruped robot in rough terrain is studied in this paper. First, the position and posture of the body of the robot are adjusted to maximize the number of choices for foot placement of the next swing leg. The more choices the robot has to select the next suitable foothold, the better it will be to cope with rough terrain. Second, an effective foothold search algorithm is developed. The foothold search algorithm not only tries to find a valid foothold but also tries to maintain high adaptability of the robot. For implementing the algorithm, some new concepts such as potential swing direction, complementary kinematic margin and elliptical set of candidate footholds are also proposed. The effectiveness of this procedure in improving the adaptability of a quadruped robot moving in challenging terrain is verified in both simulation and experiment.

Aiming to real easy application in several quadruped robot platforms, this paper introduces a new method of modeling central pattern generators (CPG) to control quadruped locomotion. Not only can this new model generate all the primary gaits of quadrupeds stably with limit cycle effect, but it also has the ability of tuning the periodic outputs with arbitrary waveforms. The core idea is to combine strong points of two mathematical tools: Fourier series and Recurrent neural networks. In addition, a new biomimetic controller is also introduced using the proposed CPG model and several reflex modules. Finally, dynamic simulations are performed to validate the efficiency of the proposed controller.

In this paper, we propose a method of sensing outdoor environments for a walking and climbing robot. The proposed method ensures the realtime perception of the surface geometry by integrating a range and a gyroscope with a novel arrangement of the sensors. In addition, a gait planning algorithm based on the sensing method is presented. The algorithm runs fast since it is simple, and the rich information from the sensing system helps the robot overcome the real environment, even an extremely complex one. The sensing system is implemented in a quadruped walking robot, called MRWALLSPECT IV and its effectiveness is proved experimentally.

In this paper, we introduce a wall climbing robotic system for visual inspection of man-made structures. The adhesion mechanism of our system consists of an impeller and two-layer suction seals which provide sufficient adhesion forces for supporting the robot body on the non-smooth vertical wall and horizontal ceiling by generating pressure difference between the inside of the pressure chamber and the ambient environment. A comprehensive study is performed on the dynamic fluid modeling of the adhesion mechanism and the adhesion force is controlled by adjusting the pressure inside of the chamber. In addition, stable differential-driving locomotion on non-smooth surface are achieved by adapting a suspension mechanism for each wheel. A wall climbing robot, called LARVA, is successfully developed and its effectiveness of locomotion is verified with experiments on non-smooth vertical wall and horizontal ceiling surface.

In this paper, a new biomimetic control method for a quadruped walking robot is proposed. The method is derived by the observation of the gravity load receptor and stimulus- reaction mechanism of quadrupeds' locomotion, and the study of the stances on walking and energy efficiency. Though the controller is simple, it provides a useful framework for controlling a quadruped walking robot. In particular, by introducing a new rhythmic pattern generator the heavy computational burden to be paid on solving kinematics is relieved. The effectiveness of the proposed method is validated via a dynamic simulation and experimental works in a quadruped walking robot, called AiDIN(Artiflcial Digitigrade for Natural Environment).