Instead of traditional ranging radar and camera, Kinect was used to ensure the robot can avoid obstacles in the process of movement. And an obstacle avoidance method of robot was proposed based on admittance control. The relative distance between the robot and human hand was real-time measured by Kinect sensor and converted into a virtual force, acting as an input of the admittance controller to control the next moment speed of the controller to avoid obstacles. Then a modification algorithm for the robot's speed direction was added on the basis of admittance control, when the operator's arm is moving at a high speed, to control the robot's avoid obstacles urgently. At the same time, a method of singular transition to the joint space was proposed to solve the problem that once the robot encountering strange points in Cartesian space it will out of control. Finally, a physical experiment platform based on Kinect sensor and UR robot was set up to conduct dynamic and static obstacle avoidance experiments. The results verified the effectiveness of the proposed method.
The prediction of the human motion plays an important role in the predictive motion controller of human-assisting mobile robot, and precise prediction requires a precise motion model. In this paper, a novel human motion prediction method is proposed. This prediction method is based on a hybrid motion model which combines the inverted pendulum model and constant velocity model. Derivations of the kinematics, especially the inverted pendulum model, are presented in this paper. Estimation of the prediction by inverted pendulum and constant velocity models based on the Unscented kalman filter(UKF) and Kalman filter(KF) respectively, are also given. The hybrid model precisely describes the walking cycle. Experiments verifying the effectiveness of the proposed model are also presented.
This paper mainly introduces the the control system of micro AUV based on open source hardware. The control system is the most important part of the AUV and its performance has a direct impact on the stability and reliability of AUV. Due to the small size and limited energy, micro AUV puts more restrictions on the volume and power consumption of the control system. In this paper, we choose the BeagleBone Black, the open source hardware, as the main controller, the rich peripheral interface of it facilitates data acquisition and centralized processing of the vehicle. Arduino, another type of open source hardware, is used as the node controller of the control system. Compared with the traditional hardware design, this architecture has the advantages of small volume, low power consumption, low cost, short period of development and maintenance, and it also had good performance in data processing and real-time control. The experimental results showed that the control system based on this architecture run effectively.
Rope-driven rigid-flexible parallel joints are driven by flexible ropes, combining the advantages of both rigid parallel and rope-driven parallel mechanisms. This paper firstly proposes a rope-driven 4SPS-1U rigid-flexible parallel joint mechanism with a flexible spring. Then, the joint mechanism is modeled with forward/inverse position. Secondly, the position solution is numerically analyzed. The work space was simulated and Adams simulation software was used to verify the correctness of the numerical calculation method. The results of this study confirm that the rope-driven rigid-flexible parallel joint mechanism can achieve a variety of movement patterns, which provides a new idea for the innovative design of the robot mechanism.
This paper proposes a new semantic-based framework for real-time detection of arbitrary polygons and circles. In the proposed framework, the first stage is to extract Directional-Cornered Line (DCL) segments for representation of the shape contours. In the second stage, a technique of Directional-Local Searching (DLS) is proposed to group these DCL segments into shape candidates. These shape candidates can be further classified with intuitive semantic criteria. The proposed method is suitable for real-time applications with linear time complexity. Comparative experiments on natural images prove that the proposed method has good performance and the ability to simultaneously locate and detect the polygonal and circular shapes of various objects in real-time.
