Journal of Robotics

As the best representative of the current cutting-edge technology, unmanned aerial vehicle (UAV) is widely used in various fields such as electric power inspection, agriculture, forestry and plant protection, fire rescue, and film and television shooting. With the rapid development of UAV, the safety work of UAV has become more important. In order to improve the safety of hexarotor UAV during flight, a fault-tolerant control scheme independent of basic control law and control distribution is designed in this paper. Firstly, the linear active disturbance rejection control (LADRC) was used as the basic control law for attitude control of hexarotor UAV. Secondly, in the case of actuator failure of hexarotor UAV, a fault observer was used to estimate fault information accurately. Then, on this basis, the control distribution matrix was adjusted to reduce the use of the faulty motor, and the purpose of fault-tolerant control was achieved. Finally, simulation experiments and actual flight experiments were carried out to verify the effectiveness of the proposed scheme. Experimental results show that the proposed scheme can improve the robustness of the control system and the flight safety of UAV.

In this research work, we propose a method for human action recognition based on the combination of structural and temporal features. The pose sequence in the video is considered to identify the action type. The structural variation features are obtained by detecting the angle made between the joints during the action, where the angle binning is performed using multiple thresholds. The displacement vector of joint locations is used to compute the temporal features. The structural variation features and the temporal variation features are fused using a neural network to perform action classification. We conducted the experiments on different categories of datasets, namely, KTH, UTKinect, and MSR Action3D datasets. The experimental results exhibit the superiority of the proposed method over some of the existing state-of-the-art techniques.

The current flapping wing adopts T-shaped or cross-shaped tail fin to adjust its flight posture. However, how the tail fin will affect the hover control is not very clear. So, the effects of the two types of tail on flight will be analyzed and compared by actual flight tests in this paper. Firstly, we proposed a new X-wing single-bar biplane flapping-wing mechanism with two pairs of wings. Thereafter, the overall structure, gearbox structure, tail, frame, and control system of the flapping wing were designed and analyzed. Secondly, the control mechanism of hover is analyzed to describe the effect of two-tail fin on posture control. Thirdly, the Beetle was used as the control unit to achieve a controllable flight of flapping wing. The MPU6050 electronic gyroscope was used to monitor the drone’s posture in real time, and the Bluetooth BLE4.0 wireless communication module was used to receive remote control instructions. At last, to verify the flight effect, two actual flapping wings were fabricated and flight experiments were conducted. The experiments show that the cross-shaped tail fin has a better controllable performance than the T-shaped tail fin. The flapping wing has a high lift-to-mass ratio and good maneuverability. The designed control system can achieve the controllable flight of the flapping wing.

This paper presents a literature survey documenting the evolution of continuum robots over the past two decades (1999–present). Attention is paid to bioinspired soft robots with respect to the following three design parameters: structure, materials, and actuation. Using this three-faced prism, we identify the uniqueness and novelty of robots that have hitherto not been publicly disclosed. The motivation for this study comes from the fact that continuum soft robots can make inroads in industrial manufacturing, and their adoption will be accelerated if their key advantages over counterparts with rigid links are clear. Four different taxonomies of continuum robots are included in this study, enabling researchers to quickly identify robots of relevance to their studies. The kinematics and dynamics of these robots are not covered, nor is their application in surgical manipulation.

In order to solve the problem of demand for large capacity storage and high-performance computing resource of intelligent robot navigation and demand-service matching in the field of assisting handicapped or elderly people, overcoming the limitations of carrying resources, the method of constructing robot service platform for assisting handicapped or elderly people (RSP-AHEP) was proposed based on cloud robotics technology. Firstly, the demand of assisting handicapped or elderly people and the corresponding robot service category was analyzed to assure relationship-matching subjects. Secondly, based on VMware and Hadoop cluster technology, the architecture of a three-layer robot service platform was designed, which were universal interface layer, resource service layer, and application layer. Complex computing tasks, such as the matching computing between robot service and demand of the handicapped or elderly people, and the robot path-planning service, were placed in the robot service platform with the advantages of storage and computing resources. Thirdly, the remote communication between the robot and the service platform was realized based on the ROS (Robot Operation System) technology; finally, the function experiments, which included the remote dispatch, path planning, and service response between the service platform and robot, were carried out in the simulation environment. The result verified the feasibility of the proposed method.

This work presents a novel fuzzy adaptive sliding mode-based feedback linearization controller for trajectory tracking of a flexible robot manipulator. To reach this goal, after deriving the dynamical equations of the robot, the feedback linearization approach is utilized to change the nonlinear dynamics to a linear one and find the control law. Then, the sliding mode control strategy is implemented to design a stabilizer for trajectory tracking of the flexible robot. In order to adaptively tune the parameters of the designed controller, the gradient descent approach and the chain derivative rule are employed. Moreover, the Takagi–Sugeno–Kang fuzzy system is applied to regulate the controller gains. Finally, a multiobjective particle swarm optimization algorithm is used to find the optimum fuzzy rules. The conflicting objective functions considered as the integrals of the absolute values of the state error and the control effort should be minimized, simultaneously. The simulation results illustrate the effectiveness and capability of the introduced scenario in comparison with other methods.