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Journal of Robotics publishes original research articles as well as review articles on all aspects of automated mechanical devices, from their design and fabrication, to testing and practical implementation.
Journal of Robotics maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.
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Power Optimization in Mobile Robots Using a Real-Time Heuristic
Mobile robots typically run using finite energy resources, supplied by finite batteries. The limitation of energy resources requires human intervention for recharging the batteries. To reduce human intervention, this work focuses on coordinating power in a group of robots. A power optimization subroutine provides some sense of distribution of power by the control unit (CU). A variety of on-board sensors, actuators, and communication modules are controlled by a heuristic-based controller class allowing such components to conserve the current taken from the attached power source. Using the proposed approach, autonomous robots will be aware of their power system, especially regarding battery life. The new approach takes advantage of a heuristic function which uses evaluation values calculated at different times for the different robots. An experimental setup is applied on the team of robots. The optimization module is evaluated on each robot. The results show that the team of mobile robots consumes less energy and more efficient power regulation during their duties. Finally, the application of the proposed optimization technique in a distributed manner achieves good power saving figures when performing the particular task.
Microcontroller-Based Direct Torque Control Servodrive
Robot technology has become an integral part of the automotive industry in several tasks such as material handling, welding, painting, and part assembly. Therefore, the knowledge and skills to control the electric motors in these manipulators are essential for undergraduate electrical engineering students. Currently, the digital signal processor (DSP) is the core chip in industrial motor-control drives; however, the implementation of DSP control algorithms can be quite challenging for an experienced programmer, even more so for the novice. Considerable research has been done on this topic, although authors usually focus on DSP-based motor drives using popular control techniques such as field-oriented control (FOC). Although highly efficient, this approach is usually reserved for postgraduate education due to its complex structure and functionality. In this paper, the authors present a modular servodrive design on a low-cost, general-purpose microcontroller using the direct torque control (DTC) method, an alternative known for greater simplicity and torque response, compared with FOC. The system design was based on Micropython language allowing the software structure to be more manageable and the code to be more understandable. This design will be useful to undergraduates and researchers with interests in motor control design.
Stiffness and Elastic Deformation of 4-DoF Parallel Manipulator with Three Asymmetrical Legs for Supporting Helicopter Rotor
The stiffness and elastic deformation of a 4-DoF parallel manipulator with three asymmetrical legs are studied systematically for supporting helicopter rotor. First, a 4-DoF 2SPS + RRPR type parallel manipulator with two linear SPS type legs and one RRPR type composite leg is constructed and its constraint characteristics are analyzed. Second, the formulas for solving the elastic deformation and the stiffness matrix of the above mentioned three asymmetrical legs are derived. Third, the formulas for solving the total stiffness matrix and the elastic deformation of this manipulator are derived and analyzed. Finally, its finite element model is constructed and its elastic deformations are solved using both the derived theoretical formulas and the finite element model. The theoretical solutions of the elastic deformations are verified by that of the finite element model.
Research on Dynamic Path Planning of Wheeled Robot Based on Deep Reinforcement Learning on the Slope Ground
The existing dynamic path planning algorithm cannot properly solve the problem of the path planning of wheeled robot on the slope ground with dynamic moving obstacles. To solve the problem of slow convergence rate in the training phase of DDQN, the dynamic path planning algorithm based on Tree-Double Deep Q Network (TDDQN) is proposed. The algorithm discards detected incomplete and over-detected paths by optimizing the tree structure, and combines the DDQN method with the tree structure method. Firstly, DDQN algorithm is used to select the best action in the current state after performing fewer actions, so as to obtain the candidate path that meets the conditions. And then, according to the obtained state, the above process is repeatedly executed to form multiple paths of the tree structure. Finally, the non-maximum suppression method is used to select the best path from the plurality of eligible candidate paths. ROS simulation and experiment verify that the wheeled robot can reach the target effectively on the slope ground with moving obstacles. The results show that compared with DDQN algorithm, TDDQN has the advantages of fast convergence and low loss function.
A New Type of Industrial Robot Trajectory Generation Component Based on Motion Modularity Technology
Motion modularity is the main method of motion control for higher animals. That means the complex movements of the muscles are made up of basic motion primitives, and the brain or central nervous system does not care about the specific details of the movement. However, the industrial robot control system does not adopt the technical roadmap of motion modularity, it generates complex trajectories by providing a large number of sampling points. This approach is equivalent to using the brain to directly guide the specific movement of the muscle and has to rely on a faster Fieldbus system to obtain complex motion trajectories. This work constructs a modularized industrial robot trajectory generation component based on Dynamic Movement Primitives (DMP) theory. With this component, the robot controller can generate complex trajectories without increasing the sampling points and can obtain good trajectory accuracy. Finally, the rationality of this system is proved by simulations and experiments.
Robust Hybrid Control Algorithm for Tuning the Altitude and Attitude of Unmanned Aerial Vehicle
In this article, a new and novel robust hybrid control algorithm is designed for tuning the parameters of unmanned aerial vehicle (UAV). The quadrotor type UAV mathematical model is taken to observe the effectiveness of our designed robust hybrid control algorithm. The robust hybrid control algorithm consists of based regulation, pole-placement and tracking (RST) controller along with mixed sensitivity function is applied to control the complete model of UAV. The selected rotor craft is under-actuated, nonlinear and multivariable behavior in nature along with six degrees of freedom (DOF). Due to all these aforementioned issues its stabilization is quite difficult as compared to fully actuated systems. For the tuning of nonlinear parameters of the UAV, we designed, robust hybrid control algorithm is used. Moreover, the performance of the designed controller is compared with robust controller. The validity and effectiveness of the designed controllers are simulated in MATLAB and Simulink, in which the designed controller shows better steady state behavior, robustness and converges quickly in specific amount of time as compared to robust controller.