Motion Planning and Control of Robot Systems

The hybrid space of robots is divided into task space and joint space, with task space focused on trajectory-tracking accuracy, while joint space considers dynamic responsiveness and synchronization. Therefore, the robot-motion control systems need to effectively integrate both aspects, ensuring precision in task trajectory while promptly responding to unforeseen environmental events. Hence, this paper proposes an online trajectory-generation method for robots in both joint and task spaces. In task space, a planning approach is presented for high-precision NURBS curves. The global NURBS curve is segmented into several rational Bezier curves, establishing local coordinate systems for control points. This ensures that all local control points meet the chord error constraint, guaranteeing trajectory accuracy. To address the feed rate dynamic planning issue for segmented curves, an improved online S-shape feed-rate scheduling framework is introduced. This framework dynamically adjusts the current execution speed to meet task requirements. In joint space, an offline velocity planning based on a time synchronization scheme and a multi-dimensional synchronization technique based on the principle of spatial-coordinate system projection are proposed. Building upon the offline scheme, it allows for the modification of the target state for any sub-dimension during the motion process, with the remaining dimensions adapting accordingly. Simulation and experimentation demonstrate that the two proposed online trajectory generations for robot motion spaces, while ensuring task trajectory accuracy, effectively handle external unexpected events. They ensure joint synchronization and smoothness, carrying significant practical implications and application value for the stability of robot systems. Full article

Accurate trajectory tracking is a paramount objective when a mobile robot must perform complicated tasks. In high-speed movements, hardware-induced delays may produce overshoots and even instability when controlling the system. In this case, Smith predictor controllers can be used because they are well suited for delayed processes. This paper addresses the accurate positioning of a mobile robot on a terrain of an unknown slope. This slope produces disturbance torques of unknown amplitudes in the robot actuators that yield a steady-state error in the positioning. Because our actuators are integrating plus time delay plants, the standard Smith predictor cannot remove these disturbances. This paper proposes a modification of this control scheme in order to remove these disturbances yielding a zero steady-state error in the actuators. Our new scheme is compared with other modified $SPs$ existing in the literature by means of simulations. These simulations show the superior performance of our scheme in the sense of removing the steady-state error more efficiently (i.e., faster) than other schemes. Finally, the performance of our control scheme is tested experimentally in a low-cost mobile robot. Full article

To overcome the limitations of the sparrow search algorithm and the challenges of dynamic obstacle avoidance in mobile robots, an integrated method combining the enhanced sparrow search algorithm with the dynamic window approach is introduced. First, logistic–tent chaotic mapping is utilized for the initialization of the sparrow population, thereby achieving a uniform distribution of the sparrow population and simultaneously enhancing the exploratory capability of the algorithm. The implementation of the elite reverse learning strategy aims to diversify the sparrow population, thus improving the quality of initial solutions and the algorithm’s search accuracy. Additionally, the position update dynamic self-adaptive adjustment strategy is adopted to enhance the optimization capability of the algorithm by refining the position update formulas for both producers and scroungers. By combining the Lévy flight strategy and the optimal position perturbation strategy, the algorithm’s efficacy in escaping local optima can be improved. Second, an adaptive velocity adjustment strategy is presented for the dynamic window approach and optimized for its evaluation function to enhance the safety of the path. Third, the enhanced sparrow search algorithm is integrated with the dynamic window approach to tackle the problems of the non-smooth global path and inadequate dynamic obstacle avoidance capability. Both simulation and experimental results show the superiority of the enhanced sparrow search algorithm in comparison to other algorithms in terms of the path length, total rotation angle, and algorithm execution time. Notably, in comparison to the basic sparrow search algorithm, there is a decrease in average path lengths by 15.31% and 11.92% in the improved sparrow search algorithm. The integrated algorithm not only crafts local paths rooted in global paths but also adeptly facilitates real-time dynamic obstacle evasion, ensuring the robot’s safe arrival at its destination. Full article

Energy consumption is one of the most critical factors in determining the kinematic performance of quadruped robots. However, existing research methods often encounter challenges in quickly and efficiently reducing the energy consumption associated with quadrupedal robotic locomotion. In this paper, the deep deterministic policy gradient (DDPG) algorithm was used to optimize the energy consumption of the Cyber Dog quadruped robot. Firstly, the kinematic and energy consumption models of the robot were established. Secondly, energy consumption was optimized by reinforcement learning using the DDPG algorithm. The optimized plantar trajectory was then compared with two common plantar trajectories in simulation experiments, with the same period and the number of synchronizations but varying velocities. Lastly, real experiments were conducted using a prototype machine to validate the simulation data. The analysis results show that, under the same conditions, the proposed method can reduce energy consumption by 7~9% compared with the existing optimal trajectory methods. Full article

This paper shows the dynamic modeling and design of a passivity-based controller for the RV-3SB robot. Firstly, the dynamic modeling of a Mitsubishi RV-3SB robot is conducted using Euler–Lagrange formulation in order to obtain a decoupled dynamic model, considering the actuator orientation besides the position of the analyzed robot. It is important to remark that the dynamic model of the RV-3SB robot is conducted based on kinematic model obtention, which is developed by the implementation of screw theory. Then, the passivity-based controller is obtained by separating the end effector variables and the actuator variables by making an appropriate coordinate transformation. The passivity-based controller is obtained by selecting an appropriate storage function, and by using Lyapunov theory, the passivity-based control law is obtained in order to drive the error variable, which is the difference between the measured end effector position variable and the desired end effector position variable. The passivity-based controller makes the error variable reach the origin in finite time, taking into consideration the dissipation properties of the proposed controller in order to stabilize the desired end effector position. A numerical simulation experiment is performed in order to validate the theoretical results obtained in this research. Using numerical experimentation, it is verified that the proposed control strategy is efficient and effective in driving the error variable to the origin in comparison with other modified techniques found in the literature. Finally, an appropriate discussion and conclusion of this research study are provided. Full article

In submarine oil and gas pipelines, the movement of a differential pressure multisection pipeline robot mainly relies on the front and rear driving pressure difference of the fluid and the friction between the cup and the pipe wall. The passability of the pipeline is a key point to guarantee success in scanning and detecting the inner wall of a pipeline by robot. When the multisection pipeline robot moves, the force of the internode connection points changes the degree of freedom of the robot. The existence of the connection points causes speed fluctuations in the robot during the movement process which, in turn, affects the detection accuracy of the pipeline. Consequently, a systematic analysis of the connection point movement is of great importance. In this paper, a rigid–flexible, coupled, multibody, dynamic motion system is established, where a multisection micro pipeline robot is built. The cup of the robot is set as a flexible body. The motion law of the differential pressure multisection pipeline robot is analyzed through simulation, and the robot’s motion speed and internode stress of the different cabin sections are explored jointly in practice. Taking the transportation of an oil and gas pipeline into full consideration, the motion law and force of the multisection pipeline robot are analyzed by changing the bulge and turning the radius of the inner wall of the pipeline. A corresponding experimental bench is built to explore the effects of different numbers of cups and lengths of the cabin sections on the turning characteristics of the robot. Simulations and experiments results are highly similar and within the error range. To this end, the presented work provides significant information for the model selection of multisection micro pipeline robots in the fields of submarine oil and gas pipelines. Full article

Robots are expanding from industrial applications to daily life, in areas such as medical robotics, rehabilitative robotics, social robotics, and mobile/aerial robotics systems. In recent years, augmented reality (AR) has been integrated into many robotic applications, including medical, industrial, human–robot interactions, and collaboration scenarios. In this work, AR for both medical and industrial robot applications is reviewed and summarized. For medical robot applications, we investigated the integration of AR in (1) preoperative and surgical task planning; (2) image-guided robotic surgery; (3) surgical training and simulation; and (4) telesurgery. AR for industrial scenarios is reviewed in (1) human–robot interactions and collaborations; (2) path planning and task allocation; (3) training and simulation; and (4) teleoperation control/assistance. In addition, the limitations and challenges are discussed. Overall, this article serves as a valuable resource for working in the field of AR and robotic research, offering insights into the recent state of the art and prospects for improvement. Full article