This paper presents a comprehensive study of the forward and inverse kinematics of a six-degrees-of-freedom (DoF) spatial manipulator with a novel architecture. Developed by Systemantics India Pvt. Ltd., Bangalore, and designated as the H6A (i.e., Hybrid 6-Axis), this manipulator consists of two arm-like branches, which are attached to a rigid waist at the proximal end and are coupled together via a wrist assembly at the other. Kinematics of the manipulator is challenging due to the presence of two multi-DoF passive joints: a spherical joint in the right arm and a universal in the left. The forward kinematic problem has eight solutions, which are derived analytically in the closed form. The inverse kinematic problem leads to $160$ solutions and involves the derivation of a $40$-degree polynomial equation, whose coefficients are obtained as closed-form symbolic expressions of the pose parameters of the end-effector, thus ensuring the generality of the results over all possible inputs. Furthermore, the analyses performed lead naturally to the conditions for various singularities involved, including certain non-trivial architecture singularities. The results are illustrated via numerical examples which are validated extensively.

Due to the ever-increasing demand for food commodities and issues arising in their transport from rural to urban areas, commercial agricultural practices with the help of vertical farming are being taken up near urban regions. For the realization of agricultural practices on high-rise vertical farms, where human intervention is quite laborious, robotic assistance would be an effective solution to perform agricultural processes like seeding, transplanting, harvesting, health monitoring, nutrient-water supply, etc. The requirements and complexities of these tasks to be performed are different such as end-effector requirement, payload capacity required, amount of clutter while performing the task, etc. In such cases, an individual robotic configuration would not serve all the purposes and each task may require a different configuration. Purchasing a large number of configurations, as per requirement, is not economical and will also increase the cost of maintenance. Thus, the design of a reconfigurable robot manipulator is proposed in this work which can cater to modular layouts. A thorough study of the processes involved in the farming of leafy vegetables is done and the tasks to be performed by the manipulator are identified. Constrained optimization is performed based on reachability, while minimizing DoF, for the tasks of transplanting, plant heath monitoring, and harvesting to find the optimal configurations which can perform the given tasks. The study resulted in 5-DoF, 4-DoF, and 6-DoF configurations for transplanting, plant heath monitoring, and harvesting, respectively, thus emphasizing the need of a reconfigurable solution. The configurations are realized using modular library and verified to satisfy reachability to provide a complete solution.

A robotic system constructed as a wheeled inverted pendulum (WIP) serves as an impressive demonstrator, since this kind of system is inherently nonlinear, unstable, and nonminimum phase. These properties may pose several difficulties, when it comes to control and trajectory planning. This paper shows a method for deriving a highly dynamic trajectory compliant with the system dynamics by means of solving an optimal control problem (OCP) using multiple shooting. The assumed task includes that the WIP should pass a height-restricting barrier. This can be achieved by leaning back or forth, in order to reduce the overall height of the WIP. The constraints inherent to the definition of this trajectory are nonconvex due to the shape of the robot. The constraint functions have a local minimum in an infeasible region. This can cause problems when the initial guess is within this infeasible region. To overcome this, a multistage approach is proposed for this special OCP to evade the infeasible local minimum. After solving four stages of subsequent optimization problems, the optimal trajectory is obtained and can be used as feedforward for the real system.

With the development of intelligent manufacturing, more and more nonstandard parts are used in high-precision assembly. The robotic assembly method based on attractive region in environment (ARIE) has been proven to have good performance in the high-precision assembly under the limitation of robot system accuracy or sensing accuracy. However, for the assembly of nonstandard parts, especially nonconvex parts, the existing ARIE-based strategy lacks a targeted design. In the assembly process, the nonconvex structure may cause blocking problems, which will lead to assembly failure when using the strategy. In order to solve this problem, this paper proposes a new assembly method by using the geometric features of constraint region based on the concept of ARIE. Specifically, first, when using the ARIE-based classic strategy, the reasons for the possible blocking problem in the assembly of a class of nonconvex axisymmetric parts are analyzed in detail. Second, a multi-step sliding strategy is proposed based on the theory of ARIE to solve the possible blocking problem in the assembly process. Third, impedance control is used to enable the peg to achieve the desired compliant motion in the proposed strategy. The improvement in the success rate of the proposed method is verified by the comparison experiment of small clearance peg-in-hole assembly, where the structure of the peg is nonconvex and axisymmetric.

In today’s world, in order to increase the movement abilities of amputees, different types of passive prostheses are used according to the level of the person’s disability. Although these types of prostheses increase a person’s mobility; however, they still have a limited ability to help the amputee walk normally on uneven terrain, as there is no net stimulus input power for the target joint. These limitations have led to the growth of the use of active prostheses in recent years, which has led to a variety of prosthetic designs. The purpose of this research is, first of all, to provide a suitable design and control of active transtibial prosthesis close to the performance of lost limb of a healthy person, and second and more importantly, to develop a 7-link inverse dynamic model of human gait and use it to analyze of an amputee gait with the designed prosthesis. Winter’s reference data are used in the entire process of design and simulation of prosthesis performance. Also, it is assumed that the amputation occurred in only one leg of the person. Based on the obtained results, when an amputee with 57 kg weight and 1.55 cm height wears an active prosthesis designed with 0.5 kg extra weight, the amount of metabolic cost of amputee in the swing phase increases by about 20%. By using this obtained model, it will be possible to optimize different prosthesis designs for people with different weight and height conditions.

There are a lot of high-height structures that should be inspected or manipulated frequently due to maintenance purposes. According to the safety considerations and time or cost limitations, substituting the human operator with an automatic robot is inevitable. The main objective of this paper is to design and manufacture a novel climbing robot equipped with grip-based locomotion system which can climb through scaffold structures and trusses to accomplish inspectional and operational tasks. The proposed robot has good maneuverability and stability. The proposed robot is manufactured in order to verify the simulation results with experimental data. The chassis and its corresponding grippers are designed first, and the corresponding model of the system is extracted. This model is used then for designing the controlling strategy of the system. The path planning of the robot is conducted to realize the climbing process by the robot during several steps in an optimum way. The prototype of the proposed robot is manufactured at Kharazmi University called KharazmBot. Experimental results not only show the capability of the manufactured robot toward ascending the mentioned structures but also prove its high stability as a result of its designed gripper and also its good maneuverability as a result of its over-actuated mechanism. Thus, it is concluded that the designed and manufactured climbing robot of this paper can successfully ascend through the pipes and trusses and perform a desired inspectional or operational task with good accuracy and safety while its stability is also satisfied.

Selective compliance articulated robot arms (SCARA) robotic manipulators find wide use in industry. A nonlinear optimal control approach is proposed for the dynamic model of the 4-degrees of freedom (DOF) SCARA robotic manipulator. The dynamic model of the SCARA robot undergoes approximate linearization around a temporary operating point that is recomputed at each time-step of the control method. The linearization relies on Taylor series expansion and on the associated Jacobian matrices. For the linearized state-space model of the system, a stabilizing optimal (H-infinity) feedback controller is designed. To compute the controller’s feedback gains, an algebraic Riccati equation is repetitively solved at each iteration of the control algorithm. The stability properties of the control method are proven through Lyapunov analysis. The proposed control method is advantageous because: (i) unlike the popular computed torque method for robotic manipulators, it is characterized by optimality and is also applicable when the number of control inputs is not equal to the robot’s number of DOFs and (ii) it achieves fast and accurate tracking of reference setpoints under minimal energy consumption by the robot’s actuators. The nonlinear optimal controller for the 4-DOF SCARA robot is finally compared against a flatness-based controller implemented in successive loops.

Dynamical movement primitives (DMPs) method is a useful tool for efficient robotic skills learning from human demonstrations. However, the DMPs method should know the specified constraints of tasks in advance. One flexible solution is to introduce the human superior experience as part of input. In this paper, we propose a framework for robot learning based on demonstration and supervision. Superior experience supplied by teleoperation is introduced to deal with unknown environment constrains and correct the demonstration for next execution. DMPs model with integral barrier Lyapunov function is used to deal with the constrains in robot learning. Additionally, a radial basis function neural network based controller is developed for teleoperation and the robot to track the generated motions. Then, we prove convergence of the generated path and controller. Finally, we deploy the novel framework with two touch robots to certify its effectiveness.

This paper demonstrates that “completely-jigless” assembly of a model product that requires fitting accuracy at the level of industrial products is possible by using a universal hand with four parallel stick fingers mounted on a conventional position-control-based industrial robot. Assuming that each part is taken out of the parts bin and temporarily placed on the work table, the accuracy required for precise fitting cannot be achieved with a vision sensor alone. Introducing an appropriate grasping strategy, the initial position error of the part is absorbed by self-alignment in the process of grasping. Once the alignment is completed, the pose of the grasped part is fixed and jigless assembly is possible with a conventional industrial robot, which has high repeatability. In this paper, we use a gear unit as an example of an industrial product and present some grasping strategies with the universal hand. We also propose some subsequent assembly strategies for shafts and gears. Using those grasping and assembly strategies, it is shown that jigless assembly of the gear unit was successfully completed in the experiment. Although the target product in this paper is specific, the assembly elements in this product, such as shaft screwing, bearing insertion, and gear meshing, are also included in many other products. Therefore, the methods shown in this paper can be applied to other products.

The paper addresses a significant challenge in on-orbit robotics servicing and assembly, which is to overcome the saturation setback of force/torque on robot joint and spacecraft actuators during the post-capture stage while controlling a target spacecraft with uncontrolled large angular and linear momentums. The authors propose a novel solution based on two robust and efficient control algorithms: Optimal Control Allocation (OCA) and Non-linear Model Predictive Control (NMPC). Both algorithms aim to minimize joint torques, spacecraft actuator moments, contact forces, and moments of a compound redundant system that includes a common payload (target spacecraft) grasped by dual n-degree space robotics manipulators mounted on a chaser spacecraft. The OCA algorithm minimizes a quadratic cost function using only the current states and the system dynamics, but the NMPC also considers the future state estimates and the control inputs over a specified prediction horizon. It is computationally more involved but provides superior results in reducing joint torques. The literature to date in application of MPC to robotics mainly focuses on linear models but the dual arm coordination is highly non-linear and there is no MPC application on dual arm coordination. The proposed discretized technique offers exact realizations (of a non-linear model) with elegance and simplicity and yet considers the full non-linear model of the dual arm coordinating system. It is computationally very efficient. The computer simulation results show that the proposed algorithms work efficiently, and the minimum torques, contact forces, and moments are realized. The developed algorithm also is very efficient in tracking problems.

Sleep apnea is one of the most common sleep disorders. The consequences of undiagnosed sleep apnea can be very serious, increasing the risk of high blood pressure, heart disease, stroke, and Alzheimer’s disease over a long period of time. However, many people are often unaware of their condition. The gold standard for diagnosing sleep apnea is nighttime polysomnography monitoring in a specialized sleep laboratory. However, these diagnoses are expensive and the number of beds is limited, and there is insufficient monitoring in terms of time dimension. Existing methods for automated detection use no more than three physiological signals, but all other signals are also associated with the patient’s sleep. In addition, the limited amount of medical real annotation data, especially abnormal samples, lead to weak model generalization capability. The gap between model generalization capability and medical field needs still exists. In this paper, we propose a method for integrating medical interpretation rules into a long short-term memory neural network based on self-attention with multichannel respiratory signals as input. We obtain attention weights through a token-level attention mechanism and then extract key rules of medical interpretation to assist the weights, improving model generalization and reducing the dependence on data volume. Compared with the best prediction performance of existing methods, the average improvements of our method in accuracy, precision, and f1-score are 3.26%, 7.03%, and 1.78%, respectively. The algorithm tested the performance of our model on the Sleep Heart Health Study data set and found that the model outperformed existing methods and could help physicians make decisions in their practices.

With the progress of industry, people are facing more and more complicated tasks, which cannot be completed by conventional rigid robot. For this, a deployable robot based on spherical linkage parallel mechanism was proposed to satisfy relevant requirements for the degrees of freedom in this study. Based on the design of robot model and its control box, a mathematical model for the robot was established, and the relationship between motion space and drive space was deduced accordingly. Subsequently, a control system consisting of the upper and lower computers was introduced. Two control modes, that is, Joystick control and remote control, were developed. The upper computer control interface for the robot was completed by MATLAB construction. At last, the two control modes as well as autonomous detection were demonstrated by motion test. This achievement will further advance the applications of deployable robot in job aid and intelligent exploration.

For greater autonomy of visual control-based solutions, especially applied to mobile robots, it is necessary to consider the existence of unevenness in the navigation surface, an intrinsic characteristic of several real applications. In general, depth information is essential for navigating three-dimensional environments and for the consistent parameter calibration of the visual models. This work proposes a new solution, including depth information in the visual path-following (VPF) problem, which allows the variation of the perception horizon at runtime while forcing the coupling between optical and geometric quantities. A new NMPC (nonlinear model predictive control) framework considering the addition of a new input to an original solution for the constrained VPF-NMPC allows the maintenance of low computational complexity. Experimental results in an outdoor environment with a medium-sized commercial robot demonstrate the correctness of the proposal.