In this paper, we propose a method of using an autonomous flying robot to explore an underground tunnel environment and build a 3D map. The robot model we use is an extension of a 2D non-holonomic robot. The measurements and sensors we considered in the presented method are simple and valid in practical unmanned aerial vehicle (UAV) engineering. The proposed safe exploration algorithm belongs to a class of probabilistic area search, and with a mathematical proof, the performance of the algorithm is analysed. Based on the algorithm, we also propose a sliding control law to apply the algorithm to a real quadcopter in experiments. In the presented experiment, we use a DJI Guidance sensing system and an Intel depth camera to complete the localization, obstacle detection and 3D environment information capture. Furthermore, the simulations show that the algorithm can be implemented in sloping tunnels and with multiple UAVs.

Cooperative robots are usually required in flexible manufacturing systems or complex working environments. In particular, when an object under processing is too big or too heavy, a single robot may not be enough to handle it. Two or more manipulators are to be used in such a case. This paper presents the study of the dynamic equations for two industrial robots holding a rigid object. To this end, holonomic constraints are combined with the manipulators and object equations of motion to obtain the dynamic model of the whole system, which can be used for simulation purposes. Experimental results are presented to validate the theoretical results.

In this paper, motion synchronization is investigated for human–robot collaboration such that the robot is able to “actively” follow its human partner. Force tracking is achieved with the proposed method under the impedance control framework, subject to uncertain human limb dynamics. Adaptive control is developed to deal with point-to-point movement, and learning control and neural networks control are developed to generate periodic and arbitrary continuous trajectories, respectively. Stability and tracking performance of the closed-loop system are discussed through rigorous analysis. The validity of the proposed method is verified through simulation and experiment studies.

Low cost automation often requires accurate positioning. This happens whenever a vehicle or robotic manipulator is used to move materials, parts or minerals on the factory floor or outdoors. In last few years, such vehicles and devices are mostly autonomous. This paper presents the method of sensor fusion based on the Adaptive Fuzzy Kalman Filtering. This method has been applied to fuse position signals from the Global Positioning System (GPS) and Inertial Navigation System (INS) for the autonomous mobile vehicles. The presented method has been validated in 3-D environment and is of particular importance for guidance, navigation, and control of mobile, autonomous vehicles. The Extended Kalman Filter (EKF) and the noise characteristic have been modified using the Fuzzy Logic Adaptive System and compared with the performance of regular EKF. It has been demonstrated that the Fuzzy Adaptive Kalman Filter gives better results (more accurate) than the EKF. The presented method is suitable for real-time control and is relatively inexpensive. Also, it applies to fusion process with sensors different than INS or GPS.

This paper illustrates means by which the techniques of compliance and electroadhesion can, using electror-heological, and other fluids, be combined to provide very effective shape adaptive robot end-effectors and similar holding surface.

This paper contributes by presenting a parameter identification procedure for n-degrees-of-freedom flexible joint robot manipulators. An advantage of the given procedure is the obtaining of robot parameters in a single experiment. Guidelines are provided for the computing of the joint position filtering and velocity estimation. The method relies in the filtered robot model, for which no acceleration measurements are required. The filtered model is expressed in regressor form, which allows applying a parameter identification procedure based on the least squares algorithm. In order to assess the performance of the proposed parameter identification scheme, an implementation of a least squares with forgetting factor (LSFF) parameter identification method is carried out. In order to assess the reliability of the tested identification schemes, a model-based trajectory tracking controller has been implemented twice in different conditions: one control experiment using the estimated parameters provided by the proposed scheme, and another experiment using the parameters given by the LSFF method. These real-time control experiments are compared with respect to numerical simulations using the estimated parameters for each identification method. For the proposed scheme, the comparison between experiments and numerical simulations indicates better accuracy in the torque and position prediction.

This study presents an optimal design procedure including topology optimization and size–shape optimization methods to maximize mechanical advantage (which is defined as the ratio of output force to input force) of the synthesized compliant mechanism. The formulation of the topology optimization method to design compliant mechanisms with multiple output ports is presented. The topology-optimized result is used as the initial design domain for subsequent size–shape optimization process. The proposed optimal design procedure is used to synthesize an adaptive compliant gripper with high mechanical advantage. The proposed gripper is a monolithic two-finger design and is prototyped using silicon rubber. Experimental studies including mechanical advantage test, object grasping test, and payload test are carried out to evaluate the design. The results show that the proposed adaptive complaint gripper assembly can effectively grasp irregular objects up to 2.7 kg.

This paper investigates the passive and active control strategies to provide a physical assistance and rehabilitation by a 7-DOF exoskeleton robot with nonlinear uncertain dynamics and unknown bounded external disturbances due to the robot user's physiological characteristics. An Integral backstepping controller incorporated with Time Delay Estimation (BITDE) is used, which permits the exoskeleton robot to achieve the desired performance of working under the mentioned uncertainties constraints. Time Delay Estimation (TDE) is employed to estimate the nonlinear uncertain dynamics of the robot and the unknown disturbances. To overcome the limitation of the time delay error inherent of the TDE approach, a recursive algorithm is used to further reduce its effect. The integral action is employed to decrease the impact of the unmodeled dynamics. Besides, the Damped Least Square method is introduced to estimate the desired movement intention of the subject with the objective to provide active rehabilitation. The controller scheme is to ensure that the robot system performs passive and active rehabilitation exercises with a high level of tracking accuracy and robustness, despite the unknown dynamics of the exoskeleton robot and the presence of unknown bounded disturbances. The design, stability, and convergence analysis are formulated and proven based on the Lyapunov–Krasovskii functional theory. Experimental results with healthy subjects, using a virtual environment, show the feasibility, and ease of implementation of the control scheme. Its robustness and flexibility to deal with parameter variations due to the unknown external disturbances are also shown.

In this paper, wall-climbing robots with a single suction cup are studied. The robots mainly consist of three parts: A vacuum pump, a sealing mechanism and a driving mechanism. Basic conditions that the robots can adhere to and move reliably on a vertical surface are first established, then the sealing mechanism with an air spring and regulating springs is analyzed, and the synthetic rigidity formula for the sealing loop is obtained. Finally, two application examples are given: One for the ultrasonic inspection of cylindrical stainless steel nuclear storage tanks, and the other for cleaning high-rise buildings.

The significance of singularities in the design and control of robot manipulators is well known, and there is an extensive literature on the determination and analysis of singularities for a wide variety of serial and parallel manipulators—indeed such an analysis is an essential part of manipulator design. Singularity theory provides methodologies for a deeper analysis with the aim of classifying singularities, providing local models and local and global invariants. This paper surveys applications of singularity-theoretic methods in robot kinematics and presents some new results.

The complex model presented in this paper consists of three main parts: (a) a model of the robot; (b) tools for building and displaying models of workpieces to be painted; (c) a model of the spatial distribution of the paint particles. The simulation process is preceded by the creation of the surface to be coated. A colour graphic display is used for this purpose. The surface appears on the screen together with the robot (hidden line elimination is included). The “spray gun” is installed at the robot's tip and its orientation, performance and other characteristics are to be set. The paint particles sprayed are supposed to form a cone, and by considering its intersection with the surface, the amount of paint delivered to any point can be evaluated and visualized in different colours. The operator simulates the process of painting by moving the spray gun, changing the parameters (speed, distance, etc.), erasing and repeating any step of the process. He can also work in an automatic path generation mode. The result is a near-to-optimal path of the robot's tip (minimizing paint loss and providing a good evenness of coating).

We present two visual servoing controllers (pose-based and image-based) enabling mobile robots with a fixed pinhole camera to reach and follow a continuous path drawn on the ground. The first contribution is the theoretical and experimental comparison between pose-based and image-based techniques for a nonholonomic robot task. Moreover, our controllers are appropriate not only for path following, but also for path reaching, a problem that has been rarely tackled in the past. Finally, in contrast with most works, which require the path geometric model, only two path features are necessary in our image-based scheme and three in the pose-based scheme. For both controllers, a convergence analysis is carried out, and the performance is validated by simulations, and outdoor experiments on a car-like robot.

This paper discusses the problem of stable grasping and object manipulation by a pair of robot fingers when fingertips are covered with soft compressible material and fingers are allowed to incline their last link against the object surface. The area contact between the fingertips and the rigid object surface leads to nonholonomic constraints even for the planar case; however, the variational principle can be applied and the equation of motion is derived as a set of nonlinear differential equations with extra terms of Langrange multipliers incorporating the constraints. The proposed feedback controller is a linear combination of simple feedback control signals each designed for realizing grasp stabilization, regulation of object rotation and regulation of object position respectively. The controller is shown to achieve asymptotic convergence to the desired state at a stable grasping configuration. Simulation results are presented confirming the theoretical findings.

This paper compares two methods to estimate the position of a mobile robot in an indoor environment using only odometric calculus and the WiFi energy received from the wireless communication infrastructure. In both cases we use a well-known probabilistic method based on the Bayes rule to accumulate localization probability as the robot moves on with an experimental WiFi map, and with a theoretically built WiFi map. We will show several experiments made in our university building to compare both methods using a Pioneer robot. The two major contributions of the presented work are that the self-localization error achieved with WiFi energy is bounded, and that no significant degradation is observed when the expected WiFi energy at each point is taken from radio propagation model, instead of an a priori experimental intensity map of the environment.

This paper presents the design of four controllers for a mobile robot such that the system may follow a preestablished trajectory. To reach this aim, the kinematic model of a mobile robot is approximated using numerical methods. Then, from such approximation, the control actions to get a minimal tracking error are calculated. Both simulation and experimental results on a PIONEER 2DX mobile robot are presented, showing a good performance of the four proposed mobile robot controllers. Also, an application of the proposed controllers to a leader robot following problem is shown; in it, the relative position between robots is obtained through a laser.

This paper addresses the problem of contact force control for multiple contacts distributed over multiple links in a robot. This is of importance when performing complex tasks in unstructured environment, particularly in humanoid robot applications. The proposed multicontact control framework provides a new way of defining the operational space coordinates, which facilitates the specification of multiple contact control. The contact force space on multiple links is constructed as an operational space for the highest priority task. Motion control, given lower priority, can be executed using the rest of degree of freedom within the null-space of the force control. The dynamic control structure, then, provides a means to control each contact force and motion independently. This dynamic decoupling enables each contact force controller to utilize linear control theories. In particular, the contact force controllers adopt full state feedback control and estimation methods to produce robust performance with respect to modeling and parameter uncertainties. The effectiveness of the multiple contact control framework was demonstrated using a PUMA560 manipulator, with multiple contacts on the end-effector and third link. The demonstrated tasks involved controlling each of the contact forces with null-space motion.

This paper presents a new method using hierarchical optimal control for path planning and calculating maximum allowable dynamic load (MADL) of wheeled mobile manipulator (WMM). This method is useful for high degrees of freedom WMMs. First, the overall system is decoupled to a set of subsystems, and then, hierarchical optimal control is applied on them. The presented algorithm is a two-level hierarchical algorithm. In the first level, interaction terms between subsystems are fixed, and in the second level, the optimization problem for subsystems is solved. The results of second level are used for calculating new estimations of interaction variables in the first level. For calculating MADL, the load on the end effector is increased until actuators get into saturation. Given a large-scale robot, we show how the presenting in distributed hierarchy in optimal control helps to find MADL fast. Also, it enables us to treat with complicated cost functions that are generated by obstacle avoidance terms. The effectiveness of this approach on simulation case studies for different types of WMMs as well as an experiment for a mobile manipulator called Scout is shown.

Fault diagnosis plays an important role in the operation of modern robotic systems. A number of researchers have proposed fault diagnosis architectures for robotic manipulators using the model-based analytical redundancy approach. One of the key issues in the design of such fault diagnosis schemes is the effect of modeling uncertainties on their performance. This paper investigates the problem of fault diagnosis in rigid-link robotic manipulators with modeling uncertainties. A learning architecture with sigmoidal neural networks is used to monitor the robotic system for off-nominal behavior due to faults. The robustness, sensitivity, missed detection and stability properties of the fault diagnosis scheme are rigorously established. Simulation examples are presented to illustrate the ability of the neural network based robust fault diagnosis scheme to detect and accommodate faults in a two-link robotic manipulator.

This paper presents a continuous wall-following controller for wheeled mobile robots based on odometry and distance information. The reference for this controller is the desired distance from the robot to the wall and allows the robot to follow straight wall contour as well as smoothly varying wall contours by including the curvature of the wall into the controller. The asymptotic stability of the control system is proved using a Lyapunov analysis. The controller is designed so as to avoid saturation of the angular velocity command to the robot. A novel switching scheme is also proposed that allows the robot to follow discontinuous contours allowing the robotic system to deal with typical problems of continuous wall-following controllers such as open corners and possible collisions. This strategy overcomes these instances by switching between dedicated behavior-based controllers. The stability of the switching control system is discussed by considering Lyapunov concepts. The proposed control systems are verified experimentally in laboratory and office environments to show the feasibility and good performance of the control algorithms.

This paper reports on recent progress made toward the development of a new magnetic resonance imaging (MRI)-compatible robot-assisted surgical system for closed-bore image-guided prostatic interventions: thermal ablation, radioactive seed implants (brachytherapy), and biopsy. Each type of intervention will be performed with a different image-guided, robot-based surgical tool mounted on the same MRI-guided robot through a modular trocar. The first stage of this development addresses only laser-based focal ablation. The robot mechanical structure, modular surgical trocar, control architecture, and current stage of performance evaluation in the MRI environment are presented. The robot actuators are ultrasonic motors. A methodology of using such motors in the MRI environment is presented. The robot prototype with surgical ablation tool is undergoing tests on phantoms in the MRI bore. The tests cover MRI compatibility, image visualization, robot accuracy, and thermal mapping. To date, (i) the images are artifact- and noise-free for certain scanning pulse sequences; (ii) the robot tip positioning error is less than 1.2 mm even at positions closer than 0.3 m from the MRI isocenter; (iii) penetration toward the target is image-monitored in near-real time; and (iv) thermal ablation and temperature mapping are achieved using a laser delivered on an optical fiber and MRI, respectively.