This paper delves into the fusion of quantum computing and robotics, focusing on motion planning in cluttered environments. Traditional algorithms struggle with complex problems where many constraints need to be satisfied. Hence, optimization-based approaches such as Constrained Quadratic Models (CQM) have become increasingly popular. Our work presents a 3D tracking algorithm based on CQM uniquely adapted for quantum computers to address computational challenges. With their parallel processing capabilities, Quantum computers offer a groundbreaking approach to optimizing complex problems. We formulate the CQM problem for efficient resolution on the D-Wave quantum computer, showcasing its superiority over classical counterparts. Our application centers on real-time planning in a target-chaser tracking scenario, highlighting the quantum advantage in handling the computation complexity of constrained problems. This paper bridges the quantum-robotics gap and sets the stage for future research in quantum-enhanced robotic motion planning.

Aerial manipulation systems are an emerging subclass of unmanned aerial vehicles (UAVs or “drones”) that use a mobile arm to manipulate their environment. Public opinion is an important consideration for drones, but past public opinion polls have focused on drones without attached arms and have relied on text-based surveys. Study 1 (N= 190) assessed participants’ perceived benefit, risk, and acceptance of aerial manipulation systems across five applications, using an animation-based online public opinion survey. Study 2 (N= 194) assessed the influence of stimulus sampling on public opinion of aerial manipulation systems by replicating Study 1 using an alternative set of animations. Study 3 (N= 396) assessed the influence of using animations versus a text control on perceptions of the poll and aerial manipulation systems, as well as the influence of survey platform (YouGov Direct versus Mechanical Turk). Results show that delivery applications are perceived as more beneficial than several other applications; that people’s opinions and imagining of drones were different if they watched animations versus read text; and that stimulus and population sampling influence the results of public opinion polls about drones.

Rotary-wing flying machines draw attention within the UAV community for their in-place hovering capability, and recently, holonomic motion over fixed-wings. In this paper, we investigate about the power-optimality in a mono-spinner, i.e., a class of rotary-wing UAVs with one rotor only, whose main body has a streamlined shape for producing additional lift when counter-spinning the rotor. We provide a detailed dynamic model of our mono-spinner. Two configurations are studied: (1) a symmetric configuration, in which the rotor is aligned with the fuselage's COM, and (2) an asymmetric configuration, in which the rotor is located with an offset from the fuselage's COM. While the former can generate an in-place hovering flight condition, the latter can achieve trajectory tracking in 3D space by resolving the yaw and precession rates. Furthermore, it is shown that by introducing a tilting angle between the rotor and the fuselage, within the asymmetric design, one can further minimize the power consumption without compromising the overall stability. It is shown that an energy optimal solution can be achieved through the proper aerodynamic design of the mono-spinner for the first time.

This investigation focuses on servoing a robot's tool with respect to a smooth workpiece surface by making use of the surface's characteristic local differential properties. A novel formulation for 6 degree-of-freedom (DoF) textureless visual servoing based on these properties is proposed, which extends an existing 3-DoF scheme. Our approach naturally combines the geometric tools of computer-aided design and machining (CAD/CAM) theory with the manipulator control tools of visual servoing synergistically to achieve full 6-DoF pose control. A novel family of observed feature sets and their associated interaction matrices are presented. A geometric condition on the surface shape is derived under which local asymptotic stability for 6-DoF is guaranteed. Validation of the proposed method is performed in simulation and experiment using an articulated desktop robot equipped with only a monocular camera and 16 laser pointers.

The seminal work cited in [1],[2] showed, for the first time, that flight stability of quadcopters would be possible in case of one or even multiple rotor failures. However, the quadcopter can remain airborne only by going through a spinning maneuver about an axis, fixed w.r.t the vehicle (i.e., resolved yaw). Furthermore, positional control can be achieved by periodically tilting this axis. This paper builds upon this concept with two major improvements: (1) introducing a precise aerodynamic model of propellers that takes the flapping torque due to unbalanced lifting force in the advancing and retreating blades subjected to freestream, into account, and (2) adding to the stability and flight efficiency of the quadcopter by introducing symmetric fixed tilting angles to the trust vectors. In our previous work [3], it was shown how the flight stability and energy efficiency can be improved by introducing fixed tilting angles in the thrust vectors. For controlled crash landing in case of one rotor failure, where a resolved yaw maneuver would be inevitable, introducing a titling angle in rotors can generate a reasonable resolved-rate-yaw spinning speed to keep the quadcopter airborne at a lower rotational speed of the blades by taking advantage of the freestream generated by spinning. This tilting angle would also lead to passive stability in yaw motion of the quadcopter before the failure. Our hypothesis was successfully tested via simulations.

A special type of rotary-wing Unmanned Aerial Vehicles (UAV), called Quadcopter have prevailed to the civilian use for the past decade. They have gained significant amount of attention within the UAV community for their redundancy and ease of control, despite the fact that they fall under an under-actuated system category. They come in a variety of configurations. The “+” and “x” configurations were introduced first. Literature pertinent to these two configurations is vast. However, in this paper, we define 6 additional possible configurations for a Quadcopter that can be built under either “+” or “x” setup. These configurations can be achieved by changing the angle that the axis of rotation for rotors make with the main body, i.e., fuselage. This would also change the location of the COM with respect to the propellers which can add to the overall stability. A comprehensive dynamic model for all these configurations is developed for the first time. The overall stability for these configurations are addressed. In particular, it is shown that one configuration can lead to the most statically-stable platform by adopting damping motion in Roll/Pitch/Yaw, which is described for the first time to the best of our knowledge.

Visual servoing techniques are proven to be beneficial in unstructured workspaces. However, visual servoing is bounded by several constraints and is prone to the uncertainties of the system, leaving it of limited applicability. Several previous works have tackled these problems; yet, most of these works considered only a partial set of the aforementioned shortcomings. This work proposes a novel two-stage controller that is capable of minimizing the uncertainties of the system, while handling the constraints properly. The effectiveness of the controller is put into test via various simulations and experiments. The experimental results confirm the usefulness and applicability of this controller.

Visual servoing methods have proven their usefulness for robot control in unstructured environments. However, the practicality of such methods highly depends on their robustness to system uncertainties and ability to handle the constraints of the system. Many of the previous works proposed effective remedies for constraint handling; yet, only a few of them considered the system uncertainties. This work proposes a novel control scheme for visual servoing which basically exploits a model predictive controller to handle the constraints. In addition to that, the uncertainty model of the system is developed to handle the constraints more efficiently. The simulation and experimental results confirm the effectiveness of the proposed control method for constraint handling, in the presence of system uncertainties.

Object pose estimation is of great importance to many applications, such as augmented reality, localization and mapping, motion capture, and visual servoing. Although many approaches based on a monocular camera have been proposed, only a few works have concentrated on applying multicamera sensor fusion techniques to pose estimation. Higher accuracy and enhanced robustness toward sensor defects or failures are some of the advantages of these schemes. This paper presents a new Kalman-based sensor fusion approach for pose estimation that offers higher accuracy and precision, and is robust to camera motion and image occlusion, compared to its predecessors. Extensive experiments are conducted to validate the superiority of this fusion method over currently employed vision-based pose estimation algorithms.

Robustness is the key feature for practical visual servoing systems. Uncertainty modeling in these systems is the first step towards accurate and robust visual servoing. Camera displacement error due to image noise has been investigated using the noise boundaries in the previous works. Also error covariance propagation has already been developed to model the uncertainties of different visual servoing system components. However, these works were either open-loop or not fully successful under controlled simulations. This work presents a new error modeling method based on error covariance propagation in closed-loop visual servoing systems, considering the discrete-time nature of the system. The proposed framework could be employed for a broad range of controllers. Simulation results suggest the effectiveness of the proposed method.

The main objective of this paper is to develop an efficient method for learning and reproduction of complex trajectories for robot programming by demonstration. Encoding of the demonstrated trajectories is performed with hidden Markov model, and generation of a generalized trajectory is achieved by using the concept of key points. Identification of the key points is based on significant changes in position and velocity in the demonstrated trajectories. The resulting sequences of trajectory key points are temporally aligned using the multidimensional dynamic time warping algorithm, and a generalized trajectory is obtained by smoothing spline interpolation of the clustered key points. The principal advantage of our proposed approach is utilization of the trajectory key points from all demonstrations for generation of a generalized trajectory. In addition, variability of the key points' clusters across the demonstrated set is employed for assigning weighting coefficients, resulting in a generalization procedure which accounts for the relevance of reproduction of different parts of the trajectories. The approach is verified experimentally for trajectories with two different levels of complexity.

In this paper, Internet-based teleoperation of mobile robots for obstacle avoidance is analyzed. A shared impedance-control scheme is presented, and the results of an experimental study for the evaluation of the effects of different teleoperation parameters are reported. In the experimental study, the effects of time delay, operator training, image-display alternatives (virtual model versus real images), viewpoint, and force-reflection method were studied. For this purpose, several hypotheses were formulated and tested through the experiments using the introduced quantitative and qualitative measures. A fuzzy force-reflection controller is also proposed as an alternative force-reflection technique, and its performance is compared with a conventional proportional-derivative-type force-reflection method. The experimental scheme was implemented using MATLAB XPC Target and Simulink. The results could serve as guidelines in the design of teleoperation systems for obstacle avoidance and could also provide directions for further investigations.

The problem of estimating position and orientation (pose) of an object in real time constitutes an important issue for vision-based control of robots. Many vision-based pose-estimation schemes in robot control rely on an extended Kalman filter (EKF) that requires tuning of filter parameters. To obtain satisfactory results, EKF-based techniques rely on “known” noise statistics, initial object pose, and sufficiently high sampling rates for good approximation of measurement-function linearization. Deviations from such assumptions usually lead to degraded pose estimation during visual servoing. In this paper, a new algorithm, namely iterative adaptive EKF (IAEKF), is proposed by integrating mechanisms for noise adaptation and iterative-measurement linearization. The experimental results are provided to demonstrate the superiority of IAEKF in dealing with erroneous a priori statistics, poor pose initialization, variations in the sampling rate, and trajectory dynamics.

Microassembly has become an important technique to fabricate micro devices with different materials, complex shapes, and incompatible processes. Vision-based microassembly is a promising technique for automated microassembly. However, conventional vision-based techniques in microassembly are limited by inherent problems such as a small depth-of-field (DOF) and a narrow field-of-view (FOV). Microassembly operations initially need to detect micro parts in a wide FOV and a large DOF yet also maintain high resolution for the final state. A tradeoff between the DOF (and/or FOV) and resolution prevents the conventional systems from satisfying the aforementioned microassembly requirements. This paper presents an active zooming control method that enables dynamic adjustment of the DOF (FOV) according to the position and the focus measure of micro objects. The proposed method is based on an artificial potential field method with the capability to combine different kinds of constraints such as the FOV, the focus measure, and the joint limits into the system. The stability and robustness of the proposed system are also investigated. The novelty of this method is that it can ensure the vision system maintains a wide FOV and a large DOF initially, and high resolution at the end. Simulations and microassembly experimental results are provided to verify the feasibility of the proposed approach.

This paper presents a haptic rendering technique for a telemanipulation system of deformable objects using image processing techniques and physically based modeling. The interaction forces between an instrument driven by a haptic device and a deformable object are inferred in real time based on a continuum mechanics model of the object, which consists of a boundary element model and a prior knowledge of the object’s mechanical properties. These models allow users to feel reaction forces during manipulation tasks through a haptic device. Macro- and micro-scale experimental systems, equipped with a telemanipulation system and a commercial haptic display, were developed and tested using silicone (macro-scale) and zebrafish embryos (micro-scale). The developed algorithm can be used with a needle operation robot or a cellular injection system.

This paper presents a kinematic and dynamic model to predict the arm posture when high velocity or acceleration motion of the end-effector (hand) is required. Two measures, directional dynamic dexterity (DDD) and directional kinematic dexterity (DKD) are defined and applied to predict the human arm posture. It is shown that DDD predicts the postures in which the hand has a high acceleration and DKD predicts the postures in which hand adopts a high velocity. The results are verified by comparing the predicted postures with practical postures required in high acceleration and velocity applications.

Catheters are flexible devices widely used in minimally invasive cardiac surgeries. The limited maneuverability of currently available catheters necessitates a control system by which the catheter is moved inside the heart with more precision. To realize such a system, the first step is to model the catheter's movements in real-time. In this paper, a simplified approach is proposed to kinematically characterize a general single-segment active catheter. In this approach, the distal shaft of the flexible catheter is modeled as a rigid manipulator with coupled joints. To demonstrate the validity of the new model, experiments are conducted by using an existing steerable ablation catheter.

This paper presents a new kinematic method to evaluate the human visual perceptibility in the workspace. The results of the method determine the regions of the workspace from which human visual system (HVS) has high quality images in most of the eye and neck postures. To evaluate the image quality of points in the HVS, motion perceptibility and acuity measures are defined and computed for the points that are in the focus range and field-of-view (FOV) of the observer. Motion perceptibility in the HVS is evaluated by applying a motion resolvability concept for the first time. The perceptibility measures are averaged over all the eye poses to determine the average visual perceptibility of the human workspace. The method is implemented on a subject with average body sizes, however, it can be easily adapted to different human subjects

Omni directional mobile robots have been popularly employed in several applications especially in soccer player robots in Robocup competitions. However, control and self-localization of omni directional mobile robots are important issues and different teams in the Robocup competitions have used different techniques to tackle it. Since it is very complicated to calculate the omni directional system transfer function as in classic control, a simplified model of the system would be helpful for estimating the PID coefficients of the robots' position and orientation control. The vision-based self-localization combined with the odometry system enables us to have a robust self-localization method. The findings have been tested in the Robocup competition field using three Persia middle size omni directional robots.

In this paper, a class of parallel manipulators known as Rotary Positioners (RP) will be analyzed. A Rotary Positioner is actuated by six circular motors on the circular base and does not involve any prismatic joint. Therefore, it has constant length limbs. A model of an RP is presented. The constraints related to passive joints and links interference are formulated and the workspace of a normalized RP is computed accordingly. The optimal combination of the main parameters for maximizing the workspace volume of the RP is also given.

In many practical problems of online system identification, the distribution of observed samples is uneven. For instance, at points where system is idle or changes slowly, the sample density increases and where system moves quickly, it is reduced. This generally results in performance degradation of learning. We will propose a new algorithm for training RBF networks that is particularly developed for online learning with uneven sample distribution. The basic idea is to find peaks and stick to them. Experiments show a notable improvement in convergence rate, settling of weights and error minimization.

In this paper, the problem of combined target model estimation and position-based visual servoing is addressed. The target object is assumed to be stationary with at least 3 distinguishable feature points. The midpoint triangulation method and a rough estimation method are developed for initial estimation of the target model and relative pose of target in current end-effector frame. A novel decoupled EKF-based online estimation algorithm is proposed to improve the target model and relative pose estimation simultaneously under continuous robot dynamic motion. This new method is robust to large initial estimation errors and provides consistent and accurate target model estimation for optimal pose estimation as required in position-based visual servoing. Experimental results are given to demonstrate the performance of the proposed method.

Many real world applications would involve grasp of large objects in unstructured environments. Agent-based approach to multi-robot grasp of objects would prove useful under the above circumstances. In this paper, the problem of form closure grasp for planar convex objects by multiple robots is tackled. Contrary to the previous approaches, no a priori information about the shape of the object is assumed, and the robots are not allowed to fully communicate among themselves. A distributed multi-agent based approach using Q-learning is proposed. The state space, action set and learning algorithm are formulated. The results are verified through simulations using a developed Q-learning test bed.

Feature selection and planning are integral parts of visual servoing systems. Because many irrelevant and nonreliable image features usually exist, higher accuracy and robustness can be expected by selecting and planning good features. Assumption of perfect radiometric conditions is common in visual servoing. The following paper discusses the issue of radiometric constraints for feature selection in the context of visual servoing. Here, radiometric constraints are presented and measures are formulated to select the optimal features (in a radiometric sense) from a set of candidate features. Simulation and experimental results verify the effectiveness of the proposed measures.

In this paper, the dynamic performance of position-based visual servoing methods in the Cartesian and image spaces are investigated. Two major classifications of the position-based method are analyzed. For motion control defined in the stationary desired end-effector frame, the shortest straight line Cartesian trajectory can be achieved, but the corresponding image trajectories are not controlled and may leave the camera field of view. For motion control defined in the moving current end-effector frame, the straight line Cartesian trajectory is not guaranteed, however, the im- age trajectories can be indirectly controlled in this method to move approximately along straight lines when the commanded orientation is small. Simulation results are given to compare the Cartesian and image trajectories of various implementations of the position-based method with those of the image-based and 2 1/2-D methods.

This paper presents a theoretical comparison of the stability and robustness of the two basic visual servoing methods, image-based and position-based visual servoing. A common framework for comparison based on the energy shaping plus damping injection methodology is presented. Previous stability results for the image-based method are extended to show the robustness of stability with respect to camera and target modeling, errors. Similar stability and robustness analyses are extended to the analysis of the position-based method. It is shown that both methods are locally asymptotically stable and robust with respect to camera and target modeling errors. However the stability boundary with respect to above errors has not been established.

In this paper, hybrid motion control and planning strategies for image constraints avoidance in robot visual servoing are presented. The proposed methods consist of a local switching control method and a global hybrid trajectory planning method. In local switching control, image local minima and image singularities can both be resolved by hybrid image-based and position-based visual servoing. The global hybrid trajectory planner is based on the artificial potential field method and various Cartesian and image constraints are incorporated. Based on the proposed methods, motion planning for image boundaries and image local minima avoidance is considered in this paper. Experimental and simulation results are given to demonstrate the efficiency of the proposed methods.

Traditional looper control methods cannot deal effectively with unmodeled dynamics and large variations which can lead to scrap runs and damages to machinery in steel industry. This paper presents the design of a fuzzy looper control with neural-net based tuning of rule-base and defuzzification. The effects of various design options are discussed and practical conclusions are made. Also, the results are compared with the results of different initial rule-bases, PID control, and with membership function tuning.

The extended Kalman filter has been shown to produce accurate pose estimates for visual servoing, assuming that the dynamic noise covariance is known prior to application. Poor estimation of the dynamic noise covariance matrix, Q, can lead to large tracking error or divergence. This paper discusses the use of an adaptive filtering technique to update Q. This provides robust object tracking with unknown trajectory for a visual servoing system with little increase in computational cost. Furthermore, an approximation to a maximum likelihood method with a limited memory filter is proposed, for a time-efficient pose-based visual servoing system.

Looper control is one of the challenging problems in rolling mills. The purpose of the paper is to propose an intelligent control method using fuzzy logic and neural networks for improved the performance of looper control over conventional loop control methods. The focus of the paper is on the rule-tuning aspect of the proposed fuzzy looper control. Simulation results are presented to verify the performance of the control system.

In this paper, several issues of looper control in rolling mills are discussed and a fuzzy logic control is proposed to address the enumerated issues. The proposed system incorporated fast tuning algorithms for both off-line and online learning of membership functions and singleton values. Also, it is relatively simple to design and implement. The effectiveness of the proposed system is verified by the simulations results. It is shown that the proposed system is more robust to parameter uncertainties and noises, when compared with conventional PID control.

Vision based grasping holds great promise for grasping in dynamic environments where the object and/or robot are moving. The paper introduces a grasp planning approach for visual servo controlled robots with a single camera mounted at the end effector. Sensory control, mechanical, and geometrical issues in the design of such an automatic grasp planner are discussed and the corresponding constraints are highlighted. In particular, the integration of visual feature selection constraints is emphasized. Some quality measures are introduced to rate the candidate grasps. The grasp planning strategy and implementation issues in the development of an automatic grasp planner (AGP) are described. Simulation and experimental results are presented to verify the correctness of the approach and its effectiveness in dealing with dynamic situations.

The problem of automatically selecting the image features is an important aspect of visual servoing systems. This paper addresses the main issues relevant to automatic features selection in the context of visual servoing. The constraints imposed by the task, the feature extraction and state estimation processes are derived. The quality measures are formulated and a features selection strategy is introduced. In addition to the features selection, the proposed methodology provides the automatic selection of windows for the target features during the servoing. Also it can be applied for optimal vision sensor positioning. The design and implementation of an automatic CAD-based features selector (AFS) using the proposed methodology has been achieved. The experimental results are presented to demonstrate the effectiveness and correctness of the proposed framework.

This paper deals with the integration of a real-time trajectory planner and neural network (CMAC) based controller with on-line learning for robotic manipulators in the presence of obstacles. The effectiveness of the integrated system is verified by simulations for the first two links of the Adept-One robot.>

In this article a fast and simple method for the motion planning of telerobotic manipulators is presented. It is based on solving numerically a linear system of equations that includes a simple null space vector for obstacle avoidance, and an efficient procedure for the appropriate damping of the pseudoinverse matrix. This approach enables the pursuance simultaneously, of both collision avoidance and singularities prevention on-line in a sensor based environment. These properties also make it suitable for entirely autonomous operations.>