Robotica

This paper describes a new robotic manipulator with three fingers based on an origami twisted tower design. The design specifications, kinematic description, and results from the stiffness and durability tests for the selected origami design are presented. The robotic arm is made of a 10-layer twisted tower, actuated by four cables with pulleys driven by servo motors. Each finger is made of a smaller 11-layer tower and uses a single cable directly attached to a servo motor. The current hardware setup supports vision-based autonomous control and internet-based remote control in real time. For preliminary evaluation of the robot's object manipulation capabilities, arbitrary objects with varying weights, sizes, and shapes (i.e., a shuttlecock, an egg shell, a paper cub, and a cubic block) were selected and the rate of successful grasping and lifting for each object was measured. In addition, an experiment comparing a rigid gripper and the new origami-based manipulator revealed that the origami structure in the fingers absorbs the excessive force applied to the object through force distribution and structural deformation, demonstrating its potential applications for effective manipulation of fragile objects.

This paper presents a novel system for human–robot interaction in object-grasping applications. Consisting of an RGB-D camera, a projector and a robot manipulator, the proposed system provides intuitive information to the human by analyzing the scene, detecting graspable objects and directly projecting numbers or symbols in front of objects. Objects are detected using a visual attention model that incorporates color, shape and depth information. The positions and orientations of the projected numbers are based on the shapes, positions and orientations of the corresponding objects. Users select a grasping target by indicating the corresponding number. Projected arrows are then created on the fly to guide a robotic arm to grasp the selected object using visual servoing and deliver the object to the human user. Experimental results are presented to demonstrate how the system is used in robot grasping tasks.

There is a large gap between reality and grasp models that are currently available because of the static analysis that characterizes these approaches. This work attempts to fill this need by proposing a control law that, starting from an initial contact state which does not necessarily correspond to an equilibrium, achieves dynamically a stable grasp and a relative finger orientation in the case of pinching an object with arbitrary shape via rolling soft fingertips. Controlling relative finger orientation may improve grasping force manipulability and allow the appropriate shaping of the composite object consisted of the distal links and the object, for facilitating subsequent tasks. The proposed controller utilizes only finger proprioceptive measurements and is not based on the system model. Simulation and experimental results demonstrate the performance of the proposed controller with objects of different shapes.

The Figure 4 in the original version has a stylistic error.

It ought to use the figure as follows: The authors apologise for this error.

In this study, the kinematics and dynamics of a six-degree-of-freedom parallel manipulator, known as Hexarot, are evaluated. Hexarot is classified under axis-symmetric robotic mechanisms. The manipulator comprises a cylindrical base column and six actuated upper arms, which are connected to a platform through passive joints and six lower arms. The actuators of the mechanism are located inside a cylindrical-shaped base, which allows the mechanism to rotate infinitely about the axes of the latter column. In the context of kinematics, the inverse-kinematic problem is solved using positions, velocities, and accelerations of the actuated joints with respect to the position, orientation, and motion of the platform. Accordingly, the main objective of this study is to dynamically model the manipulator using the Newton–Euler approach. For validation, the obtained dynamic model of the Hexarot manipulator is simulated in MATLAB based on the formulations presented in this paper. The kinematic and dynamic models of the manipulator are simulated for a given motion scenario using MATLAB and ADAMS. The results of the mathematical model obtained using MATLAB are in good agreement with that using the ADAMS model, confirming the effectiveness of the proposed mathematical model.

This paper presents a novel reconfigurable parallel mechanism, which can serve as a machine tool. The proposed parallel mechanism can change its structure parameters by driving a bevel gear system fixed in the base platform. First, the forward and inverse kinematics of the proposed mechanism are investigated. Second, the reachable workspace and Jacobian matrix are conducted. Based on the Jacobian matrix, the stiffness model and dexterity of the end effector are developed in detail. Finally, a multi-objective optimization is performed by using the Genetic Algorithm, and the workspace and global performance indexes of stiffness as well as the dexterity are considered as the performance indices to improve the performance of the reconfigurable parallel mechanism. Finally, Pareto frontier figure and several tables are provided to illustrate the results of the optimization. The results showed the proposed method has improved the performance of the reconfigurable machine tool in terms of its stiffness and dexterity.

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.

We present an approach that allows a robot to generate trajectories to perform a set of instances of a task using few physical trials. Specifically, we address manipulation tasks which are highly challenging to simulate due to complex dynamics. Our approach allows a robot to create a model from initial exploratory experiments and subsequently improve it to find trajectory parameters to successfully perform a given task instance. First, in a model generation phase, local models are constructed in the vicinity of previously conducted experiments that explain both task function behavior and estimated divergence of the generated model from the true model when moving within the neighborhood of each experiment. Second, in an exploitation-driven updating phase, these generated models are used to guide parameter selection given a desired task outcome and the models are updated based on the actual outcome of the task execution. The local models are built within adaptively chosen neighborhoods, thereby allowing the algorithm to capture arbitrarily complex function landscapes. We first validate our approach by testing it on a synthetic non-linear function approximation problem, where we also analyze the benefit of the core approach features. We then show results with a physical robot performing a dynamic fluid pouring task. Real robot results reveal that the correct pouring parameters for a new pour volume can be learned quite rapidly, with a limited number of exploratory experiments.

This study focuses on a door-opening mobile manipulator operating in four phases (reaching the door, grasping the door handle, turning the door handle, and pulling the door). We use force/torque feedback-based control, achieving compliance of the mobile base when it comes into contact with the handle. A method is proposed for estimating the unknown force-related constraints from manipulator joint position measurements. A robust adaptive control strategy is developed for tracking the planned trajectory to open the door. Finally, a mobile manipulator opens a real door with a locked latch and unknown force-related constraints, demonstrating the validity of the proposed approach.

This paper addresses a problem of complete sensing coverage in 3D environments. We propose a distributed random algorithm to drive mobile robotic sensors on the vertices of a truncated octahedral grid for complete sensing coverage of a bounded 3D area. Furthermore, we develop a distributed algorithm for the self-deployment of mobile sensors to form a desired 3D geometric shape on the vertices of the truncated octahedral grid. These algorithms are developed based on some consensus rules that only rely on local information. The proposed algorithms utilize 3D grids for the coverage task. Several simulations are conducted to illustrate the validity of the proposed distributed sensing coverage and formation building algorithms for a mobile robotic sensor network. Also, we give mathematically rigorous proof of the convergence with probability 1 of our proposed algorithms.

Stability, high response quality and rapidity are required for reactive omnidirectional walking in humanoids. Early schemes focused on generating gaits for predefined footstep locations and suffered from the risk of falling over because they lacked the ability to suitably adapt foot placement. Later methods combining stride adaptation and center of mass (COM) trajectory modification experienced difficulties related to increasing computing loads and an unwanted bias from the desired commands. In this paper, a hierarchical planning framework is proposed in which the footstep adaption task is separated from that of COM trajectory generation. A novel omnidirectional vehicle model and the inequalities deduced therefrom are adopted to describe the inter-pace connection relationship. A constrained nonlinear optimization problem is formulated and solved based on these inequalities to generate the optimal strides. A black-box optimization problem is then constructed and solved to determine the model constants using a surrogate-model-based approach. A simulation-based verification of the method and its implementation on a physical robot with a strictly limited computing capacity are reported. The proposed method is found to offer improved response quality while maintaining rapidity and stability, to reduce the online computing load required for reactive walking and to eliminate unnecessary bias from walking intentions.

In this paper, a safe map building and area search algorithm for a mobile robot in a closed unknown environment with obstacles is presented. A range finder sensor is used to detect the environment. The objective is to perform a complete search of the environment and build a complete map of it while avoiding collision with the obstacles. The developed robot navigation algorithm is randomized. It is proved that with probability 1 the robot completes its task in a finite time. Computer simulations and experiments with a real Pioneer-3DX robot confirm the performance of the proposed method.

This paper focuses on the problem of robust time-optimal trajectory planning of robotic manipulators to track a given path under a probabilistic limited actuation, that is, the probability for the actuation to be limited is no less than a given bound κ. We give a general and practical method to reduce the probabilistic constraints to a set of deterministic constraints and show that the deterministic constraints are equivalent to a set of linear constraints under certain conditions. As a result, the original problem is reduced to a linear optimal control problem which can be solved with linear programming in polynomial time. In the case of κ = 1, the original problem is proved to be equivalent to the linear optimal control problem. Overall, a very general, practical, and efficient algorithm is given to solve the above problem and numerical simulation results are used to show the effectiveness of the method.

This paper proposes a collision avoidance method for the teleoperation of multiple non-holonomic mobile robots from multiple users. Each human operator drives a mobile robot, where each one performs an independent task in a common workspace. To avoid collisions, the proposed method only acts on the speed of the mobile robots; therefore, the human operator can freely drive the robot over the path he chooses to. The developed analysis allows us to assure that a solution is always achieved. Finally, the results of the experiments are shown, in order to test the performance of the proposed control scheme.

This paper discusses the application of a constraint-based model predictive control (MPC) to mobile manipulation tracking problems. The problem has been formulated so as to guarantee offset-free tracking of piecewise constant references, with convergence and recursive feasibility guarantees. Since MPC inputs are recomputed at every control iteration, it is possible to deal with dynamic and unknown scenarios. A number of motion constraints can also be easily included: Acceleration, velocity and position constraints have been enforced, together with collision avoidance constraints for the mobile base and the arm and field-of-view constraints. Such constraints have been extended over the prediction horizon maintaining a linear-quadratic formulation of the problem. Navigation performance has been improved by devising an online algorithm that includes an additional goal to the problem, derived from the classical vortex field approach. Experimental validation shows the applicability of the proposed approach.

This paper addresses the formation tracking control of a group of tractor–trailer systems in the presence of model uncertainties. A virtual leader–follower formation technique is used to design a controller in order to force a team of tractor–trailer systems to construct a desired formation configuration. Since tractor–trailer systems have a nonlinear multi-input multi-output model with strong couplings, multi-layer neural networks are employed to overcome unknown nonlinearities and uncertain parameters by using on-line weight tuning algorithms. Neural network approximation errors and external disturbances are also compensated with adaptive robust signals. The dynamic surface control approach has been used to reduce the complexity of the proposed controller effectively. Lyapunov’s direct method proves that all signals in the closed-loop formation control system are bounded and tracking errors converge to a neighborhood of the origin whose size is adjustable. Finally, simulation results will be provided to illustrate the efficiency of the proposed controller.

Robotic hands use rolling contact to manipulate a grasped object to a desired location, even when the finger and the palm linkage mechanisms lack degrees of freedom. This paper presents a systematic approach to the forward and inverse kinematics of in-hand manipulation. The moving frame method in differential geometry is integrated into the product of exponential formula to establish a pure geometric framework of the kinematics of a robot hand. The forward and inverse kinematics of a multifingered hand are obtained in terms of the joint rates and contact trajectories. A two-fingered planar robot hand and a three-fingered spatial robot hand are used to demonstrate the proposed approach. The proposed formulation amounts to solving a univariate polynomial, providing an alternative to the existing ones that require numerical integration.

Mirage is a camera pose estimation method that analytically solves pose parameters in linear time for multi-camera systems. It utilizes a reference camera pose to calculate the pose by minimizing the 2D projection error between reference and actual pixel coordinates. Previously, Mirage has been successfully applied to trajectory tracking (visual servoing) problem. In this study, a comprehensive evaluation of Mirage is performed by particularly focusing on the area of camera pose estimation. Experiments have been performed using simulated and real data on noisy and noise-free environments. The results are compared with the state-of-the-art techniques. Mirage outperforms other methods by generating fast and accurate results in all tested environments.

We present an analytical method for the concurrent calculation of optimal parallel compliant elements and frequency of reference trajectories for serial manipulators performing cyclic tasks. In this approach, we simultaneously shape and exploit the robot's natural dynamics by finding a set of compliant elements and task frequency that result in minimization of an energy-based cost function. The cost function is the integral of a weighted squared norm of the generalized forces. We prove that the generalized force needed for tracking the reference trajectory is a linear function of compliance coefficients and a quadratic function of task frequency. Therefore, the cost function is quadratic with respect to stiffness coefficients and quartic with respect to the task frequency. These properties lead to a well-posed optimization problem with a closed-form solution. Using three case studies, we elucidate the properties of our method.

This paper introduces the methodology of the dimensional synthesis for a 3UPS-PRU parallel robot. The dimensional synthesis of the 3UPS-PRU parallel robot is proposed considering the maximum input velocity of actuating joints as the objective function and constraints on the installation dimension, robot dimension, joint rotation angle and interference. The objective of the dimensional synthesis is to minimize the maximum input velocity of actuating joints when the moving platform translates along the z-axis in the maximum linear velocity and rotates about an arbitrary axis in the maximum angular velocity in the desired workspace. The constraint on the robot dimension is included in the dimensional synthesis of the 3UPS-PRU parallel robot when pursuing the kinematic property to meet the miniaturization principle with the reduced building cost. An example of the dimensional synthesis of a 3UPS-PRU parallel robot is presented with the maximum linear velocity and angular velocity required for the moving platform in the desired workspace.