A new motion-free iterative closest point (ICP) algorithm is proposed for building a two-dimensional (2D) map for mobile robot navigation. A laser range finder (LRF) sensor is installed on a mobile robot to scan and measure the depth data of the environment to form a 2D map during mobile robot navigation. Because the scanning and navigation motions are performed independently, the scanned data contain distortions from the motions of the mobile robot. To compensate for the distortions, the proposed motion-free ICP algorithm estimates the effects of the dynamic motions of the robot on the scanning process. That is, the motion-free algorithm compensates for the distance measurement errors related to the dynamic changes in the mobile robot's velocity. Experiments were performed with actual velocity changes of a mobile robot to demonstrate and verify the effective performance of the proposed algorithm.

There is growing interest in robots that are designed specifically to interact with people and which respond to voice commands. Very little attention has been paid, however, to the kind of verbal interaction that is possible or desirable with robots. This paper presents recent work in multimodal interfaces that addresses this question. It proposes a new form of robot-user interface, namely a collaborative conversational interface. This article explains what collaborative conversational interfaces are, argues for their application in robots, and presents strategies for designing good conversational interfaces. It concludes with a discussion of the particular challenges faced in designing conversational interfaces for robots.

A generally accepted helicopter model used for control includes the effect of Small Body Forces (SBF) which couple the vehicle's rotational subsystem inputs to its translational dynamics. SBF result from tail rotor thrust and lateral forces due to main rotor flapping. It is well-known that SBF lead to a theoretically challenging stabilization problem for the tracking error dynamics. Hence, much of the existing work has neglected SBF in order to simplify control design. We design a controller that directly compensates the influence of the tail rotor component of the SBF. The design is validated in simulation and flight tests.

The given article emphasizes the development and modeling of a hybrid navigational controller to optimize the path length and time taken. The proposed navigational controller is developed by hybridizing the metaheuristic moth–flame optimization (MFO) approach and the reinforcement learning (RL) approach. Input parameters like obstacle and target locations are fed to the MFO controller that implements a proper navigational direction selection. It forwards to the RL controller, which exercises further refinement of the output turning angle around obstacles. The collaboration of the global MFO approach with the local-based RL approach helps to optimize the path traversed by the humanoid robot in an unknown environment. The major breakthrough in this article is the utilization of humanoid robots for navigation purposes between various checkpoints. The humanoid robots are placed in a cluttered environment and assigned specific target positions to complete the assigned tasks. In the case of a multi-humanoid robot system, to avoid self-collision, it requires a Petri-Net controller to be configured in the navigation system to prevent deadlock situations and enhance the smooth completion of tasks without inter-collision among the humanoid robots. Simulations and real-time experiments are undertaken using different controllers involving single- and multi-humanoid robot systems. The robustness of the proposed controller is also validated in dynamic environment. Comparisons are carried with an established navigational controller in a similar environmental setup, which proves the proposed hybrid controller to be robust and efficient.

Mechanical regulator-free bicycle robots have lighter weight and fewer actuators than the traditional regulator-based bicycle robots. In order to deal with the difficulty of maintaining balance for this kind of bicycle robot, we consider a front-wheel drive and mechanical regulator-free bicycle robot. We present the methodologies for realizing the robot's ultra-low-speed track-stand motion, moderate-speed circular motion and high-speed rectilinear motion. A simplified dynamics of the robot is developed using three independent velocities. From the dynamics, we suggest there may be an underactuated rolling angle in the system. Our balancing strategies are inspired by human riders' experience, and our control rules are based on the bicycle system's underactuated dynamics. In the case of track-stand and circular motion, we linearize the frame's rolling angle and configure the robot to maintain balance by the front-wheel's motion with a fixed front-bar turning angle. In the case of the rectilinear motion, we linearize both front-bar steering angle and front-wheel rotating angle, and configure the system to maintain balance by the front-bar's turning with a constant front-wheel rotating rate. Numerical simulations and physical experiments are given together to validate the effectiveness of our control strategies in realizing the robot's proposed three motions.

In this paper, the robust, optimal, output control problem is dealt with for a 3-degree-of-freedom laboratory helicopter. The control goal is to achieve the practical tracking of the desired elevation and pitch angles without the angular velocity feedback. A nominal linear time-invariant system is introduced and the real system is considered as the nominal one with uncertainties, including parameter perturbations, nonlinear time-varying uncertainties, and external disturbances. An observer is first used to estimate angular velocity. Then a nominal controller based on the optimal control method is designed for the nominal system to achieve the desired tracking properties. Lastly, a robust output compensator is added to restrain the effects of uncertainties in the real system. It is shown that asymptotic tracking properties and robust stability can be achieved. Experimental results on the laboratory helicopter are shown to verify the effectiveness of the proposed control method.

This paper is devoted to the study of systems of entities that are capable of generating other entities of the same kind and, possibly, self-reproducing. The main technical issue addressed is to quantify the requirements that such entities must meet to be able to produce a progeny that is not degenerative, i.e., that has the same reproductive capability as the progenitor. A novel theory that allows an explicit quantification of these requirements is presented. The notion of generation rank of an entity is introduced, and it is proved that the generation process, in most cases, is degenerative in that it strictly and irreversibly decreases the generation rank from parent to descendent. It is also proved that there exists a threshold of rank such that this degeneracy can be avoided if and only if the entity has a generation rank that meets that threshold – this is the von Neumann rank threshold. On the basis of this threshold, an information threshold is derived, which quantifies the minimum amount of information that must be provided to specify an entity such that its descendents are not degenerative. Furthermore, a complexity threshold is obtained, which quantifies the minimum length of the description of that entity in a given language. As an application, self-assembly for a 2 Degrees of Freedom planar robot is considered, and simulation results are presented. A robot arm capable of picking up and placing the components of another arm, in the presence of errors, is considered to have successfully reproduced if these are placed within an allowable tolerance. The example shows that, due to the kinematics of the robot, errors can grow from one generation to the next, until the reproduction process fails eventually. However, error correction (via error sensing and feedback control) can then be used to prevent such degeneracy. The von Neumann generation rank and information thresholds are computed for this example, and are consistent with the simulation results in predicting degeneracy in the case without error correction, and predicting successful self-reproduction in the case with error correction.

In the process of trajectory optimization for robot manipulator, the path that is generated may deviate from the intended path because of the adjustment of trajectory parameters, if there is limitation of end-effector path in Cartesian space for specific tasks, this phenomenon is dangerous. This paper proposes a methodology that is based on the Pareto front to address this issue, and the methodology takes into account both the multi-objective optimization of robotic arm and the quality of end-effector path. Based on dung beetle optimizer, this research proposes improved non-dominated sorting dung beetle optimizer. This paper interpolates manipulator trajectory with quintic B-spline curves, achieves multi-objective trajectory optimization that simultaneously optimizes traveling time, energy consumption, and mean jerk, proposes a trajectory selection strategy that is based on Pareto solution set by introducing the concept of Fréchet distance, and the strategy enables the end-effector to approach the desired path in Cartesian space. Simulation and experimental results validate the effectiveness and practicability of the proposed methodology on the Sawyer robot manipulator.

Handling and manipulating flexible porous objects is one of the main challenges in robotics for household and industrial tasks. Improving the design of grippers for flexible objects of manipulation is an important stage in the development of this topic. This article proposes a method of modeling a gripper for porous objects using the finite element method. It identifies the main parameters of the model that will affect the grasping force and the permeability of porous objects. The power characteristics of the obtained gripper model for different supply pressures, with varying porosity of the manipulated objects, are determined. The obtained characteristics are then used to find the correspondence of channel length for three textile materials with different permeable properties. An experimental study of the lifting force is conducted, and a comparison is made with the obtained modeling data for the presented samples. Additionally, using the obtained simulation data, an analysis of the pressure distribution on the surface of the porous object of manipulation is performed. As a result, it is found that the gripping device must use a design with elements to stabilize the distribution of pressure in its chamber, which will increase the stability of the gripping process.

The loading and unloading operations of smart logistic application robots depend largely on their perception system. However, there is a paucity of study on the evaluation of Lidar maps and their SLAM algorithms in complex environment navigation system. In the proposed work, the Lidar information is finetuned using binary occupancy grid approach and implemented Improved Self-Adaptive Learning Particle Swarm Optimization (ISALPSO) algorithm for path prediction. The approach makes use of 2D Lidar mapping to determine the most efficient route for a mobile robot in logistical applications. The Hector SLAM method is used in the Robot Operating System (ROS) platform to implement mobile robot real-time location and map building, which is subsequently transformed into a binary occupancy grid. To show the path navigation findings of the proposed methodologies, a navigational model has been created in the MATLAB 2D virtual environment using 2D Lidar mapping point data. The ISALPSO algorithm adapts its parameters inertia weight, acceleration coefficients, learning coefficients, mutation factor, and swarm size, based on the performance of the generated path. In comparison to the other five PSO variants, the ISALPSO algorithm has a considerably shorter path, a quick convergence rate, and requires less time to compute the distance between the locations of transporting and unloading environments, based on the simulation results that was generated and its validation using a 2D Lidar environment. The efficiency and effectiveness of path planning for mobile robots in logistic applications are validated using Quanser hardware interfaced with 2D Lidar and operated in environment 3 using proposed algorithm for production of optimal path.

The main objective of this paper is a presentation of an experimental identification of a non-direct drive robot and load dynamic parameters, which appear in the integral model. The last one is based on the energy theorem formulation. In the robotics literature there are not many experimental results known to the authors, concerning the identification of the dynamic parameters of different models. In order to satisfy this, the experimental system has been built around an industrial ASEA IRp-6 robot. In this paper we propose to precompute the friction characteristics which are separated in the integral model. Various aspects of the exciting trajectories are considered. It is shown how to identify the friction coefficients using a short integral model. The experimental results are presented, including comparison of the results for both integral and differential identification. The identified models are verified by computing the predicted torques and trajectories

The flexible structure of a robot multi-links manipulator can be either a side effect or, on the contrary, an essential feature. We present a fairly general model to derive the corresponding dynamic equations in quite a systematic and simple way. To this end, we use the Lagrange formulation with strain energy potential and Raleigh (dissipation) functions. The approach can incorporate torsional deformation and aerodynamic friction, and it applies easily to robots working in the sea. The trajectory control appears to be one in the presence of model imprecision, and a slightly modified version of the classical sliding control technique is utilized to design the tracking control of the manipulator. Then we introduce the time-varying inertia link device (carried out by means of sliding masses) which we suggested in earlier work, and we show how it can be used to improve the tracking control scheme above. This paper contributes new ideas concerning flexible multi-links arms and active inertia links.

This paper presents a method for automatic forming and solving dynamic equations of motion of manipulator with elastic joints and rigid segments. For the minimal configuration of a cylindrical manipulator the numerical integration of these equations is performed, and vibrations in the joints are obtained as dynamic errors with respect to the prescribed trajectory.

A time-optimal problem for redundantly actuated robots moving on a specified path is a challenging problem. Although the problem is well explored and there are proposed solutions based on phase plane analysis, there are still several unresolved issues regarding calculation of solution curves. In this paper, we explore the characteristics of the maximum velocity curve and propose an efficient algorithm to establish the solution curve. Then we propose a straightforward method to calculate the maximum or minimum possible acceleration on the path based on the pattern of saturated actuators, which substantially reduces the computational cost. Two numerical examples are provided to illustrate the issues and the solutions.

This paper proposes a globally and exponentially convergent predictive observer for attitude and position estimation based on landmark measurements and velocity (angular and linear) readings. It is assumed that landmark measurements are available with time-delay. The maximum value of the sensor delay under which the estimation error converges to zero is calculated. Synthesis of the observer is based on a representation of rigid-body kinematics and sensor delay, formulated via ordinary and partial differential equations (ODE-PDE). Observability condition specifies necessary and sufficient landmark configuration for convergence of attitude and position estimation error to zero. Finally, for implementation purposes, a PDE-free realization of the predictive observer is proposed. Simulation results are presented to demonstrate performance and convergence properties of the predictive observer in case of a wheeled mobile robot.

In this paper, we propose a unique, decoupled 3 degree-of-freedom (DOF) parallel wrist. The condition required for synthesizing a fully isotropic parallel mechanism is obtained on the basis of the physical meaning of the row vector in the Jacobian matrix. Specifically, an over-constrained spherical 3-DOF parallel mechanism is presented and the modified structure, which avoids the redundant constraints, is also introduced. The proposed manipulator is capable of decoupled rotational motions around the x, y, and z axes and contains an output angle that is equal to the input angle. As this device is analyzed with the Jacobian matrix, the mechanism is free of singularity within its workspace and maintains homogenous stiffness over the entire workspace.

Most of the past research in swarm robotics has considered object capture and transport using a specified and very large number of agents. The objects therein were either stationary or moving deterministically (i.e., along a known path). In most previous efforts, the obstacles were also considered stationary. Here we present a modified projective path planning algorithm and illustrate via laboratory experiments that an object exhibiting stochastic (unplanned) but low-speed motion can be restrained by a limited number of agents guided in real-time across randomly moving obstacles. Relaxation of certain restrictions in the grasping objective allows for the determination of a minimum number and placement of agents around the perimeter of any generically shaped prismatic object. A closed loop experiment is designed using a single overhead camera that provides the visual feedback and helps determine the instantaneous positions of all entities in the workspace. Control signals are sent to the robots via wireless modules by a central processing unit to navigate and guide them to their respective new positions in the subsequent time-step. Agents continue to receive signals until they restrict the moving object in form closure.

The performance of robotic manipulators is limited by the nature of the control systems which do not satisfactorily integrate the non-linear phenomena associated with the dynamic behavior of the mechanisms. The significant variations in the axial inertias lead to control problems and require an optimization of the mechanical structures in order to improve the stability of the manipulators. This paper proposes mechanical solutions in the domain of dynamic decoupling of robots and demonstrates, using numerical simulations the value of these solutions in terms of control.

A new mathematical formulation of robot and obstacles is presented such that for on-line collision recognition only robot joint positions in workspace are required. This reduces calculation time essentially because joint positions in workspace can be computed every time from the joint variable through robot geometry. It is supposed that the obstacles in the workspace of the manipulator are represented by convex polygons. For every link of the redundant robot and every obstacle a boundary ellipse in 2D is defined in workspace such that there is no collision if the robot joints are outside these ellipses. First, some methods are presented for the automatic determination of these ellipse functions from the obstacle and robot data. Then, the boundary ellipse functions are used as optimization criterion in a collision-avoidance method. The method permits the tip of the hand to follow a given path in the free space while the kinematic control algorithm maximizes the boundary ellipse function of the critical link. The effectiveness of the proposed methods is discussed by theoretical considerations and illustrated by simulations of the motion of three- and four-link manipulators between obstacles.

This paper presents a Kalman filtering technique for reducing errors in locating 3-D objects using a sensor system. The location information is employed to control the motion of an industrial robot to pick up the objects. The sensor consists of a rigid platform mounted on a flexible column. Each object to be located is placed on the sensor. The static deflections and natural frequencies of vibrations of the sensor are measured and processed to determine the position and orientation of the object. In practice, the sensor signals obtained are corrupted with noise leading to errors in location determination. A Kalman filter is used to reduce the noise present in the sensor system.