This paper introduces a new RGBD-Simultaneous Localization And Mapping (RGBD-SLAM) based on a revisited keyframe SLAM. This solution improves the localization by combining visual and depth data in a local bundle adjustment. Then, it presents an extension of this RGBD-SLAM that takes advantage of a partial knowledge of the scene. This solution allows using a prior knowledge of the 3D model of the environment when this latter is available which drastically improves the localization accuracy. The proposed solutions called RGBD-SLAM and Constrained RGBD-SLAM are evaluated on several public benchmark datasets and on real scenes acquired by a Kinect sensor. The system works in real time on a standard central processing units and it can be useful for certain applications, such as localization of lightweight robots, UAVs, and VR helmet.

In this paper, an integrated mathematical model for the balancing and sequencing problems of a mixed-model assembly line (MMAL) is developed. The proposed model minimizes the total overload and idleness times. For the sake of reality, the impact of operator’s learning and fatigue issues on the optimization of the assembly line balancing and sequencing problems is considered. Furthermore, it is assumed that the Japanese mechanism is used in this assembly line to deal with the overload issue. With respect to the complexity level of the proposed model, a genetic algorithm is developed to solve the model. In order to set the parameters of the developed genetic algorithm, the well-known Taguchi method is used and the efficiency of this solution method is compared with the GAMS software using several test problems with different sizes. Finally, the sensitivity of the balancing and sequencing problems to the parameters such as station length, learning rate, and fatigue rate are analyzed and the impact of changing these parameters on the model is studied.

This paper considers the task allocation problem under the requirement that the assignments of some critical tasks must be maximized when the network capacity cannot accommodate all tasks due to the limited capacity for each unmanned aerial vehicle (UAV). To solve this problem, this paper proposes an extended performance impact algorithm with critical tasks (EPIAC) based on the traditional performance impact algorithm. A novel task list resizing phase is developed in EPIAC to deal with the constraint on the limited capacity of each UAV and maximize the assignments of critical tasks. Numerical simulations demonstrate the outstanding performance of EPIAC compared with other algorithms.

In this article, we investigate the problem of parameter identification of spatial–temporal varying processes described by a general nonlinear partial differential equation and validate the feasibility and robustness of the proposed algorithm using a group of coordinated mobile robots equipped with sensors in a realistic diffusion field. Based on the online parameter identification method developed in our previous work using multiple mobile robots, in this article, we first develop a parameterized model that represents the nonlinear spatially distributed field, then develop a parameter identification scheme consisting of a cooperative Kalman filter and recursive least square method. In the experiments, we focus on the diffusion field and consider the realistic scenarios that the diffusion field contains obstacles and hazard zones that the robots should avoid. The identified parameters together with the located source could potentially assist in the reconstruction and monitoring of the field. To validate the proposed methods, we generate a controllable carbon dioxide (CO2) field in our laboratory and build a static CO2 sensor network to measure and calibrate the field. With the reconstructed realistic diffusion field measured by the sensor network, a multi-robot system is developed to perform the parameter identification in the field. The results of simulations and experiments show satisfactory performance and robustness of the proposed algorithms.

This paper presents a novel method for modeling a 3-degree of freedom open kinematic chain using quaternions algebra and neural network to solve the inverse kinematic problem. The structure of the network was composed of 3 hidden layers with 25 neurons per layer and 1 output layer. The network was trained using the Bayesian regularization backpropagation. The inverse kinematic problem was modeled as a system of six nonlinear equations and six unknowns. Finally, both models were tested using a straight path to compare the results between the Newton–Raphson method and the network training.

This paper presents a novel solution for precision draping of prepreg composite fabrics onto double curved molds. Our contributions relate to system design, including hardware and software components, and to system integration. On the hardware side, design and implementation of a drape tool with up to 120 suction cups positioned individually by linear actuators are described. On the software side, design and implementation of the software architecture are presented, along with necessary algorithms within sensor technologies and mathematical modeling. The essential system’s components were verified individually, and the entire integrated system was successfully validated in the Proof-of-Concept experiments, performed on an experimental physical model of the system.

In this work, a planar cable parallel robot (CPR) driven by four cable-and-pulley differentials is proposed and analyzed. A new cable-and-pulley differential is designed by adding an extra pulley to eliminate the modeling inaccuracies due to the pulley radius and obviate the need of solving the complex model which considers the pulley kinematics. The design parameters of the proposed CPR are determined through multi-objective optimal design for the largest total orientation wrench closure workspace (TOWCW) and the highest global stiffness magnitude index. The proposed differentially driven CPR is evaluated by comparing various performance indices with a fully actuated CPR.

In this paper, a sliding mode control using a control point concept is proposed for an under-actuated quadrotor. The proposed controller controls the position of the control point, a displaced point from the quadrotor’s geometric center, and the yaw angle. This method solves singularity issues in control matrix inversion and enables the utilization of the multi-input multi-output equation to derive the control inputs. The sliding surface is designed to control four outputs while stabilizing roll and pitch angles. Simulation and experimental results show the effectiveness and robustness of the proposed controller in the tracking of a trajectory under parametric uncertainties.

This paper proposes a mobile parallel robot (MPR) and focuses on obstacle avoidance. When analyzing the collision-free trajectories, the coupling constraints caused by the parallel mechanism and the obstacle should be emphatically solved. The solution is to divide the problem into two steps. First, the genetic algorithm is employed to search and optimize the feasible trajectories under the mechanism constraint of the MPR. Then the trajectory tracking controller is designed to make the tracked vehicles move cooperatively and track a trajectory asymptotically. Finally, simulations and experiments are carried out to verify the effectiveness of the solution.

This paper proposes a shared control scheme which aims to achieve a stable control of the speed and turn of a bipedal robot during a delayed bilateral teleoperation. The strategy allows to get a delay-dependent damping value that must be injected to assure a bounded response of the hybrid system, while simultaneously, the human operator receives a force feedback that help him to decrease the synchronism error. Furthermore, a test where a human operator handles the walking of a simulated bipedal robot, to follow a curve path in front of varying time delay, is performed and analyzed.

This paper presents a general framework for human-like motion control of 7-DOF S-R-S-redundant manipulators. The new framework simultaneously accomplishes five objectives: Cartesian trajectory tracking, obstacle avoidance, joint limit avoidance, human-like movement, and a feasibility evaluation of the Cartesian trajectory.We exhaustively compute all feasible arm configurations. This allows for quick evaluations of the feasibility of the Cartesian trajectories. They are applied to inverse kinematics of the redundant manipulator to improve the capability to handle multiple constraints, and enable the manipulator to imitate human movements. The efficiency of the proposed framework is demonstrated by kinematic experiments with a humanoid robot.

The soft actuator is made of superelastic material and embedded flexible material. In this paper, a kind of soft tube was designed and used to assemble two kinds of pneumatic soft actuators. The experiment and finite element analysis are used to comprehensively analyze and describe the bending, elongation, and torsion deformation of the soft actuator. The results show that the two soft actuators have the best actuation performance when the inner diameter of the soft tube is 4 mm. In addition, when the twisting pitch of the torsional actuator is 24 mm, its torsional performance is optimized. Finally, a device that can be used in the production line was assembled by utilizing those soft actuators, and some operation tasks were completed. This experiment provides some insights for the development of soft actuators with more complex motions in the future.

In this paper, a novel dynamic navigational planning strategy is proposed for single as well as multiple humanoids in intricate environments on a glowworm-based optimization method. The sensory information regarding the obstacle distances and target information are supplied as inputs to the navigational model. The essential turning angle is generated as the output of the controller to avoid obstacles present in the environment and reach the target location with ease. The proposed model is certified in a V-REP simulation software, and the simulation results are authenticated in a real-time setup arranged under testing conditions.

In this paper, a new type of biped mobile robot is designed. Each leg of the robot is a 6 degree-of-freedom (DOF) parallel mechanism, and each leg has three relatively fixed landing points. The leg’s structure gives the robot better performance on large carrying capacity, strong environmental adaptability and fast moving speed simultaneously. At the same time, it helps the robot move more steadily and change direction more simply. Based on the structural features of the leg, the inverse kinematics model of the biped robot is established and a unified formula is obtained. According to an analysis of robot’s workspace, gait planning is completed and simulated. Finally, the special case that the robot can keep the upper body horizontal while walking on a slopy surface is validated.

This paper addresses robust stability and position tracking problems in teleoperation systems subject to varying delay in the communication medium, uncertainties in the models of manipulators, and non-passive interaction forces in the terminations. Fixed-structure nonlinear control law is developed based on the notion of Interconnection and Damping Assignment Passivity-Based Control (IDA-PBC) scheme. Then, utilizing the Lyapunov–Krasovskii theorem, sufficient conditions are derived in terms of Linear Matrix Inequalities (LMIs) to tune the controller parameters. Differently from literature, the objectives are achieved without requirement for any passive parts in the model of interaction forces. Comparative simulations and experimental results demonstrate the applicability and superiority of the proposed method.

Conventional planar manipulators have their links in a single plane. Increasing payload at the end effector/mobile platform can induce high stress in the links due to the cantilever nature of links. Thus, it limits the total vertical load that can be applied on the mobile platform. In contrast to the links in conventional planar parallel mechanisms, non-planar links are proposed in this paper, that is, links are made inclined to the horizontal plane and non-planar legs are constructed. Though the links are made non-planar, the end effectors’ planar motion is retained. For studying the application of such non-planar links in planar manipulators, new models of inertia, stiffness and leg dynamics have to be developed. In this article, these models are developed by the static analysis of the planar manipulators with non-planar links, and the performance is compared with the corresponding conventional planar manipulators.

Accurate torque control is a critical issue in the compliant human–robot interaction scenario, which is, however, challenging due to the ever-changing human intentions, input delay, and various disturbances. Even worse, the performances of existing control strategies are limited on account of the compromise between precision and stability. To this end, this paper presents a novel high-performance torque control scheme without compromise. In this scheme, a new nonlinear disturbance observer incorporated with equivalent control concept is proposed, where the faster convergence and stronger anti-noise capability can be obtained simultaneously. Meanwhile, a continuous fractional power control law is designed with an iteration method to address the matched/unmatched disturbance rejection and global finite-time convergence. Moreover, the finite-time stability proof and prescribed control performance are guaranteed using constructed Lyapunov function with adding power integrator technique. Both the simulation and experiments demonstrate enhanced control accuracy, faster convergence rate, perfect disturbance rejection capability, and stronger robustness of the proposed control scheme. Furthermore, the evaluated assistance effects present improved gait patterns and reduced muscle efforts during walking and upstair activity.

Suppose in a given planar circular region, there are smart mobile evaders and we want to find them using sweeping agents. We assume the sweeping agents are in a line formation whose total length is predetermined. We propose procedures for designing a sweeping process that ensures the successful completion of the task, thereby deriving conditions on the sweeping velocity of the linear formation and its path. Successful completion of the task means that evaders with a given limit on their velocity cannot escape the sweeping agents. We present results on the search time given the initial conditions.

This paper addresses the issue of monitoring spatial environmental phenomena of interest utilizing information collected by a network of mobile, wireless, and noisy sensors that can take discrete measurements as they navigate through the environment. It is proposed to employ Gaussian Markov random field (GMRF) represented on an irregular discrete lattice by using the stochastic partial differential equations method to model the physical spatial field. It then derives a GMRF-based approach to effectively predict the field at unmeasured locations, given available observations, in both centralized and distributed manners. Furthermore, a novel but efficient optimality criterion is then proposed to design centralized and distributed adaptive sampling strategies for the mobile robotic sensors to find the most informative sampling paths in taking future measurements. By taking advantage of conditional independence property in the GMRF, the adaptive sampling optimization problem is proven to be resolved in a deterministic time. The effectiveness of the proposed approach is compared and demonstrated using pre-published data sets with appealing results.

Localization based on visual natural landmarks is one of the state-of-the-art localization methods for automated vehicles that is, however, limited in fast motion and low-texture environments, which can lead to failure. This paper proposes an approach to solve these limitations with an extended Kalman filter (EKF) based on a state estimation algorithm that fuses information from a low-cost MEMS Inertial Measurement Unit and a Time-of-Flight camera. We demonstrate our results in an indoor environment. We show that the proposed approach does not require any global reflective landmark for localization and is fast, accurate, and easy to use with mobile robots.