The goal of this study is to investigate the use of emotion as a braking system for wheelchair navigation. In the first part emotion is detected based on ElectroEncephalography (EEG) technology and emotion induction experiments. Using different techniques for features extraction (Welch and Wavelets), selection (Principal Component Analysis (PCA) and Genetic Algorithm (GA)) and classification (Support Vector Machine (SVM), Multi Layer Perceptron (MLP) and Linear Discriminate Analysis (LDA)), the best combination was assigned to Wavelets-GA-MLP. In the second part, in order to validate the impact of emotion as velocity modulator, a comparison between emotion-based and non emotion-based wheelchair navigation scenarios in a simulated environment was conducted. The assessment was based on four parameters: obstacles hit, navigation path, execution time and outbound points of gaze (POG). While the first two emotion introductions showed better results, this was not the case for the third. These findings can be utilized in order to prescribe a suitable wheelchair according to the subject pathology.

The balance control problem of planar bipedal robots during disturbed standing is investigated. Virtual holonomic constraints which specify the angles of actuated joints as a function of the rotation angle between the sole of the stance foot and the ground are recalled. These constraints enable the robot to avoid turnover during external disturbances similar to how a human being would react. Moreover, for a disturbance beyond the balance conditions for single-leg support, the robot changes to a double support posture to avoid turnover by the virtual holonomic constraints. Further, for a special kind of balance: standing on toe which leads to underactuation, we also give the design conditions of the virtual holonomic constraints.

This paper presents a solution to the problem of generating constrained robust trajectory planning for nonlinear mechatronic systems. By using an indirect variational solution method, the necessary optimality conditions deriving from the Pontryagin's minimum principle are imposed, and lead to a differential Two-Point Boundary Value Problem (TPBVP); numerical solution of the latter is accomplished by means of collocation techniques. The robustness to parametric mismatches is obtained trough the use of sensitivity functions, while a hard constraint on actuator effort is obtained using a smoothing technique. Numerical results shows that the robustness can be greatly improved, and that the inclusion of constraints on actuator effort does not affect it.

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.

This paper presents an improved Rao-Blackwellized particle filtering framework with consideration of the particle swarm characteristics in FastSLAM, called Relational FastSLAM or R-FastSLAM. The R-FastSLAM seeks to cope with the inherent problems of FastSLAM, i.e., a particle depletion problem and an error accumulation problem in large environments. The R-FastSLAM uses the particle swarm characteristics in calculating the importance weight and maintaining a particle formation. We assign more accurate weights to particles by clustering them using the Expectation-Maximization (EM) algorithm according to an adaptive weight compensation scheme. In addition, particles constitute an adaptive triangular mesh formation to maintain multiple data association hypotheses without any resampling step. Its outstanding accomplishments are verified on simulations and a test using the Victoria Park dataset by comparing the standard FastSLAM 2.0 with the particle swarm optimization based FastSLAM.

This paper investigates the trajectory tracking of a Stewart platform, which is a typical multi-input multi-output nonlinear system, with unmodeled dynamics, parameter uncertainties, friction, and unpredictable actuator faults. An adaptive vector sliding mode fault-tolerant control law is derived to ensure the system is insensitive to uncertainties and drive the state variable errors of the closed-loop system to converge to the origin. Moreover, novel adaptive laws are proposed to update the upper boundary of uncertainty according to the actual system state, which greatly reduces the chattering of sliding mode control. Furthermore, velocity signals are estimated by introducing a simple nonlinear observer, resulting in the proposed controller requiring position measurements only. Finally, numerical simulations illustrate the effectiveness of the proposed control scheme.

In robotics, path planning and trajectory optimization are usually performed separately to optimize the path from the given starting point to the ending point in the presence of obstacles. In this paper, path planning and trajectory optimization for robotic manipulators are solved simultaneously by a newly developed methodology called Discrete Mechanics and Optimal Control (DMOC). In DMOC, the Lagrange–d'Alembert equation is discretized directly unlike the conventional variational optimization method in which first the Euler–Lagrange equations are derived and then discretization takes place. In this newly developed method, the constraints for optimization of a desired objective function are the forced discrete Euler–Lagrange equations. In this paper, DMOC is used for simultaneous path planning and trajectory optimization for robotic manipulators in the presence of known static obstacles. Two numerical examples, applied on a DELTA parallel robot, are discussed to show the applicability of this new methodology. The optimal results obtained from DMOC are compared with the other state-of-the-art techniques. The difficulties and problems associated in using the DMOC for Parallel Kinematic Machine (PKM) are also discussed in this paper.

Single-support heel-off occurs when the heel of the trailing leg has been lifted from the ground around its toe, while the leading leg is still swinging forward. A similar gait event occurs during human walking, and is crucial to achieve a longer step length and a higher walking speed. In this paper, this crucial gait event is studied, specifically in how it influences the agility and the energy efficiency of bipedal walking. Toward this goal, the concept of limit-cycle bipedal walking which possesses natural and energy-efficient gaits is employed. The aforementioned concept is applied to a flat-foot bipedal model which is developed and actuated by a constant hip torque only during the single-support phase to walk on the ground. The impedance of each ankle is adjusted by using two springs, one at the back-side and the other at the front-side, as well as one damper. In comparison with point/round foot bipedal models, the flat-foot bipedal model produces more versatile limit-cycle gaits comprised of a number of gait series, each of which is a sequence detected among twelve gait postures dictated by the kinetics of the unilateral constraints at the heel, toe, or both. As a result of comprehensive simulations, it is concluded that single-support heel-off significantly improves the agility of bipedal walking because of the increase in the step length and the walking speed. Furthermore, even though limit-cycle gaits including single-support heel-off require higher energy input as compared with gaits excluding such an event, single-support heel-off significantly improves the energy efficiency of bipedal walking since the increase in the step length dominates the increase in the energy input.

Particle swarm optimization (PSO) is a heuristic optimization algorithm and is commonly used for the tuning of PD/PID-type controllers. In this paper, PSO is applied for control gain tuning of a position domain PID controller in order to improve contour tracking performances of linear and nonlinear contours for a serial multi-DOF robotic manipulator. A new fitness function is proposed for gain tuning based on the statistics of the contour error, and pre-existed fitness functions are also applied for the optimization process, followed by some comparison studies. The PSO tuning technique demonstrated the same effectiveness in position domain controllers as in time domain controllers with the results being quite satisfying with low contour errors for both linear and nonlinear contours, and the proposed fitness function is proved to be on par with the pre-existed fitness functions.

In this paper the control of flexible-joint manipulators while explicitly avoiding actuator saturation is considered. The controllers investigated are composed of a bounded proportional control term and a Hammerstein strictly positive real angular rate control term. This control structure ensures that the total torque demanded of each actuator is bounded by a value that is less than the maximum torque that each actuator is able to provide, thereby disallowing actuator saturation. The proposed controllers are shown to render the closed-loop system asymptotically stable, even in the presence of modeling uncertainties. The performance of the controllers is demonstrated experimentally and in simulation.

The N-3 Revolute-Prismatic-Spherical (N-3RPS) manipulator is a kind of serial-parallel manipulator and has higher stiffness and accuracy compared with serial mechanisms, and a larger workspace compared with parallel mechanisms. The locking mechanism in each joint allows the manipulator to be controlled by only three wires. Modeling the dynamics of this manipulator presents an inherent complexity due to its closed-loop structure and kinematic constraints. In the first part of this paper, the inverse kinematics of the manipulator, which consists of position, velocity, and acceleration, is studied. In the second part, the inverse and forward dynamics of the manipulator is formulated based on the principle of virtual work and link Jacobian matrices. Finally, the numerical example is presented for some trajectories.

A distributed control mechanism for ground moving nonholonomic robots is proposed. It enables a group of mobile robots to autonomously manage formation shapes while navigating through environments with obstacles. The mechanism consists of two stages, with the first being formation control that allows basic formation shapes to be maintained without the need of any inter-robot communication. It is followed by obstacle avoidance, which is designed with maintaining the formation in mind. Every robot is capable of performing basic obstacle avoidance by itself. However, to ensure that the formation shape is maintained, formation scaling is implemented. If the formation fails to hold its shape when navigating through environments with obstacles, formation morphing has been incorporated to preserve the interconnectivity of the robots, thus reducing the possibility of losing robots from the formation.

The algorithm has been implemented on a nonholonomic multi-robot system for empirical analysis. Experimental results demonstrate formations completing an obstacle course within 12 s with zero collisions. Furthermore, the system is capable of withstanding up to 25% sensor noise.

The paper deals with the development and application of a new adaptive calibration method that extends the geometrical calibration of mechanisms from calibration of only dimensions and kinematical joint positions into the calibration of kinematic joint shape imperfections. Originally unknown nonlinear properties of the kinematical pairs are adaptively included into the kinematical models that are used during the calibration calculation. The method uses description by local linear models and validity functions in order to identify and describe nonlinear properties of the kinematical pairs. During each calibration step, various models with growing complexity are considered before the best model variant is selected to improve calibration results. The method is mainly devoted to the structures with many loops like parallel and fiber-driven parallel mechanisms. The method is applied to parallel mechanism Sliding Star and parallel fiber-driven mechanism Quadrosphere.