When employing autonomous helicopters, it is desirable to use navigation approaches, which firmly ensure safety. In this paper, we propose and compare two approaches to navigation through environments containing obstacles. The first uses sliding mode boundary following to maintain a prespecified distance to obstacles, and the second uses a model predictive control approach to plan short horizon trajectories around detected objects, while ensuring that the helicopter is brought to a halt within the sensor visibility radius. The navigation approaches are subjected to analysis for robustness, and simulations are carried out with a realistic helicopter model for verification. Additional real-world experiments were performed with a wheeled robot to demonstrate potential for real-time application.

Spherical robot is a special kind of nonholonomic system that cannot be converted to chained form, which means most of the well-known control methodologies are not suitable for this system. For the trajectory tracking of BHQ-1, a spherical robot designed by our lab, a two-state trajectory tracking controller is proposed in this paper. First, the kinematic model of the robot is built using screw theory and exponential method and the controllability is proved based on the differential geometric control theory. Then to solve the two-state trajectory tracking problem of BHQ-1, a shunting model of neurodynamics and Lyapunov's direct method are combined to design a two-state trajectory tracking controller, of which the Lyapunov stability is validated. Finally, typical simulation examples, such as tracking linear, circular, and sinusoidal trajectories, are introduced to verify the effectiveness of the proposed controller. In this paper the proposed method can also be applied to the control of other spherical robots.

Wireless Sensor Network (WSN) localization has shown a growing research interest, thanks to the expected proliferation of WSN applications. This work is focused on indoor localization of a mobile robot in a WSN using only inter-node range measurements, which are estimated by radio frequency signal strength attenuation. These measurements are affected by different sources of uncertainty that make them highly noisy and unreliable. The proposed approach makes use of fuzzy logic for modeling and dealing with such uncertain information. Besides, the position estimation is enhanced using a rough description of indoor environment. The experiments show that the proposed localization approach (i) is fault-tolerant, (ii) results feasible in low-density WSNs, and (iii) provides better position estimations than well-known localization methods when the position measurements are affected by high uncertainty.

There exist several kinds of definitions for complexity in different areas. In mechanical engineering area, some formulations capable of measuring the complexity of robotic architectures have been proposed at the conceptual-design stage. This paper presents the definition of Kinematic Coupling Complexity to measure the kinematic coupling degree of a manipulator based on the input/output velocity matrix. In order to distinguish the differences among many matrixes, three factors will be considered including the proportion of nonzero elements in matrix J, the difference of nonzero elements' numbers in every row among many matrixes, and the difference of nonzero elements' positions among many matrixes. To illustrate the methods introduced in this paper, four types of heavy-payload forging manipulators are analyzed and the type of forging manipulator with the smallest Kinematic Coupling Complexity is chosen as our prototype.

This paper presents a seven-link dynamic walking model that is more close to human beings than other passivity-based dynamic walking models. We add hip actuation, upper body, flat feet, and ankle joints with torsional springs to the model. Walking sequence of flat-feet walkers has several substreams, which forms bipedal walking with dynamic series of phases. We investigate the effects of ankle stiffness on gait selection and evaluate different gaits in walking velocity, efficiency, and stability. Experimental results indicate that ankle stiffness plays different roles in different gaits and the gaits, which are more close to human walking with moderate speed, achieve better motion characteristics.

A biomimetic underwater vehicle, which is propelled by two undulating long-fins, is introduced in this paper. The undulating or oscillating movements of symmetrical long-fins cause the complex locomotion of biomimetic underwater vehicle. For convenience, three motion modes are proposed and considered firstly. Then an inertial unit is installed for collection of accelerations and angular velocity. The underwater vehicle's MIMO model is reduced into a SISO model by some simplifications. A sine wave function deduced from the long-fin's time-varying membrane is proposed and used as the input of the biomimetic underwater vehicle ARMA model, and velocity or angular velocity is considered as the model output. The algorithms based on recursive weighted least squares are applied for model parameter identification. Experiments carried out with a long-fin propelled underwater vehicle. The experimental results show that the proposed methods can build valid locomotion models for three motion modes efficiently.

The localization problem in mobile robotics can be defined as the search of the robot's coordinates in a known environment. If there is no information about the initial location, we are talking about global localization. In this work, we have developed an algorithm that solves this problem in a three-dimensional (3D) environment using evolutionary computation concepts. The method has been called RELF-3D and has many features that make it very robust and reliable: thresholding and discarding mechanisms, different cost functions, effective convergence criteria, and so on. The resulting global localization module has been tested in numerous experiments and the most important improvement obtained is the accuracy of the method, allowing its application in manipulation tasks.

Measuring a driver's level of attention and drowsiness is fundamental to reducing the number of traffic accidents that often involve bus and truck drivers, who must work for long periods of time under monotonous road conditions. Determining a driver's state of alert in a noninvasive way can be achieved using computer vision techniques. However, two main difficulties must be solved in order to measure drowsiness in a robust way: first, detecting the driver's face location despite variations in pose or illumination; secondly, recognizing the driver's facial cues, such as blinks, yawns, and eyebrow rising. To overcome these challenges, our approach combines the well-known Viola–Jones face detector with the motion analysis of Shi–Tomasi salient features within the face. The location of the eyes and blinking is important to refine the tracking of the driver's head and compute the so-called PERCLOS, which is the percentage of time the eyes are closed over a given time interval. The latter cue is essential for noninvasive driver's alert state estimation as it has a high correlation with drowsiness. To further improve the location of the eyes under different conditions of illumination, the proposed method takes advantage of the high reflectivity of the retina to near infrared illumination employing a camera with an 850 nm wavelength filter. The paper shows that motion analysis of the salient points, in particular cluster mass centers and spatial distributions, yields better head tracking results compared to the state-of-the-art and provides measures of the driver's alert state.

In this work, accuracy enhancement through backlash elimination is considered. When a nonredundantly actuated parallel manipulator is subjected to a wrench while following a trajectory, required actuator torque switching (going from positive to negative or vice versa) may occur. If backlash is present in the actuation hardware for a manipulator, torque switching compromises accuracy. When in-branch redundant actuation is added, a pseudoinverse torque solution requires smaller joint torques, but torque switching may still occur. A method is presented where concepts of exploiting a nullspace basis of the joint torques are used to ensure that single sense joint torques can be achieved for the actuated joints. The same sense torque solutions are obtained using nonlinear optimization. The methodology is applied to several examples simulating parallel manipulators in machining applications.

In this paper a 3-degree of freedom compliant parallel positioning stage utilizing flexure hinges is explored for nanoimprint lithography. The performance of the stage is extensively analyzed using a pseudo-rigid body model and finite element method. The position and velocity models are established. Accordingly, the stiffness at driving point of the active arm is obtained on the basis of Castigliano's first theorem. The system stiffness of the compliant stage is explored using the compliant matrix methodology and matrix transformation, and the influence of the geometry parameters on stiffness factors in three directions has been graphically evaluated as well. Finite element analysis has been conducted to verify that the established models faithfully predict device performance.

A computational derivation of valid kinematic limbs for spatial 3-DOF parallel mechanisms (PMs) without redundant constraint is studied based on contracted graphs, topological graphs, and basic joints. First, some contracted graphs without any binary links are constructed, some curves with only binary links are distributed over contracted graphs and many valid topological graphs are derived. Second, a software is developed in Visual Basic for deriving the kinematic limb, and some basic chain structures are constructed by connecting various basic joints in series. Third, all valid kinematic limbs of the 3-DOF PMs without redundant constraint are derived computationally from the basic chain structures and some novel 3-DOF PMs are synthesized using this approach. Finally, the number of the different 3-DOF PMs without redundant constraint are determined based on the topological graphs and valid chain structures of the limb.

This paper presents a new method using hierarchical optimal control for path planning and calculating maximum allowable dynamic load (MADL) of wheeled mobile manipulator (WMM). This method is useful for high degrees of freedom WMMs. First, the overall system is decoupled to a set of subsystems, and then, hierarchical optimal control is applied on them. The presented algorithm is a two-level hierarchical algorithm. In the first level, interaction terms between subsystems are fixed, and in the second level, the optimization problem for subsystems is solved. The results of second level are used for calculating new estimations of interaction variables in the first level. For calculating MADL, the load on the end effector is increased until actuators get into saturation. Given a large-scale robot, we show how the presenting in distributed hierarchy in optimal control helps to find MADL fast. Also, it enables us to treat with complicated cost functions that are generated by obstacle avoidance terms. The effectiveness of this approach on simulation case studies for different types of WMMs as well as an experiment for a mobile manipulator called Scout is shown.

Autonomous operation of mechanical systems often requires the ability to detect and locate a particular phenomenon occurring in the surrounding environment. Being implemented to articulated manipulation, such a capability may realize a wide range of applications in autonomous maintenance and repair. This paper presents the sensor-driven task space control of an end-effector that combines the field estimation and the target tracking in an unknown spatial field of interest. The radial basis function network is adopted to model spatial distribution of an environmental phenomenon as a scalar field. Their weight parameters are estimated by a recursive least square method using collective measurements from the on-board sensors mounted to the manipulator. Then the asymptotic source tracking has been achieved by the control law based on the gradient of the estimated field. A new singularity tolerant scheme has been suggested to command the task space control law despite singular configurations. Simulation results using the three-link planar robot and the 6-revolute elbow manipulator are presented to validate the main ideas.

Nature has always inspired engineers. This research tries to understand the contribution of snake anatomy in its locomotion from engineering point of view to be adopted in the design of snake robots. Rib design and muscular structure of snake robots will have a great impact on snake robot flexibility, weight, and actuators' torque. It will help to eliminate wheels in snake robots during serpentine locomotion. The result of this research shows that snakes can establish the required peg points on smooth surfaces by deflecting the body and ribs. The results are verified by both field observations and simulation.

This paper addresses the map merging problem, which is the most important issue in multi-robot simultaneous localization and mapping (SLAM) using the Rao–Blackwellized particle filter (RBPF-SLAM) with unknown initial poses. The map merging is performed using the map transformation matrix and the pair of map merging bases (MMBs) of the robots. However, it is difficult to find appropriate MMBs because each robot pose is estimated under multi-hypothesis in the RBPF-SLAM. In this paper, probabilistic map merging (PMM) using the Gaussian process is proposed to solve the problem. The performance of PMM was verified by reducing errors in the merged map with computer simulations and real experiments.

This paper proposes a prediction system and a command fusion to help the human operator in a teleoperation system of a mobile robot with time-varying delay and force feedback. The command fusion is used to join a remote controller and the delayed user's commands. Besides, a predictor is proposed since the future trajectory of the mobile robot is not known a priori being it decided online by the user. The command fusion and predictor are designed based on the time delay and the current context measured through the crash probability. Finally, the proposed scheme is tested from teleoperation experiments considering time-varying delay as well as force feedback.

Wire-actuated parallel manipulators and their failures are studied in this paper taking into consideration their failure modes. A methodology for investigating the effect of wire/actuator failures on the force/moment capability of manipulators is presented, and the criteria for full and partial recovery from these failures are established. The methodology is also applicable for the cases that the minimum norm solution for the vector of wire tensions gives a negative value for tension by treating the corresponding wire as failed. The proposed criteria are also valid for the manipulators that utilize hybrid actuation of wires and joints. Three planar wire-actuated parallel manipulators are used as the case study to illustrate the proposed methodology and criteria.

Robotic-assisted surgery is a continuously developing field because robots have demonstrated clear benefits in operating rooms. Until now, vast majority of robots used in surgery had serial structures. This paper presents the kinematic modelling of a 5-degree of freedom hybrid parallel architecture in two slightly different variants. The kinematics of this structure is determined, and following the analysis of singularities, the best variant is chosen. The robot workspace is computed and finally the experimental model and some simulation results are presented.

Human posture prediction can often be formulated as a nonlinear multiobjective optimization (MOO) problem. The joint displacement function is considered as a benchmark of human performance measures. When the joint displacement function is used as the objective function, posture prediction is a MOO problem. The weighted-sum method is commonly used to find a Pareto solution of this MOO problem. Within the joint displacement function, the relative value of the weights associated with each joint represents the relative importance of that joint. Usually, weights are determined by trial and error approaches. This paper presents a systematic approach via an inverse optimization approach to determine the weights for the joint displacement function in posture prediction. This inverse optimization problem can be formulated as a bi-level optimization problem. The design variables are joint angles and weights. The cost function is the summation of the differences between two set of joint angles (the design variables and the realistic posture). Constraints include (1) normalized weights within limits and (2) an inner optimization problem to solve for joint angles (predicted posture). Additional constraints such as weight limits and weight linear equality constraints, obtained through observations, are also implemented in the formulation to test the method. A 24 degree of freedom human upper body model is used to study the formulation and visualize the prediction. An in-house motion capture system is used to obtain the realistic posture. Four different percentiles of subjects are selected to run the experiment. The set of weights for the general seated posture prediction is obtained by averaging all weights for all subjects and all tasks. On the basis of obtained set of weights, the predicted postures match the experimental results well.

Robot navigation is a large area of research, where many different approaches have already been tried, including navigation based on visual memories. The Sparse Distributed Memory (SDM) is a kind of associative memory based on the properties of high-dimensional binary spaces. It exhibits characteristics, such as tolerance to noise and incomplete data, ability to work with sequences and the possibility of one-shot learning. Those characteristics make it appealing to use for robot navigation. The approach followed here was to navigate a robot using sequences of visual memories stored into a SDM. The robot makes intelligent decisions, such as selecting only relevant images to store, adjusting memory parameters to the level of noise and inferring new paths from the learnt trajectories. The method of encoding the information may influence the tolerance of the SDM to noise and saturation. This paper reports novel results of the limits of the model under different typical navigation problems. The SDM showed to be very robust to illumination and scenario changes, occlusion and saturation. An algorithm to build a topological map of the environment based on the visual memories is also described.