We review a range of techniques related to navigation of unmanned vehicles through unknown environments with obstacles, especially those that rigorously ensure collision avoidance (given certain assumptions about the system). This topic continues to be an active area of research, and we highlight some directions in which available approaches may be improved. The paper discusses models of the sensors and vehicle kinematics, assumptions about the environment, and performance criteria. Methods applicable to stationary obstacles, moving obstacles and multiple vehicles scenarios are all reviewed. In preference to global approaches based on full knowledge of the environment, particular attention is given to reactive methods based on local sensory data, with a special focus on recently proposed navigation laws based on model predictive and sliding mode control.

For design using digital human models, human anthropometry data are required as input and are extracted from measurements. There is inherent error associated with these measurements which impacts the output of the simulation. Current techniques in digital human modeling applications primarily employ deterministic methods which are not well suited for handling variability in anthropometric measurement. An alternative to deterministic methods is probabilistic/sensitivity analysis. This study presents a probabilistic sensitivity approach to gain insights into how uncertainty in anthropometric measurements can affect the results of a digital human model with the specific application of vehicle-related reach tasks. Sensitivity levels are found to determine the importance of variability in each joint angle and link length to the final reach. A55-degree of freedom (DOF) digital human model is introduced to demonstrate the sensitivity approach for reach tasks. Seven right-hand reach target points and two left-hand reach target points (creating a total of 14 reach tasks) within a vehicle are used to compare the sensitivities in the joint angles and link lengths resulting from measurement uncertainty. The results show that the importance of each joint angle or link length is dependent on the characteristics of the reach task and sensitivities for joint angles, and link lengths are different for each reach task.

This paper presents a new monocular SLAM algorithm that uses straight lines extracted from images to represent the environment. A line is parametrized by two pairs of azimuth and elevation angles together with the two corresponding camera centres as anchors making the feature initialization relatively straightforward. There is no redundancy in the state vector as this is a minimal representation. A bundle adjustment (BA) algorithm that minimizes the reprojection error of the line features is developed for solving the monocular SLAM problem with only line features. A new map joining algorithm which can automatically optimize the relative scales of the local maps is used to combine the local maps generated using BA. Results from both simulations and experimental datasets are used to demonstrate the accuracy and consistency of the proposed BA and map joining algorithms.

For cable-driven parallel manipulators (CDPMs), it is known that maintaining positive cable tension is critical in constraining the moving platform. Hence, the force-closure workspace of CDPMs represents a set of poses where the cable tensions can balance arbitrary external wrench applied on the moving platform, proposed by researchers. A new computation method for the force-closure workspace of CDPMs is developed in this paper, and the new method is realized by calculating the null space of the structure matrix and solving the linear matrix inequalities. The detailed calculation procedures of the force-closure workspace for the incompletely restrained, completely restrained, and redundantly restrained CDPMs are given, respectively, and the advantages of the new method are analyzed according to the time complexity. The simulation experiments of the force-closure workspace computation are implemented on a six-degree of freedom (6-DOF) CDPM with eight cables, and then the superiority of the new method over the existing algorithm is studied.

This paper focuses on the derivation of valid contracted graphs (CGs) with ternary links T and quaternary links Q using characteristic strings for type synthesis of parallel mechanisms. First, some concepts on the associated linkage, the CGs, and characteristic strings are explained, and the numbers of vertices and edges for constructing CGs with T and Q are solved. Second, the relative criteria are determined for representing CGs with T and Q and identifying their isomorphism using characteristic strings. Third, some simple CGs with T and Q are represented by the characteristic strings and their isomorphic/invalid CGs are identified. Finally, many complex, valid CGs with T and Q are derived by characteristic strings.

This paper deals with the walking control problem of a compass-like biped robot with underactuated ankles in the framework of hybrid control systems. The compass-like biped robot is equipped with a constraint mechanism to lock the hip angle when the swing leg retracts. First, based on the Poincare return map, a limit cycle gait is obtained, and the stability of the gait is also checked. Then, a method based on transverse coordinate transformation is introduced to transform the problem of tracking a desired limit cycle into the stabilization problem of a linear time-invariant impulsive system. A feedback control design for stabilizing the walking gait is then presented. Finally, comparisons to several existing approaches for the similar model are provided to demonstrate the advantages of our proposed approach.

A sliding mode controller is designed to compensate for the flexibility uncertainties of a cable robot and improve its tracking performance. Of the most significant sources of these uncertainties are the elasticity of the cables and the flexibility of the joints. A favorable approach to improve the accuracy of the system is first to model the cable and joint flexibilities and then convert the model uncertainties into parametric uncertainties. Parametric uncertainties are the product of imprecise flexibility coefficients and are finally neutralized by a sliding mode controller. The flexibility in cables is modeled by considering the longitudinal vibration of the time-varying length cables. A simulation study is carried out to confirm the presented model and the positive effect of the designed controller. Then the impact of these uncertainties on the dynamic load carrying capacity (DLCC) of the robot is examined and compared for different cases. Finally, experimental tests are conducted on the IUST (Iran University of Science and Technology) cable-suspended robot to validate the presented theories and simulation results.

In this paper, an optimization design of a 6 DOF parallel measuring system is analyzed. First, a closed form direct kinematics formulation based on Cayley–Menger determinants is considered in the objective function, in order to measure the manipulator singularities, then an estimation of distribution algorithm is proposed to solve the optimization problem. It is shown that the evolutionary algorithm can find close to optimal solutions for minimum pose error estimation. Additionally, these global optimizers significantly reduce the computational burden in comparison with exhaustive search and other global optimization techniques. The sensitivity of the pose error estimation in the prescribed robots' workspace is analyzed and used to guide a designer in choosing the best structural configuration. Numerical examples are discussed to show the feasibility of the proposed optimization methodology.

An unmanned aerial vehicle (UAV) dynamic path planning method is proposed to avoid not only static threats but also mobile threats. The path of a UAV is planned or modified by the potential trajectory of the mobile threat, which is predicted by its current position, velocity, and direction angle, because the positions of the UAV and mobile threat are dynamically changing. In each UAV planning path, the UAV incurs some costs, including control costs to change the direction angle, route costs to bypass the threats, and threat costs to acquire some probability to be destroyed by threats. The model predictive control (MPC) algorithm is used to determine the optimal or sub-optimal path with minimum overall costs. The MPC algorithm is a rolling-optimization feedback algorithm. It is used to plan the UAV path in several steps online instead of one-time offline to avoid sudden and mobile threats dynamically. Lastly, solution implementation is described along with several simulation results that demonstrate the effectiveness of the proposed method.

This paper deals with the design of a three-degree-of-freedom (3-DoF) parallel kinematic mechanism (PKM) with high orientational capability. First, a type synthesis method based on Grassmann line geometry and a line-graph method is proposed, and a novel 3-DoF PKM is derived based on this method. Thereafter, the parasitic motions of the derived mechanism are identified under two different orientation description methods, i.e., Tilt-and-Torsion angles (T&T angles) and Roll-Pitch-Yaw angles (RPY angles), and the kinematic optimization considering the motion/force transmissibility is carried out. On this basis, the orientational capability of the discussed mechanism is investigated, and the high orientational capability is demonstrated. The design of the 3-DoF PKM in this paper is very meaningful to the development of the five-axis machine tools with hybrid architectures. The design methods of type synthesis and kinematic optimization can also be used in the design of other PKMs.

Slotine and Li's modification is a well-known, simple, and elegant approach for robot adaptive control to avoid the feedback of joint accelerations. This paper presents a simple strategy to implement a regressor-free adaptive impedance controller without using Slotine and Li's modification. In the new strategy, the joint acceleration vector and the dynamics of robot are assumed to be unavailable. Their effects are covered by using the function approximation technique so that there is no need for the joint acceleration feedback. The closed-loop stability and boundedness of internal signals are justified by the Lyapunov-like technique. Experimental results for a two-dimensional (2D) robot are presented to show the effectiveness of the proposed strategy.

This paper presents a novel method to determine the workspace of parallel manipulators using a variant of the Firefly Algorithm, which is one of the emerging techniques in swarm artificial intelligence. The workspace is defined as a set of all the coordinates in the search space that are accessible by the parallel manipulator end effector. The workspace formulation of the parallel manipulator considered in this paper has actuated and passive joint displacements which values are limited by their physical constraints. A special fitness function that discriminates between accessible and inaccessible coordinates is formulated based on the joint limitations. By finding these coordinates using the proposed Firefly Algorithm, the workspace of the manipulator can be constructed. The proposed method is an easy-to-implement alternative solution to the current numerical methods for workspace determination. The method consists of two stages of operation. The first stage maps the workspace to find the initial results with a space filling approach, in which a number of coordinates in the workspace are identified. The second stage refines the results with a boundary detection approach which focuses on the mapping of the boundaries of the workspace. The method is illustrated by its application to determine the 2D, 3D, and 6D workspaces of a Gough--Stewart Platform manipulator. Furthermore, the method is compared with a more rigorous interval analysis method in terms of computational cost and accuracy.

This paper presents a new genetic algorithm methodology to solve the trajectory planning problem. This methodology can obtain smooth trajectories for industrial robots in complex environments using a direct method. The algorithm simultaneously creates a collision-free trajectory between initial and final configurations as the robot moves. The presented method deals with the uncertainties associated with the unknown kinematic properties of intermediate via points since they are generated as the algorithm evolves looking for the solution. Additionally, the objective of this algorithm is to minimize the trajectory time, which guides the robot motion. The method has been applied successfully to the PUMA 560 robotic system. Four operational parameters (execution time, computational time, end-effector distance traveled, and significant points distance traveled) have been computed to study and analyze the algorithm efficiency. The experimental results show that the proposed optimization algorithm for the trajectory planning problem of an industrial robot is feasible.

The paper discusses the shape control problem related to a class of hyper-redundant robot arms with continuum elements, i.e. tentacle arms. A spatial weighted technique for sensor measurements is used in order to facilitate the parameter estimation. The paper focuses on the shape control by using the curvature gradient a constant parameter along the segment arm. The conditions that ensure a constant curvature gradient for a class of tentacle arms characterized by elastic backbone are determined. A sensor network distributed along the robot arm is used for the shape control. The main parameters of the arm shape, curvature and curvature gradient or “shape” Jacobian for the control problem are estimated. Two measuring systems are used: a) a distributed angle sensor network and b) a curvature sensor placed at the end of the arm segment. The stability analysis and the resulting controllers are obtained using the concept of boundary geometric control and the weighted state control methods. The shape control algorithms for dynamic models with uncertain components are proposed. Numerical simulations and experimental results illustrate the effectiveness of the above mentioned algorithms.