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.

In this paper we propose new tuning conditions for three saturated nonlinear proportional-integral-derivative (PID) global regulators with bounded torques for robot manipulators, which have been presented previously in the literature. The motivation of this work relies on the fact that the tuning conditions presented previously in the literature for assuring global asymptotic stability are so restrictive that it had been impossible, until now, to carry out experimental tests. New tuning criteria of unsaturated PID controllers for robot manipulators with stability conditions more relaxed than those presented previously in the literature have been proposed recently in some works by the authors. This was achieved by setting the stability conditions as expressions that have to be satisfied at each joint instead of general conditions for the whole robot. Based on this idea, we now obtain stability conditions for saturated global PID controllers which are so relaxed that they have allowed to perform, by the first time, experimental tests using controller gains which completely satisfy the proposed stability conditions. The results of such experiments are presented in this paper, where we have used a two-degrees-of-freedom robot manipulator.

Accurate contour tracking is one of the main tasks in modern manufacturing processes. By considering coupling effects among multiple axes, this paper proposes a cross-coupled proportional-integral-derivative (PID) control developed in position domain, and the controller is applied to a multi-axis computer numerical control (CNC) machine for contour tracking performance improvement. Stability analysis is conducted for the developed position domain cross-coupled PID control using the Lyapunov method, and guidelines for the selection of control gains are provided. The contour tracking performance are improved compared to an equivalent time domain controller, since the reference axis in position domain control does not contribute any error to the overall contouring error of the system. Simulation results demonstrate the effectiveness of cross-coupled PID position domain control for both linear and circular contour tracking, and prove the robustness of the controller to deal with random disturbances. It also shows that position domain cross-coupled PID control provides better contour tracking performance over position domain PID control and the equivalent time domain PID control.

Software engineering plays a key role in state-of-the-art robotics where more effective and efficient software development solutions are needed for implementation and integration of advanced robotics capabilities. Component-based software engineering and model-driven software development are two paradigms suitable to deal with such challenges. This paper presents the analysis, design, and implementation of control software for an Autonomous Underwater Vehicle (AUV). The software development stages are carried out by using a model-driven toolchain that provides support to design and build component-based software for robotics applications. A case study of a high-performance AUV control application and experimental results from a software schedulability analysis are presented.

An extension of the well-known Particle Swarm Optimization (PSO) to multi-robot applications has been recently proposed and denoted as Robotic Darwinian PSO (RDPSO), benefited from the dynamical partitioning of the whole population of robots. Although such strategy allows decreasing the amount of required information exchange among robots, a further analysis on the communication complexity of the RDPSO needs to be carried out so as to evaluate the scalability of the algorithm. Moreover, a further study on the most adequate multi-hop routing protocol should be conducted. Therefore, this paper starts by analyzing the architecture and characteristics of the RDPSO communication system, thus describing the dynamics of the communication data packet structure shared between teammates. Such procedure will be the first step to achieving a more scalable implementation of RDPSO by optimizing the communication procedure between robots. Second, an ad hoc on-demand distance vector reactive routing protocol is extended based on the RDPSO concepts, so as to reduce the communication overhead within swarms of robots. Experimental results with teams of 15 real robots and 60 simulated robots show that the proposed methodology significantly reduces the communication overhead, thus improving the scalability and applicability of the RDPSO algorithm.

We present an algorithm for offsetting the workspace obstacles of a circular robot. Our method has two major steps: It finds the raw offset curve for both lines and circular arcs, and then removes the global invalid loops to find the final offset. To generate the raw offset curve and remove global invalid loops, O(n) and O((n+k)log m) computational times are needed respectively, where n is the number of vertices in the original polygon, k is the number of self-intersections and m is the number of segments in the raw offset curve, where m ≤ n. Any local invalid loops are removed before generating the raw offset curve by invoking a pair-wise intersection detection test (PIDT). In the PIDT, two intersecting entities are checked immediately after they are computed, and if the test is positive, portions of the intersecting segments are removed. Our method works for conventional polygons as well as the polygons that contain circular arcs. Our algorithm is simple and very fast, as each sub-process of the algorithm can be completed in linear time except the last one, which is nearly linear. Therefore, the overall complexity of the algorithm is nearly linear. By applying our simple and efficient approach, offsetting obstacles of any shape make it possible to construct a configuration space that ensures optimized motion planning.

We consider a team of autonomous kinematically controlled non-holonomic planar Dubins car-like vehicles. The team objective is to encircle a given target so that all vehicles achieve a common and pre-specified distance from it and are uniformly distributed over the respective circle, and the entire formation rotates around the target with a prescribed angular velocity. The robots do not communicate with each other and any central decision-maker. The sensing capacity of any vehicle is heavily restricted: It has access only to the distance to the target and to the distances to the companion vehicles that are in a given disc sector centered at the vehicle at hand; no robot can distinguish between its companions, and does not know their parameters. A distributed control law is proposed, and mathematically rigorous proofs of its non-local convergence as well as collision avoidance property are presented. The performance of the control law is illustrated by computer simulations and experiments with real robots.

In this paper, a new design of neural networks is introduced, which is able to generate oscillatory patterns in its output. The oscillatory neural network is used in a biped robot to enable it to learn to walk. The fundamental building block of the neural network proposed in this paper is O-neurons, which can generate oscillations in its transfer functions. O-neurons are connected and coupled with each other in order to shape a network, and their unknown parameters are found by a particle swarm optimization method. The main contribution of this paper is the learning algorithm that can combine natural policy gradient with particle swarm optimization methods. The oscillatory neural network has six outputs that determine set points for proportional-integral-derivative controllers in 6-DOF humanoid robots. Our experiment on the simulated humanoid robot presents smooth and flexible walking.

This paper addresses attention to a design for a low-cost exoskeleton with fairly simple construction, lightweight, easy to wear and to adapt to human legs. The design core is focused on a cam-mechanism implementation at the ankle joint level of the leg exoskeleton. The engineering feasibility of the proposed design is characterized by numerical simulations for the design process.

This paper describes and evaluates the use of a head-mounted display (HMD) for the teleoperation of a field robot. The HMD presents a pair of video streams to the operator (one to each eye) originating from a pair of stereo cameras located on the front of the robot, thus providing him/her with a sense of depth (stereopsis). A tracker on the HMD captures 3-DOF head orientation data which is then used for adjusting the camera orientation by moving the robot and/or the camera position accordingly, and rotating the displayed images to compensate for the operator's head rotation. This approach was implemented in a search and rescue robot (RAPOSA), and it was empirically validated in a series of short user studies. This evaluation involved four experiments covering two-dimensional perception, depth perception, scene perception, and performing a search and rescue task in a controlled scenario. The stereoscopic display and head tracking are shown to afford a number of performance benefits. However, one experiment also revealed that controlling robot orientation with yaw input from the head tracker negatively influenced task completion time. A possible explanation is a mismatch between the abilities of the robot and the human operator. This aside, the studies indicated that the use of an HMD to create a stereoscopic visualization of the camera feeds from a mobile robot enhanced the perception of cues in a static three-dimensional environment and also that such benefits transferred to simulated field scenarios in the form of enhanced task completion times.

Three-dimensional (3D) data processing has applications in solving complex tasks such as object recognition, environment modeling, and robotic mapping and localization. Because using raw 3D data without preprocessing is very time-consuming, extraction of geometric features that describe the environment concisely is essential. In this sense, a plane can be a suitable geometric feature due to its simplicity of extraction and the abundance in indoor environments. This paper presents an online incremental plane extraction method using line segments for indoor environments. Our data collection system is based on a “nodding” laser scanner, so we exploit the incremental nature of its data acquisition in which physical rotation and 3D data processing are conducted in parallel. Line segments defined by two end points become supporting elements that comprise a plane, so a large proportion of scan points can be ignored once the line segments are extracted from each scan slice. This elimination of points reduces the algorithm complexity and computation time. Experiments with the tens of complete scan data sets which were acquired from a typical indoor environment demonstrated that our method was at least three times faster than the state-of-the-art methods.

In this research work, the maximum Cartesian workspace of a Tricept parallel robot with two rotational and one translational degrees of freedom was investigated. Generally, the Cartesian workspace identifies the maximum size of a work-piece, specifying its cubic x, y and z dimensions, on which the milling machine could perform operations. However, the workspace of a robot can be considered in its task space, such as ψ × θ × z for the Tricept Parallel Kinematic Mechanism (PKM). A novel homogeneous Jacobian matrix which transforms joint space velocity vector into end-effector Cartesian velocity vector has been generated named as a Cartesian Jacobian matrix. Using the indices derived from the homogeneous Cartesian Jacobian matrix, i.e. the maximum singular values and local conditioning indices, the manipulator is designed to reach the Cartesian workspace with rapid positioning rates as well as with singularity avoidance.

Mobility is a fundamental parameter of mechanisms expressing in a qualitative manner their kinematic and dynamic properties. The mobility formulae presented in literature take into consideration some of the structural entities, such as bodies, joints, constraints, closed loops, and space characteristics; however, the specific mechanical model that has traditionally been at the origin of the mobility criteria themselves is incompletely specified and, even then, only implicitly. In this paper, we propose a classification of the mobility criteria based on the known dynamic models. While all known mobility criteria have been associated with a specific dynamic model, some particular, less used dynamic models (like natural coordinates and multi-particle models) suggested new mobility criteria models. The associated mechanical model for each category of mobility criteria allows a qualitative assessment of the kinematic and dynamic sets of equations to be formulated in later stages of analysis. A simple multi-loop mechanism is taken as an example just to illustrate the mobility calculation and qualitative assessment of the kinematic and dynamic models in each case. Based on the proposed classification of the mobility formulae, an assessment is made with particular regard to their applicability to overconstrained mechanisms.

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.

Parallel-jaw gripper finds wide applications in various industrial sectors. In this paper, we mainly focus on the problem of form closure caging grasps of polygons with a parallel-jaw gripper equipped with four fingers. The form closure caging grasp is helpful for the fingers placements and contact region selections of a pneumatic gripper, as it is less sensitive to fingers misplacements.

We firstly prove that there is always a path from a cage to a form closure grasp of the object that never breaks the cage, as long as the attractive region constructed in the configuration space has a local minimum. If such a minimum cannot be found, we further adjust the fingers arrangements to produce the form closure grasp. Meanwhile, we also develop an algorithm to compute the initial cage of the form closure grasp. Simulations of the grasping process witness the effectiveness of the above analysis results.

In this paper a rolling robot resembling the shape of a triangular-bipyramid is proposed. The robot has three degrees of freedom and is formed by connecting two tripod mechanisms with three spherical joints. By kinematic analysis, the robot can be viewed as a planar four-bar linkage. Further, its dynamic rolling ability is discussed by Zero Moment Point (ZMP) analysis. We show that the robot has the capability to roll, adjust its step length, and switch rolling directions. These functions are verified by a series of simulations with a CAD (computer-aided design) model and experiments with a prototype.

This paper presents a new biped mechanism with low-cost easy-operation features. The mechanism is designed with functions for straight walking, changing direction, overcoming obstacle, and climbing stairs with only 7 DOFs (degrees of freedom). Dynamics of the biped mechanism are analyzed by means of simulations in the MSC.ADAMS environment. Simulation results in terms of motion torque, joint force, contact force, parts displacement, velocity, and acceleration are reported and analyzed to show the feasibility and efficiency of the proposed solution. In addition, with the simulation results, dynamical motion of the biped mechanism is investigated and its operation performances are characterized as well.

This paper is a continuation of a previous work of authors, Scaglia et al. [G. J. E. Scaglia, L. M. Quintero, V. Mut and F. Di Sciascio, “Numerical methods based controller design for mobile robots,” Robotica27(2), 269–279 (2009)]. A method is presented to choose the controller parameters such that, the values of the control actions do not exceed the maximum allowable and the tracking errors tend to zero. In addition, the analysis of the controller design parameters is included. The experimental results (laboratory experiments and a real world application) demonstrate the efficiency of the controller.

This paper presents optimization-based dynamic three-dimensional (3D) human running prediction. A predictive dynamics method is used to formulate the running problem, and normal running is formulated as a symmetric and cyclic motion. In addition, a slow jog along curved paths has been formulated. It is a non-symmetric running motion, so a stride formulation has been used. The dynamic effort and impulse are used as the performance measure, and the upper body yawing moment is also included in the performance measure. The joint angle profiles and joint torque profiles are calculated for the full-body human model, and the ground reaction force is determined. The effects of foot location and orientation on the running motion prediction are simulated and studied. Simulation results from this methodology show good correlation with experimental data obtained from human subjects.

Parental scaffolding is an important mechanism that speeds up infant sensorimotor development. Infants pay stronger attention to the features of the objects highlighted by parents, and their manipulation skills develop earlier than they would in isolation due to caregivers' support. Parents are known to make modifications in infant-directed actions, which are often called “motionese”7. The features that might be associated with motionese are amplification, repetition and simplification in caregivers' movements, which are often accompanied by increased social signalling. In this paper, we extend our previously developed affordances learning framework to enable our hand-arm robot equipped with a range camera to benefit from parental scaffolding and motionese. We first present our results on how parental scaffolding can be used to guide the robot learning and to modify its crude action execution to speed up the learning of complex skills. For this purpose, an interactive human caregiver-infant scenario was realized with our robotic setup. This setup allowed the caregiver's modification of the ongoing reach and grasp movement of the robot via physical interaction. This enabled the caregiver to make the robot grasp the target object, which in turn could be used by the robot to learn the grasping skill. In addition to this, we also show how parental scaffolding can be used in speeding up imitation learning. We present the details of our work that takes the robot beyond simple goal-level imitation, making it a better imitator with the help of motionese.