Short-range guidance of autonomous vehicles—a comparative study

In this paper, two generic line-of-sight (LOS) sensing-based short-range guidance methodologies are presented for the docking of autonomous vehicles. The first method utilizes a passive LOS sensing scheme to provide vehicle corrective motions, while the latter method utilizes active sensing. The novelty of the proposed guidance methodologies is their applicability to situations that do not allow for direct proximity measurements of the vehicle. In such instances, one must employ a guidance-based technique to move the vehicle to its desired pose using corrective actions at the final stages of its motion. The objective of both proposed guidance methods is, thus, to successfully minimize the systematic errors of the vehicle, while allowing it to converge to its desired pose within random noise limits. Both techniques were successfully tested via simulations and are discussed herein in terms of convergence rate and accuracy, in addition to the types of localization problems that each method should be used in.     

Modelless Guidance for the Docking of Autonomous Vehicles

A novel line-of-sight sensing-based modelless guidance strategy is presented for the autonomous docking of robotic vehicles. The novelty of the proposed guidance strategy is twofold: 1) applicability to situations that do not allow for direct proximity measurement of the vehicle and 2) ability to generate short-range docking motion commands without a need for a global sensing-system (calibration) model. Two guidance -based motion-planning methods were developed to provide the vehicle controller with online corrective motion commands: a passive-sensing-based and an active-sensing-based scheme, respectively. The objective of both proposed guidance methods is to minimize the accumulated systematic errors of the vehicle as a result of the long-range travel, while allowing it to converge to its desired pose within random-noise limits. Both techniques were successfully tested via simulations and experiments, and are discussed herein, in terms of convergence rate and accuracy, in addition to the types of localization problems for which each method could be specifically more suitable.     

Mapping the underside of an iceberg with a modified underwater glider

Icebergs pose many challenges to offshore operations in the Arctic Ocean and sub‐arctic regions. They could damage underwater infrastructure such as pipelines, and disrupt marine transportation. The below‐water shape of an iceberg is a key factor for iceberg management in the North Atlantic Ocean because it affects the iceberg towing plans and iceberg drift patterns. In recent years, unmanned platforms have been proposed as potential candidates for underwater iceberg mapping. Compared to a conventional ship‐based iceberg survey, using unmanned platforms is more efficient and safer. In this paper, we present research using a hybrid underwater glider to measure the underwater shape of an iceberg. The vehicle is equipped with a mechanical scanning sonar for range sensing and iceberg mapping, and a guidance system is designed to use the sonar measurements for guiding the vehicle to circumnavigate an iceberg at the desired standoff distance. Several field experiments have been conducted on an iceberg to evaluate the system performance. With repeated observations, the underside of the target iceberg was successfully reconstructed, and iceberg shape comparisons are presented.     

RBFNN‐Based Identification and Compensation Mechanism for Disturbance‐Like Parametric Friction with Application to Tractor‐Trailer Vehicles

This paper develops an effective identification and compensation mechanism for the disturbance‐like parametric friction of a typical underactuated tractor‐trailer vehicle system. To begin with, a parametric friction model is proposed to describe various friction effects associated with the system velocity, and then a disturbance‐like parametric friction concept is introduced by considering the motion characteristics of tractor‐trailer vehicle. Next, the radial basis function neural network (RBFNN) is employed to identify the friction due to its high convergence rate, superior approximation precision and local‐minima avoidance ability. Afterwards, a sliding mode control (SMC) is utilized to compensate the identified friction due to its numerous merits, such as strong robustness and fast convergence. On the basis of the effective combination of identification and compensation mechanism, a favorable transient performance can be achieved during the desired velocity tracking process. Lastly, the simulation results confirm that the RBFNN‐based disturbance‐like parametric friction identification and compensation mechanism can effectively improve the trajectory tracking performance of tractor‐trailer vehicle.     

Three‐Dimensional Impact Angle Constrained Guidance Laws with Fixed‐Time Convergence

In this paper, three dimensional coupled engagement dynamics are firstly transformed into a state‐space form without decoupling three dimensional engagement dynamics into two mutually orthogonal planes to avoid degrading the performance of the guidance law. Specially, fixed‐time guidance laws are proposed to guarantee that the line‐of‐sight (LOS) angular rates can be steered to zero before the final time of the guidance process at the same time. The exact convergence time can be set beforehand with respect to the LOS rates, and it is independent of initial conditions with respect to the guidance system. Moreover, impact angle constraint is taken into account, and these guidance laws are robust against maneuvering targets by sliding mode techniques. Simulation results validate the effectiveness of the proposed algorithms.     

Multi‐objective learning control for robotic manipulator

Several types of learning controllers have been proposed in the literature to improve the tracking performance of robot manipulators. In most cases, the learning algorithms emphasize mainly on a single objective of learning a desired motion of the end‐effector. In some applications, more than one objective may be specified at the same time. For example, a robot may be required to follow a desired trajectory (primary objective) and at the same time avoid an obstacle (secondary objective). Thus, multi‐objective learning control can be more effective to realize the collision‐free tasks. In this paper, a multi‐objective learning control problem is formulated and solved. In the proposed learning control system, the primary objective is to track a desired end‐effector's motion and several secondary objectives can be specified for the desired orientation and for obstacles avoidance. To avoid obstacles in the workspace, a new learning concept called “region learning control” is also proposed in this paper. The proposed learning controllers do not require the exact knowledge of robot kinematics and dynamics. Sufficient condition is presented to guarantee the convergence of the learning system. The proposed learning controllers are applied to a four‐link planar redundant manipulator and simulation results are presented to illustrate the performance. © 2004 Wiley Periodicals, Inc.     

Data‐Driven Reconstruction of Human Locomotion Using a Single Smartphone

Generating a visually appealing human motion sequence using low‐dimensional control signals is a major line of study in the motion research area in computer graphics. We propose a novel approach that allows us to reconstruct full body human locomotion using a single inertial sensing device, a smartphone. Smartphones are among the most widely used devices and incorporate inertial sensors such as an accelerometer and a gyroscope. To find a mapping between a full body pose and smartphone sensor data, we perform low dimensional embedding of full body motion capture data, based on a Gaussian Process Latent Variable Model. Our system ensures temporal coherence between the reconstructed poses by using a state decomposition model for automatic phase segmentation. Finally, application of the proposed nonlinear regression algorithm finds a proper mapping between the latent space and the sensor data. Our framework effectively reconstructs plausible 3D locomotion sequences. We compare the generated animation to ground truth data obtained using a commercial motion capture system.     

Vision‐based Localization and Robot‐centric Mapping in Riverine Environments

This paper presents a vision‐based localization and mapping algorithm developed for an unmanned aerial vehicle (UAV) that can operate in a riverine environment. Our algorithm estimates the three‐dimensional positions of point features along a river and the pose of the UAV. By detecting features surrounding a river and the corresponding reflections on the water's surface, we can exploit multiple‐view geometry to enhance the observability of the estimation system. We use a robot‐centric mapping framework to further improve the observability of the estimation system while reducing the computational burden. We analyze the performance of the proposed algorithm with numerical simulations and demonstrate its effectiveness through experiments with data from Crystal Lake Park in Urbana, Illinois. We also draw a comparison to existing approaches. Our experimental platform is equipped with a lightweight monocular camera, an inertial measurement unit, a magnetometer, an altimeter, and an onboard computer. To our knowledge, this is the first result that exploits the reflections of features in a riverine environment for localization and mapping.     

Laser interferometry-based guidance methodology for high precision positioning of mechanisms and robots

Laser interferometry-based sensing and measurement (LISM) technique was originally investigated to perform dynamic measurements of the end effector of a robot manipulator in motion. This technique can provide dynamic position measurements in real time and has high accuracy, large working space, high sampling rate and automatic target tracking. In this paper, a methodology using LISM technique is proposed to perform laser interferometry-based guidance (LIG) for accurate positioning of a robot manipulator in high precision manufacturing operations. The methodology utilizes the LISM apparatus to guide the robot's end effector to a desired location or along a desired path by directing the robot to follow the trajectory mapped by the laser beam. This is accomplished through the establishment of techniques for path generation, sensing and data acquisition and guidance error determination and compensation in the control algorithm. The algorithms for this methodology, together with the measurement and analysis techniques are described. A number of experiments are carried out to examine and validate the proposed LIG technique. Experimental results show that the established technique can effectively improve the positioning accuracy of the robot manipulator.     

Correlation‐based visual odometry for ground vehicles

Reliable motion estimation is a key component for autonomous vehicles. We present a visual odometry method for ground vehicles using template matching. The method uses a downward‐facing camera perpendicular to the ground and estimates the motion of the vehicle by analyzing the image shift from frame to frame. Specifically, an image region (template) is selected, and using correlation we find the corresponding image region in the next frame. We introduce the use of multitemplate correlation matching and suggest template quality measures for estimating the suitability of a template for the purpose of correlation. Several aspects of the template choice are also presented. Through an extensive analysis, we derive the expected theoretical error rate of our system and show its dependence on the template window size and image noise. We also show how a linear forward prediction filter can be used to limit the search area to significantly increase the computation performance. Using a single camera and assuming an Ackerman‐steering model, the method has been implemented successfully on a large industrial forklift and a 4×4 vehicle. Over 6 km of field trials from our industrial test site, an off‐road area and an urban environment are presented illustrating the applicability of the method as an independent sensor for large vehicle motion estimation at practical velocities. © 2011 Wiley Periodicals, Inc.     

Pose Optimization of Serial Manipulators Using Knowledge of Their Velocity‐Degenerate (Singular) Configurations

This work investigates the exploitation of velocity‐degenerate configurations to optimize the pose of either nonredundant or redundant serial manipulators to sustain desired wrenches. An algorithm is developed that determines a desirable start point for the optimization of a serial manipulator's pose. The start‐point algorithm (SPA) uses analytical expressions of the velocity‐degenerate (singular) configurations of a serial manipulator to determine a pose that would be best suitable to sustain a desired wrench. Results for an example redundant serial manipulator are presented. The example results show that by using the SPA with the optimization routine, the resulting poses obtained require less effort from the actuators when compared to the poses obtained without using the SPA. It is shown that when no constraint is imposed on the position of the end‐effector, the SPA excels at providing a better solution with less iterations than running the optimization without the SPA. © 2003 Wiley Periodicals, Inc.     

Adaptive Terminal ILC for Iteration‐varying Target Points

Terminal iterative learning control (TILC) has been developed to reduce the error between system output and a fixed desired point at the terminal end of operation interval over iterations. In this work, the desired terminal point is not fixed but allowed to change run‐to‐run among a set of fixed points and a new adaptive terminal iterative learning control scheme is developed to achieve learning objective over iterations. The control signal is updated from the measured terminal value at the end of a run, instead of the whole output trajectory. Although the reference terminal point is iteration‐varying, the new adaptive TILC guarantees that the tracking error converges to zero iteratively. Both rigorous mathematical analysis and simulation results confirm the applicability and effectiveness of the proposed approach.     

Integrated Guidance and Feedback Control of Underactuated Robotics System in SE(3)

An integrated guidance and feedback control scheme for steering an underactuated vehicle through desired waypoints in three-dimensional space, is developed here. The underactuated vehicle is modeled as a rigid body with four control inputs. These control inputs actuate the three degrees of freedom of rotational motion and one degree of freedom of translational motion in a vehicle body-fixed coordinate frame. This actuation model is appropriate for a wide range of underactuated vehicles including spacecraft with internal attitude actuators, vertical take-off and landing (VTOL) aircraft, fixed-wing multirotor unmanned aerial vehicles (UAVs), maneuverable robotic vehicles, etc. The guidance problem is developed on the special Euclidean group of rigid body motions, SE(3), in the framework ofgeometric mechanics, which represents the vehicle dynamics globally on this configuration manifold. The integrated guidance and control algorithm selects the desired trajectory for the translational motion that passes through the given waypoints, and the desired trajectory for the attitude based on the desired thrust direction to achieve the translational motion trajectory. A feedback control law is then obtained to steer the underactuated vehicle towards the desired trajectories in translation and rotation. This integrated guidance and control scheme takes into account known bounds on control inputs and generates a trajectory that is continuous and at least twice differentiable, which can be implemented with continuous and bounded control inputs. The integrated guidance and feedback control scheme is applied to an underactuated quadcopter UAV to autonomously generate a trajectory through a series of given waypoints in SE(3) and track the desired trajectory in finite time. The overall stability analysis of the feedback system is addressed. Discrete time models for the dynamics and control schemes of the UAV are obtained in the form of Lie group variational integrators using the discrete Lagrange-d’Alembert principle. Almost global asymptotic stability of the feedback system over its state space is shown analytically and verified through numerical simulations.     

Example‐Based Retargeting of Human Motion to Arbitrary Mesh Models

We present a novel method for retargeting human motion to arbitrary 3D mesh models with as little user interaction as possible. Traditional motion‐retargeting systems try to preserve the original motion, while satisfying several motion constraints. Our method uses a few pose‐to‐pose examples provided by the user to extract the desired semantics behind the retargeting process while not limiting the transfer to being only literal. Thus, mesh models with different structures and/or motion semantics from humanoid skeletons become possible targets. Also considering the fact that most publicly available mesh models lack additional structure (e.g. skeleton), our method dispenses with the need for such a structure by means of a built‐in surface‐based deformation system. As deformation for animation purposes may require non‐rigid behaviour, we augment existing rigid deformation approaches to provide volume‐preserving and squash‐and‐stretch deformations. We demonstrate our approach on well‐known mesh models along with several publicly available motion‐capture sequences.     

Adaptive‐gain multivariable super‐twisting sliding mode control for reentry RLV with torque perturbation

Reusable launch vehicle (RLV) should be under control in the presence of model uncertainty and external disturbance, which is considered as torque perturbation in this paper during the reentry phase. Such a challenge imposes tight requirements to the enhanced robustness and accuracy of the vehicle autopilot. The key of this paper is to propose an adaptive‐gain multivariable super‐twisting sliding mode controller when considering that the bounds of uncertainty and perturbation are not known. The important features of the controller are that the adaptation algorithm can achieve non‐overestimating values of the control gains and the multivariable super‐twisting sliding mode approach can obtain a more elegant solution in finite time. According to the multiple‐time scale features, the dynamics of RLV attitude motion are divided into outer‐loop subsystem and inner‐loop subsystem. On this basis, the controllers are designed respectively to ensure the finite‐time reentry attitude tracking. In addition, a proof of the finite‐time convergence for the overall system is derived using the Lyapunov function technique and multiple‐time scale characteristic. Finally, simulation results of six degree‐of‐freedom RLV are provided to verify the effectiveness and robustness of the proposed controller in tracking the guidance commands as well as achieving a safe and stable reentry flight. Copyright © 2016 John Wiley & Sons, Ltd.     

Air‐ground Matching: Appearance‐based GPS‐denied Urban Localization of Micro Aerial Vehicles

In this paper, we address the problem of globally localizing and tracking the pose of a camera‐equipped micro aerial vehicle (MAV) flying in urban streets at low altitudes without GPS. An image‐based global positioning system is introduced to localize the MAV with respect to the surrounding buildings. We propose a novel air‐ground image‐matching algorithm to search the airborne image of the MAV within a ground‐level, geotagged image database. Based on the detected matching image features, we infer the global position of the MAV by back‐projecting the corresponding image points onto a cadastral three‐dimensional city model. Furthermore, we describe an algorithm to track the position of the flying vehicle over several frames and to correct the accumulated drift of the visual odometry whenever a good match is detected between the airborne and the ground‐level images. The proposed approach is tested on a 2 km trajectory with a small quadrocopter flying in the streets of Zurich. Our vision‐based global localization can robustly handle extreme changes in viewpoint, illumination, perceptual aliasing, and over‐season variations, thus outperforming conventional visual place‐recognition approaches. The dataset is made publicly available to the research community. To the best of our knowledge, this is the first work that studies and demonstrates global localization and position tracking of a drone in urban streets with a single onboard camera.     

Tactics‐Based Behavioural Planning for Goal‐Driven Rigid Body Control

Controlling rigid body dynamic simulations can pose a difficult challenge when constraints exist on the bodies' goal states and the sequence of intermediate states in the resulting animation. Manually adjusting individual rigid body control actions (forces and torques) can become a very labour‐intensive and non‐trivial task, especially if the domain includes a large number of bodies or if it requires complicated chains of inter‐body collisions to achieve the desired goal state. Furthermore, there are some interactive applications that rely on rigid body models where no control guidance by a human animator can be offered at runtime, such as video games. In this work, we present techniques to automatically generate intelligent control actions for rigid body simulations. We introduce sampling‐based motion planning methods that allow us to model goal‐driven behaviour through the use of non‐deterministic Tactics that consist of intelligent, sampling‐based control‐blocks, called Skills. We introduce and compare two variations of a Tactics‐driven planning algorithm, namely behavioural Kinodynamic Rapidly Exploring Random Trees (BK‐RRT) and Behavioural Kinodynamic Balanced Growth Trees (BK‐BGT). We show how our planner can be applied to automatically compute the control sequences for challenging physics‐based domains and that is scalable to solve control problems involving several hundred interacting bodies, each carrying unique goal constraints.     

A time differences of arrival‐based homing strategy for autonomous underwater vehicles

A new sensor‐based homing integrated guidance and control law is presented to drive an underactuated autonomous underwater vehicle (AUV) toward a fixed target, in 3‐D, using the information provided by an ultra‐short baseline (USBL) positioning system. The guidance and control law is first derived at a kinematic level, expressed on the space of the time differences of arrival (TDOAs), as directly measured by the USBL sensor, and assuming the plane wave approximation. Afterwards, the control law is extended for the dynamics of an underactuated AUV resorting to backstepping techniques. The proposed Lyapunov‐based control law yields almost global asymptotic stability (AGAS) in the absence of external disturbances and is further extended, keeping the same properties, to the case where known ocean currents affect the motion of the vehicle. Simulations are presented and discussed that illustrate the performance and behavior of the overall closed‐loop system in the presence of realistic sensor measurements and actuator saturation. Copyright © 2009 John Wiley & Sons, Ltd.     

Three-dimensional optimal path planning for waypoint guidance of an autonomous underwater vehicle

In this paper, optimal three-dimensional paths are generated offline for waypoint guidance of a miniature Autonomous Underwater Vehicle (AUV). Having the starting point, the destination point, and the position and dimension of the obstacles, the AUV is intended to systematically plan an optimal path toward the target. The path is defined as a set of waypoints to be passed by the vehicle. Four criteria are considered for evaluation of an optimal path; they are “total length of path”, “margin of safety”, “smoothness of the planar motion” and “gradient of diving”. A set of Pareto-optimal solutions is found where each solution represents an optimal feasible path that cannot be outrun by any other path considering all four criteria. Then, a proposed three-dimensional guidance system is used for guidance of the AUV through selected optimal paths. This system is inspired from the Line-of-Sight (LOS) guidance strategy; the idea is to select the desired depth, presumed proportional to the horizontal distance of the AUV and the target. To develop this guidance strategy, the dynamic modeling of this novel miniature AUV is also derived. The simulation results show that this guidance system efficiently guides the AUV through the optimal paths.     

On the positioning of revolute‐joint robot manipulators

A new technique is introduced for obtaining the sets of joint‐motor‐displacements which correspond to a desired spatial pose at the end‐effector of a revolute‐joint manipulator. The properties of rotation are exploited and a new local coordinate notation is introduced to obtain the required solution. The proposed technique leads to two simplified sets of linear equations which correspond to the kinematic behavior of both the arm and the wrist. These two sets of equations have been manipulated further to obtain polynomial solutions for both manipulator structures. © 2000 John Wiley & Sons, Inc.