Showing papers in "Journal of Intelligent and Robotic Systems in 2017"

Survey of Model-Based Reinforcement Learning: Applications on Robotics

Abstract: Reinforcement learning is an appealing approach for allowing robots to learn new tasks. Relevant literature reveals a plethora of methods, but at the same time makes clear the lack of implementations for dealing with real life challenges. Current expectations raise the demand for adaptable robots. We argue that, by employing model-based reinforcement learning, the--now limited--adaptability characteristics of robotic systems can be expanded. Also, model-based reinforcement learning exhibits advantages that makes it more applicable to real life use-cases compared to model-free methods. Thus, in this survey, model-based methods that have been applied in robotics are covered. We categorize them based on the derivation of an optimal policy, the definition of the returns function, the type of the transition model and the learned task. Finally, we discuss the applicability of model-based reinforcement learning approaches in new applications, taking into consideration the state of the art in both algorithms and hardware.

Modelling and Control Strategies in Path Tracking Control for Autonomous Ground Vehicles: A Review of State of the Art and Challenges

Abstract: Autonomous vehicle field of study has seen considerable researches within three decades. In the last decade particularly, interests in this field has undergone tremendous improvement. One of the main aspects in autonomous vehicle is the path tracking control, focusing on the vehicle control in lateral and longitudinal direction in order to follow a specified path or trajectory. In this paper, path tracking control is reviewed in terms of the basic vehicle model usually used; the control strategies usually employed in path tracking control, and the performance criteria used to evaluate the controller's performance. Vehicle model is categorised into several types depending on its linearity and the type of behaviour it simulates, while path tracking control is categorised depending on its approach. This paper provides critical review of each of these aspects in terms of its usage and disadvantages/advantages. Each aspect is summarised for better overall understanding. Based on the critical reviews, main challenges in the field of path tracking control is identified and future research direction is proposed. Several promising advancement is proposed with the main prospect is focused on adaptive geometric controller developed on a nonlinear vehicle model and tested with hardware-in-the-loop (HIL). It is hoped that this review can be treated as preliminary insight into the choice of controllers in path tracking control development for an autonomous ground vehicle.

Survey on Computer Vision for UAVs: Current Developments and Trends

Abstract: During last decade the scientific research on Unmanned Aerial Vehicless (UAVs) increased spectacularly and led to the design of multiple types of aerial platforms. The major challenge today is the development of autonomously operating aerial agents capable of completing missions independently of human interaction. To this extent, visual sensing techniques have been integrated in the control pipeline of the UAVs in order to enhance their navigation and guidance skills. The aim of this article is to present a comprehensive literature review on vision based applications for UAVs focusing mainly on current developments and trends. These applications are sorted in different categories according to the research topics among various research groups. More specifically vision based position-attitude control, pose estimation and mapping, obstacle detection as well as target tracking are the identified components towards autonomous agents. Aerial platforms could reach greater level of autonomy by integrating all these technologies onboard. Additionally, throughout this article the concept of fusion multiple sensors is highlighted, while an overview on the challenges addressed and future trends in autonomous agent development will be also provided.

Aerial Images-Based Forest Fire Detection for Firefighting Using Optical Remote Sensing Techniques and Unmanned Aerial Vehicles

Abstract: Due to their fast response capability, low cost and without danger to personnel safety since there is no human pilot on-board, unmanned aerial vehicles (UAVs) with vision-based systems have great potential for monitoring and detecting forest fires. This paper proposes a novel forest fire detection method using both color and motion features for processing images captured from the camera mounted on a UAV which is moving during the whole mission period. First, a color-based fire detection algorithm with light computational demand is designed to extract fire-colored pixels as fire candidate regions by making use of chromatic feature of fire and obtaining fire candidate regions for further analysis. As the pose variations and low-frequency vibrations of UAV cause all objects and background in the images are moving, it is challenging to identify fires defending on a single motion based method. Two types of optical flow algorithms, a classical optical flow algorithm and an optimal mass transport optical flow algorithm, are then combined to compute motion vectors of the fire candidate regions. Fires are thereby expected to be distinguished from other fire analogues based on their motion features. Several groups of experiments are conducted to validate that the proposed method can effectively extract and track fire pixels in aerial video sequences. The good performance is anticipated to significantly improve the accuracy of forest fire detection and reduce false alarm rates without increasing much computation efforts.

Vision Based Autonomous Landing of Multirotor UAV on Moving Platform

Abstract: This paper investigates solutions for the fundamental yet challenging problem of autonomous landing of multirotor Unammaned Aerial Vehicles UAVs. In addition to landing on static targets, tracking and landing on a moving platform is addressed, as a solution to facilitate the deployment of the UAV. The paper presents the design of a new landing pad and its relative pose estimation. The fusion of inertial measurement with the estimated pose is considered to ensure a high sampling rate, and to increase the manoeuvrability of the vehicle. Two filters are designed to conduct the fusion, an Extended Kalman Filter (EKF) and an Extended Hź (EHź). The extensive simulation and practical tests permitted identification of the challenges of the landing task. Adequate solutions to these challenges are proposed to lessen their impact on landing precision.

Drone-Aided Healthcare Services for Patients with Chronic Diseases in Rural Areas

Abstract: This paper addresses the drone-aided delivery and pickup planning of medication and test kits for patients with chronic diseases who are required to visit clinics for routine health examinations and/or refill medicine in rural areas. For routine healthcare services, the work proposes two models: the first model is to find the optimal number of drone center locations using the set covering approach, and the second model is the multi-depot vehicle routing problem with pickup and delivery requests minimizing the operating cost of drones in which drones deliver medicine to patients and pick up exam kits on the way back such as blood and urine samples. In order to improve computational performance of the proposed models, a preprocessing algorithm, a Partition method, and a Lagrangian Relaxation (LR) method are developed as solution approaches. A cost-benefit analysis method is developed as a tool to analyze the benefits of drone-aided healthcare service. The work is tested on a numerical example to show its applicability.

DARP: Divide Areas Algorithm for Optimal Multi-Robot Coverage Path Planning

Abstract: This paper deals with the path planning problem of a team of mobile robots, in order to cover an area of interest, with prior-defined obstacles. For the single robot case, also known as single robot coverage path planning (CPP), an źź(n) optimal methodology has already been proposed and evaluated in the literature, where n is the grid size. The majority of existing algorithms for the multi robot case (mCPP), utilize the aforementioned algorithm. Due to the complexity, however, of the mCPP, the best the existing mCPP algorithms can perform is at most 16 times the optimal solution, in terms of time needed for the robot team to accomplish the coverage task, while the time required for calculating the solution is polynomial. In the present paper, we propose a new algorithm which converges to the optimal solution, at least in cases where one exists. The proposed technique transforms the original integer programming problem (mCPP) into several single-robot problems (CPP), the solutions of which constitute the optimal mCPP solution, alleviating the original mCPP explosive combinatorial complexity. Although it is not possible to analytically derive bounds regarding the complexity of the proposed algorithm, extensive numerical analysis indicates that the complexity is bounded by polynomial curves for practical sized inputs. In the heart of the proposed approach lies the DARP algorithm, which divides the terrain into a number of equal areas each corresponding to a specific robot, so as to guarantee complete coverage, non-backtracking solution, minimum coverage path, while at the same time does not need any preparatory stage (video demonstration and standalone application are available on-line http://tinyurl.com/DARP-app).

Control System for Free-Floating Space Manipulator Based on Nonlinear Model Predictive Control (NMPC)

Abstract: Manipulator mounted on an unmanned satellite could be used for performing orbital capture maneuver in order to repair satellites or remove space debris from orbit. Use of manipulators for such purposes presents unique challenges, as high level of autonomy is required and the motion of the manipulator influences the position and orientation of the manipulator-equipped satellite. This paper presents a new control system that consists of two modules: trajectory planning module (based on trajectory optimization algorithm) and Model Predictive Controller. Both modules take into account the free-floating nature of the satellite-manipulator system. Proposed control system was tested in numerical simulations performed for a simplified planar case. In the first set of simulations Nonlinear Model Predictive Control (NMPC) was used to ensure realization of a square reference end-effector trajectory, while in the second set control system was used for optimizing and then ensuring realization of the trajectory that leads to grasping of the rotating target satellite. Simulations were performed with disturbances and with the assumed non-perfect knowledge of parameters of the satellite-manipulator system. Results obtained with NMPC are better than results obtained with the controller based on the Dynamic Jacobian inverse and with the Modified Simple Adaptive Control (MSAC).

A Review of Redundant Parallel Kinematic Mechanisms

Abstract: Parallel kinematic mechanisms (PKM) have received particular attention due to their higher stiffness, increased payload capacity, and agility, when compared to their serial counterparts. Despite these significant advantages, however, most PKM designs, typically, yield limited workspace, problematic singularities, and configuration-dependent stiffness. In response, mechanism redundancy has emerged as an effective tool to address these and other problems. In this paper, we present an in-depth discussion of past research on PKM redundancy. The methodical review of the pertinent literature, first, introduces the concept of redundancy based on the number of actuators and the number of degrees of freedom required to perform a task and, then, discusses the two main types of redundancy according to the mobility of the mechanism, i.e., kinematic and actuation redundancy. Subsequently, research on the design aspects of redundant PKMs, including the various criteria used for design optimization is detailed. Primary advantages of PKM redundancy include workspace enlargement, singularity elimination/ avoidance, and improved joint-torque distribution. In this paper, these advantages are discussed in contrast to the main challenges redundant mechanisms present, such as in motion planning and control, and calibration. Other issues of PKM redundancy, briefly, presented herein for completeness are fault-tolerance, reconfigurability, cable-driven and hyper-redundant PKMs.

Gaussian Process Model Predictive Control of an Unmanned Quadrotor

Abstract: The Model Predictive Control (MPC) trajectory tracking problem of an unmanned quadrotor with input and output constraints is addressed. In this article, the dynamic models of the quadrotor are obtained purely from operational data in the form of probabilistic Gaussian Process (GP) models. This is different from conventional models obtained through Newtonian analysis. A hierarchical control scheme is used to handle the trajectory tracking problem with the translational subsystem in the outer loop and the rotational subsystem in the inner loop. Constrained GP based MPC are formulated separately for both subsystems. The resulting MPC problems are typically nonlinear and non-convex. We derived a GP based local dynamical model that allows these optimization problems to be relaxed to convex ones which can be efficiently solved with a simple active-set algorithm. The performance of the proposed approach is compared with an existing unconstrained Nonlinear Model Predictive Control (NMPC) algorithm and an existing constrained nonlinear GP based MPC algorithm. In the first comparison, simulation results show that the two approaches exhibit similar trajectory tracking performance. However, our approach has the advantage of incorporating constraints on the control inputs. In the second comparison, simulation results demonstrate that our approach only requires 20% of the computational time for the existing nonlinear GP based MPC.

Pursuit-Evasion Games of High Speed Evader

Abstract: In this paper, we address pursuit-evasion games of high speed evader involving multiple pursuers and a single evader with holonomic constraints in an open domain. The existing work on this problem discussed the required formation and capture strategy for a group of pursuers. However, the formulation has mathematical errors and has raised concerns over the validity of the developed capture strategy. This paper uses the idea of Apollonius circle to develop an escape strategy for the high speed evader, resolving the shortfalls in the existing work. The strategy is built on a concept of perfectly encircled formation and the conditions required to construct the same are presented. The escape strategy contains two steps. Firstly, the evader employs a strategy that forces a gap in the formation against all the admissible strategies of a group of pursuers. In the second step, it uses this gap to escape. The strategy considers both direct and indirect gaps in the formations. The indirect gap is encountered when a group of three or four pursuers is employed to capture. The efficacy of the escape strategy is established using simulation results.

Balancing and Trajectory Tracking of Two-Wheeled Mobile Robot Using Backstepping Sliding Mode Control: Design and Experiments

Abstract: The key attributes of Two Wheeled Balancing Mobile Robots (TWBMRs) are nonholonomic constraints and inherent instability. This paper deals with the problem of balancing and trajectory tracking of TWBMR using backstepping Sliding Mode Controller (SMC). First, the mathematical representation of TWBMR is derived using Lagrangian method by incorporating the dynamics of DC motors. Then, a decoupling approach is applied for simplifying the dynamic equations. The backstepping SMC technique is finally adopted to achieve the balancing and trajectory tracking of the TWBMR, whereas both model uncertainties and exogenous disturbance are taken into account in the controller design methodology. In order to determine the velocity, the trajectory tracking is achieved by the kinematic control, which is a common backstepping controller. For the velocity convergence of TWBMR to the generated desired value, two SMCs are designed, in which the motors voltage are directly controlled as the control laws. Simplicity in practical implementation and control law, ability to overcome uncertainties and appropriate performance are the main advantages of the proposed controller. The effectiveness of the proposed controller is verified through simulation and experimental results.

Nonlinear Control with Swing Damping of a Multirotor UAV with Suspended Load

Abstract: In this paper, we consider the problem of trajectory tracking of a multirotor Unmanned Aerial Vehicle carrying a suspended payload. The movement of the suspended payload influences the dynamics of the multirotor, which must be appropriately handled by the controller to achieve satisfactory tracking results. We derive a mathematical model of the interconnected multi-body system using Kane’s equations, and develop a non-linear tracking controller based on the backstepping technique. In addition to suppressing the effects of the swinging payload, the controller also compensates for an unknown constant wind disturbance. The origin of the tracking error is proven UGAS (Uniformly Globally Asymptotically Stable) and ULES (Uniformly Locally Exponentially Stable) through Lyapunov analysis. To reduce the swing motion of the suspended load, a nominal swing-free path is generated through open loop shaping filters, then further perturbed through a delayed feedback approach from measured load deflection angles to achieve robustness. The proposed controller structure is verified by simulations and experiments.

CPG-Inspired Locomotion Control for a Snake Robot Basing on Nonlinear Oscillators

Abstract: The article focuses on locomotion control of a snake-like robot with cardan joints using a central pattern generator (CPG) approach. A double chain structure of a CPG model is developed based on nonlinear oscillators connected with diffusive couplings. The proposed CPG model has the ability to produce stable rhythmic patterns applied both in the serpentine locomotion and sidewinding locomotion of snake robots. The global exponential stability of the model is also presented using the partial contraction theory. An important point addressed in this paper is that the proposed CPG model has explicit control parameters including not only frequencies of oscillation and amplitudes of oscillation but also phase differences between the neighbor oscillators. The method to adjust the speed and direction of the snake robot during the locomotion is discussed by modulating the control parameters in the proposed CPG model directly. Simulation results together with the experiments on a real snake robot show that the proposed CPG approach can be used to control snake robots successfully.

A Velocity-Based Dynamic Model and Its Properties for Differential Drive Mobile Robots

Abstract: An important issue in the field of motion control of wheeled mobile robots is that the design of most controllers is based only on the robot's kinematics. However, when high-speed movements and/or heavy load transportation are required, it becomes essential to consider the robot dynamics as well. The control signals generated by most dynamic controllers reported in the literature are torques or voltages for the robot motors, while commercial robots usually accept velocity commands. In this context, we present a velocity-based dynamic model for differential drive mobile robots that also includes the dynamics of the robot actuators. Such model has linear and angular velocities as inputs and has been included in Peter Corke's Robotics Toolbox for MATLAB, therefore it can be easily integrated into simulation systems that have been built for the unicycle kinematics. We demonstrate that the proposed dynamic model has useful mathematical properties. We also present an application of such model on the design of an adaptive dynamic controller and the stability analysis of the complete system, while applying the proposed model properties. Finally, we show some simulation and experimental results and discuss the advantages and limitations of the proposed model.

Novel Haptic Device Using Jamming Principle for Providing Kinaesthetic Feedback in Glove-Based Control Interface

Abstract: This paper presents a new type of wearable haptic device which can augment a sensor glove in various tasks of telemanipulation. We present the details of its two alternative designs jamming tubes or jamming pads, and their control system. These devices use the jamming phenomena to change the stiffness of their elements and block the hand movement when a vacuum is applied. We present results of our experiments to measure static and dynamic changes in stiffness, which can be used to change the perception of grabbing hard or soft objects. The device, at its current state is capable of resisting forces of up to 7 N with 5 mm displacement and can simulate hardness up to the hardness of a rubber. However, time necessary for a complete change of stiffness is high (time constant 0.5 s); therefore, additional cutaneous interface was added in a form of small vibration motors. Finally, we show an application of the haptic interface in our teleoperation system to provide the operator with haptic feedback in a light weight and simple form.

A Multi-Layered Component-Based Approach for the Development of Aerial Robotic Systems: The Aerostack Framework

Abstract: To achieve fully autonomous operation for Unmanned Aerial Systems (UAS) it is necessary to integrate multiple and heterogeneous technical solutions (e.g., control-based methods, computer vision methods, automated planning, coordination algorithms, etc.). The combination of such methods in an operational system is a technical challenge that requires efficient architectural solutions. In a robotic engineering context, where productivity is important, it is also important to minimize the effort for the development of new systems. As a response to these needs, this paper presents Aerostack, an open-source software framework for the development of aerial robotic systems. This framework facilitates the creation of UAS by providing a set of reusable components specialized in functional tasks of aerial robotics (trajectory planning, self localization, etc.) together with an integration method in a multi-layered cognitive architecture based on five layers: reactive, executive, deliberative, reflective and social. Compared to other software frameworks for UAS, Aerostack can provide higher degrees of autonomy and it is more versatile to be applied to different types of hardware (aerial platforms and sensors) and different types of missions (e.g. multi robot swarm systems). Aerostack has been validated during four years (since February 2013) by its successful use on many research projects, international competitions and public exhibitions. As a representative example of system development, this paper also presents how Aerostack was used to develop a system for a (fictional) fully autonomous indoors search and rescue mission.

Analytical Inverse Kinematics Solver for Anthropomorphic 7-DOF Redundant Manipulators with Human-Like Configuration Constraints

Abstract: It is a common belief that service robots shall move in a human-like manner to enable natural and convenient interaction with a human user or collaborator. In particular, this applies to anthropomorphic 7-DOF redundant robot manipulators that have a shoulder-elbow-wrist configuration. On the kinematic level, human-like movement then can be realized by means of selecting a redundancy resolution for the inverse kinematics (IK), which realizes human-like movement through respective nullspace preferences. In this paper, key positions are introduced and defined as Cartesian positions of the manipulator's elbow and wrist joints. The key positions are used as constraints on the inverse kinematics in addition to orientation constraints at the end-effector, such that the inverse kinematics can be calculated through an efficient analytical scheme and realizes human-like configurations. To obtain suitable key positions, a correspondence method named wrist-elbow-in-line is derived to map key positions of human demonstrations to the real robot for obtaining a valid analytical inverse kinematics solution. A human demonstration tracking experiment is conducted to evaluate the end-effector accuracy and human-likeness of the generated motion for a 7-DOF Kuka-LWR arm. The results are compared to a similar correspondance method that emphasizes only the wrist postion and show that the subtle differences between the two different correspondence methods may lead to significant performance differences. Furthermore, the wrist-elbow-in-line method is validated as more stable in practical application and extended for obstacle avoidance.

Tightly Bounding the Shortest Dubins Paths Through a Sequence of Points

Abstract: This article addresses an important path planning problem for robots and Unmanned Aerial Vehicles (UAVs), which is to find the shortest path of bounded curvature passing through a given sequence of target points on a ground plane. Currently, no algorithm exists that can compute an optimal solution to this problem. Therefore, tight lower bounds are vital in determining the quality of any feasible solution to this problem. Novel tight lower bounding algorithms are presented in this article by relaxing some of the heading angle constraints at the target points. The proposed approach requires us to solve variants of an optimization problem called the Dubins interval problem between two points where the heading angles at the points are constrained to be within a specified interval. These variants are solved using tools from optimal control theory. Using these approaches, two lower bounding algorithms are presented and these bounds are then compared with existing results in the literature. Computational results are presented to corroborate the performance of the proposed algorithms; the average reduction in the difference between upper bounds and lower bounds is 80 % to 85 % with respect to the trivial Euclidean lower bounds.

Distributed Time-Varying Formation Tracking Analysis and Design for Second-Order Multi-Agent Systems

Abstract: Distributed time-varying formation tracking analysis and design problems for second-order multi-agent systems with one leader are studied respectively, where the states of followers form a predefined time-varying formation while tracking the state of the leader. Different from the previous results on formation tracking control, the formation for the followers can be described by specified time-varying vectors and the trajectory of the leader can also be time-varying. A distributed formation tracking protocol is constructed using only neighboring relative information. Necessary and sufficient conditions for second-order multi-agent systems with one leader to achieve time-varying formation tracking are proposed by utilizing the properties of the Laplacian matrix, where the formation tracking feasibility constraint is also given. An approach to design the formation tracking protocol is proposed by solving an algebraic Riccati equation. The presented results can be applied to deal with the target enclosing problems and consensus tracking problems for second-order multi-agent systems with one target/leader. An application in the target enclosing of multiple vehicles is provided to demonstrate the effectiveness of the theoretical results.

Design and Implementation of a Multi Sensor Based Brain Computer Interface for a Robotic Wheelchair

Abstract: In this study, design and implementation of a multi sensor based brain computer interface for disabled and/or elderly people is proposed. Developed system consists of a wheelchair, a high-power motor controller card, a Kinect camera, electromyogram (EMG) and electroencephalogram (EEG) sensors and a computer. The Kinect sensor is installed on the system to provide safe navigation for the system. Depth frames, captured by the Kinect’s infra-red (IR) camera, are processed with a custom image processing algorithm in order to detect obstacles around the wheelchair. A Consumer grade EMG device (Thalmic Labs) was used to obtain eight channels of EMG data. Four different hand movements: Fist, release, waving hand left and right are used for EMG based control of the robotic wheelchair. EMG data is first classified using artificial neural network (ANN), support vector machines and random forest schemes. The class is then decided by a rule-based scheme constructed on the individual outputs of the three classifiers. EEG based control is adopted as an alternative controller for the developed robotic wheelchair. A wireless 14-channels EEG sensor (Emotiv Epoch) is used to acquire real time EEG data. Three different cognitive tasks: Relaxing, math problem solving, text reading are defined for the EEG based control of the system. Subjects were asked to accomplish the relative cognitive task in order to control the wheelchair. During experiments, all subjects were able to control the robotic wheelchair by hand movements and track a pre-determined route with a reasonable accuracy. The results for the EEG based control of the robotic wheelchair are promising though vary depending on user experience.

Disturbance Observer Based Control with Anti-Windup Applied to a Small Fixed Wing UAV for Disturbance Rejection

Abstract: Small Unmanned Aerial Vehicles (UAVs) are attracting increasing interest due to their favourable features; small size, low weight and cost. These features also present different challenges in control design and aircraft operation. An accurate mathematical model is unlikely to be available meaning optimal control methods become difficult to apply. Furthermore, their reduced weight and inertia mean they are significantly more vulnerable to environmental disturbances such as wind gusts. Larger disturbances require more control actuation, meaning small UAVs are far more susceptible to actuator saturation. Failure to account for this can lead to controller windup and subsequent performance degradation. In this work, numerical simulations are conducted comparing a baseline Linear Quadratic Regulator (LQR) controller to integral augmentation and Disturbance Observer Based Control (DOBC). An anti-windup scheme is added to the DOBC to attenuate windup effects due to actuator saturation. A range of external disturbances are applied to demonstrate performance. The simulations conduct manoeuvres which would occur during landing, statistically the most dangerous flight phase, where fast disturbance rejection is critical. Validation simulations are then conducted using commercial X-Plane simulation software. This demonstrates that DOBC with anti-windup provides faster disturbance rejection of both modelling errors and external disturbances.

Algorithms for Heterogeneous, Multiple Depot, Multiple Unmanned Vehicle Path Planning Problems

Abstract: Unmanned vehicles, both aerial and ground, are being used in several monitoring applications to collect data from a set of targets. This article addresses a problem where a group of heterogeneous aerial or ground vehicles with different motion constraints located at distinct depots visit a set of targets. The vehicles also may be equipped with different sensors, and therefore, a target may not be visited by any vehicle. The objective is to find an optimal path for each vehicle starting and ending at its respective depot such that each target is visited at least once by some vehicle, the vehicle–target constraints are satisfied, and the sum of the length of the paths for all the vehicles is minimized. Two variants of this problem are formulated (one for ground vehicles and another for aerial vehicles) as mixed-integer linear programs and a branch-and-cut algorithm is developed to compute an optimal solution to each of the variants. Computational results show that optimal solutions for problems involving 100 targets and 5 vehicles can be obtained within 300 seconds on average, further corroborating the effectiveness of the proposed approach.

Nonlinear and Adaptive Suboptimal Control of Connected Vehicles: A Global Adaptive Dynamic Programming Approach

Abstract: This paper studies the cooperative adaptive cruise control (CACC) problem of connected vehicles with unknown nonlinear dynamics. Different from the present literature on CACC, data-driven feedforward and optimal feedback control policies are developed by global adaptive dynamic programming (GADP). Due to the presence of nonvanishing disturbance, a modified version of GADP is presented. Interestingly, the developed policy is guaranteed to globally stabilize the vehicular platoon system, and is robust to unmeasurable nonvanishing disturbance. Numerical simulation results are presented to validate the effectiveness of the developed approach.

CAD-based Pose Estimation Design for Random Bin Picking using a RGB-D Camera

Abstract: This paper presents a CAD-based six-degrees-of-freedom (6-DoF) pose estimation design for random bin picking for multiple objects. A virtual camera generates a point cloud database for the objects using their 3D CAD models. To reduce the computational time of 3D pose estimation, a voxel grid filter reduces the number of points for the 3D cloud of the objects. A voting scheme is used for object recognition and to estimate the 6-DoF pose for different objects. An outlier filter filters out badly matching poses so that the robot arm always picks up the upper object in the bin, which increases the success rate. In a computer simulation using a synthetic scene, the average recognition rate is 97.81 % for three different objects with various poses. A series of experiments have been conducted to validate the proposed method using a Kuka robot arm. The average recognition rate for three objects is 92.39 % and the picking success rate is 89.67 %.

Optimal Capture of a Tumbling Object in Orbit Using a Space Manipulator

Abstract: This paper introduces an optimal capture strategy for a manipulator based on a servicing spacecraft to approach an arbitrarily rotating object, such as a malfunctioning satellite or a piece of orbital debris, for capturing with minimal impact to the robot's base spacecraft. The method consists of two steps. The first step is to determine an optimal future time and the target object's corresponding motion state for the robot to capture the tumbling object, so that, at the time when the gripper of the robot intercepts the target the very first instant, the resulting impact or disturbance to the attitude of the base spacecraft will be minimal. The second step is to control the robot to reach the tumbling object at the predicted optimal time along an optimal trajectory. The optimal control problem is solved with random uncertainties in the initial and final boundary conditions. Uncertainties are introduced because sensor and estimation errors inevitably exist in the first step, i.e., determination process of the initial and final boundary conditions. The application of the method is demonstrated using a dynamics simulation example.

Market-Based Task Assignment for Cooperative Timing Missions in Dynamic Environments

Abstract: New market-based decentralized algorithms are proposed for the task assignment of multiple unmanned aerial vehicles in dynamic environments with a limited communication range. In particular, a cooperative timing mission that cannot be performed by a single vehicle is considered. The baseline algorithms for a connected network are extended to deal with time-varying network topology including isolated subnetworks due to a limited communication range. The mathematical convergence and scalability analyses show that the proposed algorithms have a polynomial time complexity, and numerical simulation results support the scalability of the proposed algorithm in terms of the runtime and communication burden. The performance of the proposed algorithms is demonstrated via Monte Carlo simulations for the scenario of the suppression of enemy air defenses.

Clothoids Composition Method for Smooth Path Generation of Car-Like Vehicle Navigation

Abstract: This paper addresses a continuous curvature path generation problem for car-like vehicle navigation. The continuous curvature path is generated by multiple clothoids composition and parametric adjustment. According to the geometric conditions of the given initial and final configurations, the path generation problem is classified into two cases and then, each problem is solved by by appropriate proposed algorithm. The solution is obtained by iterative procedure subject to geometric constraint as well as solution constraints. For computational efficiency and fast convergence in the proposed algorithms, a minimax sharpness constraint is proposed as the solution constraint by minimizing the maximum sharpness of the feasible solutions. After the generation of the proposed path, the resultant curvature/sharpness diagram provides a useful information about its orientation and curvature continuity along the travel length. The proposed path planning strategy, permits us to obtain online, smooth and safe path between two defined configurations while ensuring high passengers comfort (continuous curvature and transition between the different composed clothoids). The algorithmic proposals have been applied to generate continuous curvature for two cases. The first correspond to local path planning for ensuring obstacle avoidance or lane change. The second application corresponds to global path smoothing. The resultant global path path is tested on the Lyapunov-based control scheme and showed improved performance on its steering work (reduction of 41.0% than the driving based on the raw data), which permits us therefore to validate the effectiveness of the obtained global path for car-like vehicles path following.

Dual Quaternion Modeling and Control of a Quad-rotor Aerial Manipulator

Abstract: This contribution presents a modeling technique for an aerial manipulator based on a quad-rotor vehicle provided with a robotic arm. Dual quaternions, which are a little explored but powerful mathematical tool, are proposed as an alternative to the classical Euler angles approach for the kinematic and dynamic modeling. A feedback control law based on dual quaternions is used to validate the proposed model, taking into account the external effects of the robotic limb. Numerical simulations and experiments validate the proposal, opening a path for future research.

Toward Optimal Rendezvous of Multiple Underwater Gliders: 3D Path Planning with Combined Sawtooth and Spiral Motion

Abstract: In this paper, a path planning system is proposed for optimal rendezvous of multiple underwater gliders in three-dimensional (3D) space. Inspired by the Dubins Paths consisting of straight lines and circular arcs, this paper presents the first attempt to extend the 3D Dubins curve to accommodate the characteristic glider motions include upwards and downwards straight glides in a sawtooth pattern and gliding in a vertical spiral. This modified 3D Dubins scheme is combined with genetic algorithm (GA), together with a rendezvous position selection scheme to find rendezvous trajectories for multiple gliders with minimal energy consumption over all participating vehicles. The properties and capabilities of the proposed path planning methodology are illustrated for several rendezvous mission scenarios. First, a simple application was performed for a single glider to rendezvous with a fix dock. Simulation results show the proposed planner is able to obtain more optimized trajectories when compared with the typical Dubins trajectory with nominal velocity. Additional representative simulations were run to analyse the performance of this path planner for multiple gliders rendezvous. The results demonstrate that the proposed path planner identifies the optimal rendezvous location and generates the corresponding rendezvous trajectories for multiple gliders that ensures they reach their destination with optimized energy consumption.