Kinodynamic motion planning is an important problem in robotics. It consists of planning the dynamic motion of a robotic system taking into account its kinematic and dynamic constraints. For this class of problems, high dimensionality is a major difficulty and finding an exact time optimal robot motion trajectory is proven to be NP-hard. Probabilistic approximate techniques have therefore been proposed in the literature to solve particular problem instances. These methods include Randomized Potential Field Planners (RPP), Probabilistic Roadmaps (PRM) and Rapidly Exploring Random Trees (RRT). When physical obstacles and differential constraints are added to the problem, applying RPPs or PRMs encounter difficulties. In order to handle these difficulties, RRTs have been proposed. In this study, we consider a multiple camera industrial inspection problem where the concurrent motion of these cameras needs to be planned. The cameras are required to capture maximum number of defect locations while globally avoiding collisions with each other and with obstacles. Our approach is to consider a solution to the kinodynamic planning problem of multiple camera inspection by making use of the RRT algorithm. We explore and resolve issues arising when RRTs are applied to this specific problem class. Along these lines, we consider the cases of a single camera without obstacles and then with obstacles. Then, we attempt to extend the study to the case of multiple camera where we also need to avoid collisions between cameras. We present simulation results to show the performance of our RRT based approach to different instrument configurations and compare with existing deterministic approaches.

Autonomous mobile robots - both aerial and terrestrial vehicles - have gained immense importance due to the broad spectrum of their potential military and civilian applications. One of the indispensable requirements for the autonomy of a mobile vehicle is the vehicle's capability of planning and executing its motion, that is, finding appropriate control inputs for the vehicle such that the resulting vehicle motion satisfies the requirements of the vehicular task. The motion planning and control problem is inherently complex because it involves two disparate sub-problems: (1) satisfaction of the vehicular task requirements, which requires tools from combinatorics and/or formal methods, and (2) design of the vehicle control laws, which requires tools from dynamical systems and control theory. Accordingly, this problem is usually decomposed and solved over two levels of hierarchy. The higher level, called the geometric path planning level, finds a geometric path that satisfies the vehicular task requirements, e.g., obstacle avoidance. The lower level, called the trajectory planning level, involves sufficient smoothening of this geometric path followed by a suitable time parametrization to obtain a reference trajectory for the vehicle. Although simple and efficient, such hierarchical separation suffers a serious drawback: the geometric path planner has no information of the kinematic and dynamic constraints of the vehicle. Consequently, the geometric planner may produce paths that the trajectory planner cannot transform into a feasible reference trajectory. Two main ideas appear in the literature to remedy this problem: (a) randomized sampling-based planning, which eliminates altogether the geometric planner by planning in the vehicle state space, and (b) geometric planning supported by feedback control laws. The former class of methods suffer from a lack of optimality of the resultant trajectory, while the latter class of methods makes a restrictive assumption concerning the vehicle kinematic model. In this thesis, we propose a hierarchical motion planning framework based on a novel mode of interaction between these two levels of planning. This interaction rests on the solution of a special shortest-path problem on graphs, namely, one using costs defined on multiple edge transitions in the path instead of the usual single edge transition costs. These costs are provided by a local trajectory generation algorithm, which we implement using model predictive control and the concept of effective target sets for simplifying the non-convex constraints involved in the problem. The proposed motion planner ensures "consistency" between the two levels of planning, i.e., a guarantee that the higher level geometric path is always associated with a kinematically and dynamically feasible trajectory. We show that the proposed motion planning approach offers distinct advantages in comparison with the competing approaches of discretization of the state space, of randomized sampling-based motion planning, and of local feedback-based, decoupled hierarchical motion planning. Finally, we propose a multi-resolution implementation of the proposed motion planner, which requires accurate descriptions of the environment and the vehicle only for short-term, local motion planning in the immediate vicinity of the vehicle.

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群體運動在現今的電玩、動畫或電影中，有十分重要的應用；透過群體性的運動，可以表現出故事背景設定的張力。在群體運動的模擬中，除了個體本身的運動行為模擬外，重要的是如何呈現出群體運動的整體效果。過去文獻中多數的群體運動模擬系統，要能在群體運動中呈現出特定形狀的效果，多需花費大量的時間反覆調整個體的模擬結果；個體本身的運動行為模型，則多採用虛擬力場的方式，被動的影響個體的運動，較缺乏直觀設定行為模型的方式。本論文的目標是建立一套人群模擬系統，此系統包括兩個部份：第一個部份是使用者可根據個人偏好設定群體運動理想中的外觀形狀，使此系統在模擬前能根據所輸入的環境資訊，利用運動計畫的方式，自動產生形體形變的路徑，以做為維持群體外形的參考目標。第二部份則是改進人群模擬時個體與群體的行為模型。我們利用模糊數學的特性，來表示行為模型以語意表達時的不確定性，使個體的行為能表現出貼近使用者所需求的結果。我們提出了三種類型的模糊行為模型與對應的原生動作，用以表現個體與群體的運動行為。根據我們實做出來的系統及實驗顯示，透過這樣的系統，我們可以利用程序化的方式為電腦動畫師產生具有特定群體外觀的群體模擬，減少其在製作相關動畫所需要的時間與技術成本，同時也提供了直觀的方式建立人群的行為模型，增加行為的豐富性。 / The effects of crowd behavior are becoming indispensable in computer games and computer animation. In crowd simulation, beside the issue of simulating the motion for individual agents, the more important one is how to simulate a specific behavior of a crowd based on the motion of individuals. In order for a crowd to conform to a specific shape, most simulation systems reported in the literature require the users to spend a great amount of time in tuning the behavior parameters of each individual, governed by virtual forces computed according to inter-agent relations. In this thesis, we aim to build a system of crowd simulation consisting two parts: a path planner for a flexible shape and a motion controller with fuzzy logic. The path planner can search for a feasible path for a region of flexible shape allowing the user to set his preference on the shape of the crowd. The local motion controller for each agent is based on fuzzy logic rules that can be used to present the uncertainty of linguistic behavior models. We have proposed three types of fuzzy behavior models and their corresponding primitive actions. Our experiments show that, with this simulation system, we allow a computer animator to use an intuitive way to create specific appearance and richer.

This work focuses on the use of 3D motion capture data to create and optimize a robotic human body model (RHBM) to predict the inverse kinematics of the upper body. The RHBM is a 25 degrees of freedom (DoFs) upper body model with subject specific kinematic parameters. The model was developed to predict the inverse kinematics of the upper body in the simulation of a virtual person, including persons with functional limitations such as a transradial or transhumeral amputation. Motion data were collected from 14 subjects: 10 non-amputees control subjects, 1 person with a transradial amputation, and 3 persons with a transhumeral amputation, in the University of South Florida's (USF) motion analysis laboratory. Motion capture for each subject consisted of the repetition of a series of range of motion (RoM) tasks and activities of daily living (ADLs), which were recorded using an eight camera Vicon (Oxford, UK) motion analysis system. The control subjects were also asked to repeat the motions while wearing a brace on their dominant arm. The RoM tasks consisted of elbow flexion & extension, forearm pronation & supination, shoulder flexion & extension, shoulder abduction & adduction, shoulder rotation, torso flexion & extension, torso lateral flexion, and torso rotation. The ADLs evaluated were brushing one's hair, drinking from a cup, eating with a knife and fork, lifting a laundry basket, and opening a door. The impact of bracing and prosthetic devices on the subjects' RoM, and their motion during ADLs was analyzed. The segment geometries of the subjects' upper body were extracted directly from the motion analysis data using a functional joint center method. With this method there are no conventional or segment length differences between recorded data segments and the RHBM. This ensures the accuracy of the RHBM when reconstructing a recorded task, as the model has the same geometry as the recorded data. A detailed investigation of the weighted least norm, probability density gradient projection method, artificial neural networks was performed to optimize the redundancy RHBM inverse kinematics. The selected control algorithm consisted of a combination of the weighted least norm method and the gradient projection of the null space, minimizing the inverse of the probability density function. This method increases the accuracy of the RHBM while being suitable for a wide range of tasks and observing the required subject constraint inputs.

When a robotic system executes a task, there are a number of responsibilities that belong to either the operator and/or the robot. A more autonomous system has more responsibilities in the completion of a task and must possess the decision making skills necessary to adequately deal with these responsibilities. The system must also handle environmental constraints that limit the region of operability and complicate the execution of tasks. There are decisions about the robot’s internal configuration and how the manipulator should move through space, avoid obstacles, and grasp objects. These motions usually have limits and performance requirements associated with them. Successful completion of tasks in a given environment is aided by knowledge of the robot’s capabilities in its workspace. This not only indicates if a task is possible but can suggest how a task should be completed. In this work, we develop a grasping strategy for selecting and attaining grasp configurations for flexible tasks in environments containing obstacles. This is done by sampling for valid grasping configurations at locations throughout the workspace to generate a task plane. Locations in the task plane that contain more valid configurations are stipulated to have higher dexterity and thus provide greater manipulability of targets. For valid configurations found in the plane, we develop a strategy for selecting which configurations to choose when grasping and/or placing an object at a given location in the workspace. These workspace task planes can also be utilized as a design tool to configure the system around the manipulator’s capabilities. We determine the quality of manipulator positioning in the workspace based on manipulability and locate the best location of targets for manipulation. The knowledge of valid manipulator configurations throughout the workspace can be used to extend the application of task planes to motion planning between grasping configurations. This guides the end-effector through more dexterous workspace regions and to configurations that move the arm away from obstacles. The task plane technique employed here accurately captures a manipulator’s capabilities. Initial tests for exploiting these capabilities for system design and operation were successful, thus demonstrating this method as a viable starting point for incrementally increasing system autonomy. / text

Advances in hardware design have made wheeled mobile robots (WMRs) exceptionally mobile. To fully exploit this mobility, WMR planning, control, and estimation systems require motion models that are fast and accurate. Much of the published theory on WMR modeling is limited to 2D or kinematics, but 3D dynamic (or force-driven) models are required when traversing challenging terrain, executing aggressive maneuvers, and manipulating heavy payloads. This thesis advances the state of the art in both the formulation and calibration of WMR models We present novel WMR model formulations that are high-fidelity, general, modular, and fast. We provide a general method to derive 3D velocity kinematics for any WMR joint configuration. Using this method, we obtain constraints on wheel ground contact point velocities for our differential algebraic equation (DAE)-based models. Our “stabilized DAE” kinematics formulation enables constrained, drift free motion prediction on rough terrain. We also enhance the kinematics to predict nonzero wheel slip in a principled way based on gravitational, inertial, and dissipative forces. Unlike ordinary differential equation (ODE)-based dynamic models which can be very stiff, our constrained dynamics formulation permits large integration steps without compromising stability. Some alternatives like Open Dynamics Engine also use constraints, but can only approximate Coulomb friction at contacts. In contrast, we can enforce realistic, nonlinear models of wheel-terrain interaction (e.g. empirical models for pneumatic tires, terramechanics-based models) using a novel force-balance optimization technique. Simulation tests show our kinematic and dynamic models to be more functional, stable, and efficient than common alternatives. Simulations run 1K-10K faster than real time on an ordinary PC, even while predicting articulated motion on rough terrain and enforcing realistic wheel-terrain interaction models. In addition, we present a novel Integrated Prediction Error Minimization (IPEM) method to calibrate model parameters that is general, convenient, online, and evaluative. Ordinarily system dynamics are calibrated by minimizing the error of instantaneous output predictions. IPEM instead forms predictions by integrating the system dynamics over an interval; benefits include reduced sensing requirements, better observability, and accuracy over a longer horizon. In addition to calibrating out systematic errors, we simultaneously calibrate a model of stochastic error propagation to quantify the uncertainty of motion predictions. Experimental results on multiple platforms and terrain types show that parameter estimates converge quickly during online calibration, and uncertainty is well characterized. Under normal conditions, our enhanced kinematic model can predict nonzero wheel slip as accurately as a full dynamic model for a fraction of the computation cost. Finally, odometry is greatly improved when using IPEM vs. manual calibration, and when using 3D vs. 2D kinematics. To facilitate their use, we have released open source MATLAB and C++ libraries implementing the model formulation and calibration methods in this thesis.

This report presents optimal control methods integrated with hierarchical control framework to realize real-time collision-free optimal trajectories for motion control in kinematic chain manipulator (KCM) robot systems under dynamic environments. Recently, they have been increasingly used in applications where manipulators are required to interact with random objects and humans. As a result, more complex trajectory planning schemes are required. The main objective of this research is to develop new motion control strategies that can enable such robots to operate efficiently and optimally in such unknown and dynamic environments. Two direct optimal control methods: The direct collocation method and discrete mechanics for optimal control methods are investigated for solving the related constrained optimal control problem and the results are compared. Using the receding horizon control structure, open-loop sub-optimal trajectories are generated as real-time input to the controller as opposed to the predefined trajectory over the entire time duration. This, in essence, captures the dynamic nature of the obstacles. The closed-loop position controller is then engaged to span the robot end-effector along this desired optimal path by computing appropriate torque commands for the joint actuators. Employing a two-degree of freedom technique, collision-free trajectories and robot environment information are transmitted in real-time by the aid of a bidirectional connectionless datagram transfer. A hierarchical network control platform is designed to condition triggering of precedent activities between a dedicated machine computing the optimal trajectory and the real-time computer running a low-level controller. Experimental results on a 2-link planar robot are presented to validate the main ideas. Real-time implementation of collision-free workspace trajectory control is achieved for cases where obstacles are arbitrarily changing in the robot workspace.

Research and development leading to the realisation of a fully autonomous and robust multi-fingered hand has been going on for three decades. Yet none can be found in an industrial application. This is largely because we do not fully understand the fundamental mechanics of multi-finger grasping. / This thesis is a study of the mechanics of multi-finger grasping, with particular attention being paid to applying the analysis to experimental co-operative motion tasks between a hand-arm system and grasped object. / Fine manipulation with multi-fingered robot hands is critically influenced by the capacity to achieve stable grasps. By exploring the fundamental mechanics involved, a method for establishing the stability of spatial four finger-contact grasps is obtained. This work examines both frictionless and frictional grasps in two and three dimensions and develops the stability requirements for grasping. The conditions for a stable grasp are expressed as simple equations relating the line coordinates of (i) transitory sliding actuator and (ii) the normal to the tangent plane at every contact location. This is achieved by using the principle of virtual work and a branch of statics known as astatics. / After specifying a grasp in terms of its contact locations and forces the object can be grasped. However, in general the configuration of the hand-arm combination will not be unique, as such a manipulator system has more than six degrees of freedom and is said to be super-abundant. The choice of appropriate shares taken by the arm and hand in delivering the manipulation task needs to be resolved. This can be done making use of a kinematic performance measure based on aligning the grip triangle with the hand line of symmetry and maximising the available manipulation range. The hand-arm combination can then be driven to this desired grasp enabling the manipulator to carry out the specified task effectively. A Salisbury hand and PUMA 760 robot arm are used to demonstrate these co-operative motion tasks. / All the experimental results are presented along with a detailed description of the implementation of a hierarchical robot controller system which incorporates force control of the PUMA 760.

This thesis investigates the questions of where to grasp and how to grasp a given object with an articulated robotic grasping device. To this end, aspects of grasp synthesis and hand motion planning and control are investigated. Grasp synthesis is the process of determining a palm pose with respect to the target object, as well as a hand joint configuration and/or grasp contact points such that a successful grasp execution is allowed. Existing methods tackling the grasp synthesis problem can be categorized in analytical and empirical approaches. Analytical approaches are based on geometric, kinematic and/or dynamic formulations, whereas empirical methods aim at mimicking human strategies.An overarching idea throughout this thesis is to circumvent the curse of dimensionality, which is inherent in high-dimensional planning problems, by incorporating empirical data in analytical approaches. To this end, tools from the field of constrained optimization are used (i) to synthesize grasp families based on available prototype grasps, (ii) to incorporate heuristics capturing human grasp strategies in the grasp synthesis process and (iii) to encode demonstrated grasp motions in primitive motion controllers.The first contribution is related to the computation and analysis of grasp families which are represented as Independent Contact Regions (ICR) on a target object’s surface. To this end, the well-known concept of the Grasp Wrench Space for a single grasp is extended to be applicable for a set of grasps. Applications of ICR include grasp qualification by capturing the robustness of a grasp to position inaccuracies and the visual guidance of a demonstrator in a teleoperating scenario. In the second main contribution of this thesis, it is shown how to reduce the grasp solution space during the synthesis process by accounting for human approach strategies. This is achieved by imposing appropriate constraints to a corresponding optimization problem. A third contribution in this dissertation is made to reactive motion planning. Here, primitive controllers are synthesized by estimating the free parameters of corresponding dynamical systems from multiple demonstrated trajectories. The approach is evaluated on an anthropomorphic robot hand/arm platform. Also, an extension to a Model Predictive Control (MPC) scheme is presented which allows to incorporate state constraints for auxiliary tasks such as obstacle avoidance.