Velocity constrained trajectory generation for a collinear Mecanum wheeled robot

While much research has been conducted into the generation of smooth trajectories for underactuated unstable aerial vehicles such as quadrotors, less attention has been paid to the application of the same techniques to ground based omnidirectional dynamically balancing robots. These systems have more control authority over their linear accelerations than aerial vehicles, meaning trajectory smoothness is less of a critical design parameter. However, when operating in indoor environments these systems must often adhere to relatively low velocity constraints, resulting in very conservative trajectories when enforced using existing trajectory optimisation methods. This paper makes two contributions; this gap is bridged by the extension of these existing methods to create a fast velocity constrained trajectory planner, with trajectory timing characteristics derived from the optimal minimum-time solution of a simplified acceleration and velocity constrained model. Next, a differentially flat model of an omnidirectional balancing robot utilizing a collinear Mecanum drive is derived, which is used to allow an experimental prototype of this configuration to smoothly follow these velocity constrained trajectories

A 3D dynamic model of a spherical wheeled self-balancing robot

Mobility through balancing on spherical wheels has recently received some attention in the robotics literature. Unlike traditional wheeled platforms, the operation of such platforms depends heavily on understanding and working with system dynamics, which have so far been approximated with simple planar models and their decoupled extension to three dimensions. Unfortunately, such models cannot capture inherently spatial aspects of motion such as yaw motion arising from the wheel rolling motion or coupled inertial effects for fast maneuvers. In this paper, we describe a novel, fully-coupled 3D model for such spherical wheeled platforms and show that it not only captures relevant spatial aspects of motion, but also provides a basis for controllers better informed by system dynamics. We focus our evaluations to simulations with this model and use circular paths to reveal advantages of this model in dynamically rich situations. © 2012 IEEE

3D dynamic modeling of spherical wheeled self-balancing mobile robot

Ankara : The Department of Electrical and Electronics Engineering and the Graduate School of Engineering and Science of Bilkent University, 2012.Thesis (Master's) -- Bilkent University, 2012.Includes bibliographical references.In recent years, dynamically stable platforms that move on spherical wheels, also known as BallBots, gained popularity in the robotics literature as an alternative locomotion method to statically stable wheeled mobile robots. In contrast to wheeled platforms which do not have to explicitly be concerned about their balance, BallBot platforms must be informed about their dynamics and actively try to maintain balance. Up until now, such platforms have been approximated by simple planar models, with extensions to three dimensions through the combination of decoupled models in orthogonal sagittal planes. However, even though capturing certain aspects of the robot’s motion is possible with such decoupled models, they cannot represent inherently spatial aspects of motion such as yaw rotation or coupled inertial effects due to the motion of the rigid body. In this thesis, we introduce a novel, fully-coupled 3D model for such spherical wheeled balancing platforms. We show that our novel model captures important spatial aspects of motion that have previously not been captured by planar models. Moreover, our new model provides a better basis for controllers that are informed by more expressive system dynamics. In order to establish the expressivity and accuracy of this new model, we present simulation studies in dynamically rich situations. We use circular paths to reveal the advantages of the new model for fast maneuvers. Additionally, we introduce new inverse-dynamics controllers for a better attitude control and investigate within simulations the capability of sustaining dynamic behaviors. We study the relation between circular motions in attitude angles and associated motions in positional variables for BallBot locomotion.İnal, Ali NailM.S

Receding-horizon motion planning of quadrupedal robot locomotion

Quadrupedal robots are designed to offer efficient and robust mobility on uneven terrain. This thesis investigates combining numerical optimization and machine learning methods to achieve interpretable kinodynamic planning of natural and agile locomotion. The proposed algorithm, called Receding-Horizon Experience-Controlled Adaptive Legged Locomotion (RHECALL), uses nonlinear programming (NLP) with learned initialization to produce long-horizon, high-fidelity, terrain-aware, whole-body trajectories. RHECALL has been implemented and validated on the ANYbotics ANYmal B and C quadrupeds on complex terrain. The proposed optimal control problem formulation uses the single-rigid-body dynamics (SRBD) model and adopts a direct collocation transcription method which enables the discovery of aperiodic contact sequences. To generate reliable trajectories, we propose fast-to-compute analytical costs that leverage the discretization and terrain-dependent kinematic constraints. To extend the formulation to receding-horizon planning, we propose a segmentation approach with asynchronous centre of mass (COM) and end-effector timings and a heuristic initialization scheme which reuses the previous solution. We integrate real-time 2.5D perception data for online foothold selection. Additionally, we demonstrate that a learned stability criterion can be incorporated into the planning framework. To accelerate the convergence of the NLP solver to locally optimal solutions, we propose data-driven initialization schemes trained using supervised and unsupervised behaviour cloning. We demonstrate the computational advantage of the schemes and the ability to leverage latent space to reconstruct dynamic segments of plans which are several seconds long. Finally, in order to apply RHECALL to quadrupeds with significant leg inertias, we derive the more accurate lump leg single-rigid-body dynamics (LL-SRBD) and centroidal dynamics (CD) models and their first-order partial derivatives. To facilitate intuitive usage of costs, constraints and initializations, we parameterize these models by Euclidean-space variables. We show the models have the ability to shape rotational inertia of the robot which offers potential to further improve agility

Underwater Vehicles

For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties

Actuation-Aware Simplified Dynamic Models for Robotic Legged Locomotion

In recent years, we witnessed an ever increasing number of successful hardware implementations of motion planners for legged robots. If one common property is to be identified among these real-world applications, that is the ability of online planning. Online planning is forgiving, in the sense that it allows to relentlessly compensate for external disturbances of whatever form they might be, ranging from unmodeled dynamics to external pushes or unexpected obstacles and, at the same time, follow user commands. Initially replanning was restricted only to heuristic-based planners that exploit the low computational effort of simplified dynamic models. Such models deliberately only capture the main dynamics of the system, thus leaving to the controllers the issue of anchoring the desired trajectory to the whole body model of the robot. In recent years, however, we have seen a number of new approaches attempting to increase the accuracy of the dynamic formulation without trading-off the computational efficiency of simplified models. In this dissertation, as an example of successful hardware implementation of heuristics and simplified model-based locomotion, I describe the framework that I developed for the generation of an omni-directional bounding gait for the HyQ quadruped robot. By analyzing the stable limit cycles for the sagittal dynamics and the Center of Pressure (CoP) for the lateral stabilization, the described locomotion framework is able to achieve a stable bounding while adapting to terrains of mild roughness and to sudden changes of the user desired linear and angular velocities. The next topic reported and second contribution of this dissertation is my effort to formulate more descriptive simplified dynamic models, without trading off their computational efficiency, in order to extend the navigation capabilities of legged robots to complex geometry environments. With this in mind, I investigated the possibility of incorporating feasibility constraints in these template models and, in particular, I focused on the joint torques limits which are usually neglected at the planning stage. In this direction, the third contribution discussed in this thesis is the formulation of the so called actuation wrench polytope (AWP), defined as the set of feasible wrenches that an articulated robot can perform given its actuation limits. Interesected with the contact wrench cone (CWC), this yields a new 6D polytope that we name feasible wrench polytope (FWP), defined as the set of all wrenches that a legged robot can realize given its actuation capabilities and the friction constraints. Results are reported where, thanks to efficient computational geometry algorithms and to appropriate approximations, the FWP is employed for a one-step receding horizon optimization of center of mass trajectory and phase durations given a predefined step sequence on rough terrains. For the sake of reachable workspace augmentation, I then decided to trade off the generality of the FWP formulation for a suboptimal scenario in which a quasi-static motion is assumed. This led to the definition of the, so called, local/instantaneous actuation region and of the global actuation/feasible region. They both can be seen as different variants of 2D linear subspaces orthogonal to gravity where the robot is guaranteed to place its own center of mass while being able to carry its own body weight given its actuation capabilities. These areas can be intersected with the well known frictional support region, resulting in a 2D linear feasible region, thus providing an intuitive tool that enables the concurrent online optimization of actuation consistent CoM trajectories and target foothold locations on rough terrains

Integrated motion planning and model learning for mobile robots with application to marine vehicles

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 269-275).Robust motion planning algorithms for mobile robots consider stochasticity in the dynamic model of the vehicle and the environment. A practical robust planning approach balances the duration of the motion plan with the probability of colliding with obstacles. This thesis develops fast analytic algorithms for predicting the collision probability due to model uncertainty and random disturbances in the environment for a planar holonomic vehicle such as a marine surface vessel. These predictions lead to a robust motion planning algorithm that nds the optimal motion plan quickly and efficiently. By incorporating model learning into the predictions, the integrated algorithm exhibits emergent active learning strategies to autonomously acquire the model data needed to safely and eectively complete the mission. The motion planner constructs plans through a known environment by concatenating maneuvers based upon speed controller setpoints. A model-based feedforward/ feedback controller is used to track the resulting reference trajectory, and the model parameters are learned online with a least squares regression algorithm. The path-following performance of the vehicle depends on the effects of unknown environmental disturbances and modeling error. The convergence rate of the parameter estimates depends on the motion plan, as different plans excite different modes of the system.(cont.) By predicting how the collision probability is affected by the parameter covariance evolution, the motion planner automatically incorporates active learning strategies into the motion plans. In particular, the vehicle will practice maneuvers in the open regions of the configuration space before using them in the constrained regions to ensure that the collision risk due to modeling error is low. High-level feedback across missions allows the system to recognize configuration changes and quickly learn new model parameters as necessary. Simulations and experimental results using an autonomous marine surface vessel are presented.by Matthew Greytak.Ph.D