6,157 research outputs found
Analysis of Performance Indices for Simulated Skeleton Descents
In the winter Olympic sport of Skeleton, sliders sprint and load themselves onto the sled facing head forwards. The slider uses primarily their shoulders and torso to apply control to the direction of the sled as it progressively gains speed during its descent. These small control course keeping maneuvers alongside more severe use of toe tapping onto the ice will help determine the eventual trajectory of the sled. It is therefore of interest to consider for a possible trajectory what control actions will determine the fastest descent time and in particular what metrics should be examined. In this paper a three degree-of-freedom simulation has been developed to analyse the influence of different control strategies on the descent time of a bob-skeleton. A proportional-derivative (PD) controller is used to steer the simulation down a representation of the Igls ice-track. Parametric variations of the simulation's performance were analysed and compared to identify possible correlations for controllers assist the design of an optimal controller. Analysis of the results have identified positive correlations between descent time, transverse distance travelled and energy dissipation establishing that the fastest descent time is achieved by minimising the energy lost through the descent
A Simple Mono-Dimensional Approach for Lap Time Optimisation
Lap time minimisation methods have great relevance in the analysis of race tracks, and in the design and optimisation of race vehicles. Several lap time minimisation approaches have been proposed in the literature, which are computationally demanding because they need to either solve differential equations or to implement a forward−backward integration based on an apex-finding method. This paper proposes an alternative method, based on a mono-dimensional quasi-steady-state numerical approach. The proposed approach uses a simplified vehicle model accounting for combined tyre−road interactions, aerodynamic effects, and power limitations. The method exploits the knowledge of the curvature of the trajectory, which is worked out through a rigorous approach that allows for the use trajectories defined with respect to ageneric curve parameter and not necessarily the arc length. An iterative routine is implemented that exploits the vehicle dynamics, without solving differential equations or performing forward−backward integrations from the trajectory apexes. Simulations are carried out on three different tracks and are shown to be computationally efficient. Despite being intentionally simple, the proposed method allows to grasp key aspects of the problem, such as the effect of the combined tyre−road interactions on the acceleration profiles, and the effect of aerodynamic drag and downforce on the position of the braking point on the track and on the speed profile
Automated pick-up of suturing needles for robotic surgical assistance
Robot-assisted laparoscopic prostatectomy (RALP) is a treatment for prostate
cancer that involves complete or nerve sparing removal prostate tissue that
contains cancer. After removal the bladder neck is successively sutured
directly with the urethra. The procedure is called urethrovesical anastomosis
and is one of the most dexterity demanding tasks during RALP. Two suturing
instruments and a pair of needles are used in combination to perform a running
stitch during urethrovesical anastomosis. While robotic instruments provide
enhanced dexterity to perform the anastomosis, it is still highly challenging
and difficult to learn. In this paper, we presents a vision-guided needle
grasping method for automatically grasping the needle that has been inserted
into the patient prior to anastomosis. We aim to automatically grasp the
suturing needle in a position that avoids hand-offs and immediately enables the
start of suturing. The full grasping process can be broken down into: a needle
detection algorithm; an approach phase where the surgical tool moves closer to
the needle based on visual feedback; and a grasping phase through path planning
based on observed surgical practice. Our experimental results show examples of
successful autonomous grasping that has the potential to simplify and decrease
the operational time in RALP by assisting a small component of urethrovesical
anastomosis
Trajectory-Following and Off-Tracking Minimization of Long Combination Vehicles: A Comparison Between Nonlinear and Linear Model Predictive Control
In this paper, we compared the linear and nonlinear motion prediction models of a long combination vehicle (LCV). We designed a nonlinear model predictive control (NMPC) for trajectory-following and off-tracking minimisation of the LCV. The used prediction model allowed coupled longitudinal and lateral dynamics together with the possibility of a combined steering, propulsion and braking control of those vehicles in long prediction horizons and in all ranges of forward velocity. For LCVs where the vehicle model is highly nonlinear, we showed that the control actions calculated by a linear time-varying model predictive control (LTV-MPC) are relatively close to those obtained by the NMPC if the guess linearisation trajectory is sufficiently close to the nonlinear solution, in contrast to linearising for specific operating conditions that limit the generality of the designed function. We discussed how those guess trajectories can be obtained allowing off-line fixed time-varying model linearisation that is beneficial for real-time implementation of MPC in LCVs with long prediction horizons. The long prediction horizons are necessary for motion planning and trajectory-following of LCVs to maintain stability and tracking quality, e.g. by optimally reducing the speed prior to reaching a curve, and by generating control actions within the actuators limits
Prediction of three-dimensional crutch walking patterns using a torque-driven model
Computational prediction of 3D crutch-assisted walking patterns is a challenging problem that could be applied to study different biomechanical aspects of crutch walking in virtual subjects, to assist physiotherapists to choose the optimal crutch walking pattern for a specific subject, and to help in the design and control of exoskeletons, when crutches are needed for balance. The aim of this work is to generate a method to predict three-dimensional crutch-assisted walking motions following different patterns without tracking any experimental data. To reach this goal, we collected gait data from a healthy subject performing a four-point non-alternating crutch walking pattern, and developed a 3D torque-driven full-body model of the subject including the crutches and foot- and crutch-ground contact models. First, we developed a predictive (i.e., no tracking of experimental data) optimal control problem formulation to predict crutch walking cycles following the same pattern as the experimental data collected, using different cost functions. To reduce errors with respect to reference data, a cost function combining minimization terms of angular momentum, mechanical power, joint jerk and torque change was chosen. Then, the problem formulation was adapted to handle different foot- and crutch-ground conditions to make it capable of predicting three new crutch walking patterns, one of them at different speeds. A key aspect of our algorithm is that having ground reactions as additional controls allows one to define phases inside the cycle without the need of formulating a multiple-phase problem, thus facilitating the definition of different crutch walking patterns.Postprint (author's final draft
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control
Trial-and-error based reinforcement learning (RL) has seen rapid advancements
in recent times, especially with the advent of deep neural networks. However,
the majority of autonomous RL algorithms require a large number of interactions
with the environment. A large number of interactions may be impractical in many
real-world applications, such as robotics, and many practical systems have to
obey limitations in the form of state space or control constraints. To reduce
the number of system interactions while simultaneously handling constraints, we
propose a model-based RL framework based on probabilistic Model Predictive
Control (MPC). In particular, we propose to learn a probabilistic transition
model using Gaussian Processes (GPs) to incorporate model uncertainty into
long-term predictions, thereby, reducing the impact of model errors. We then
use MPC to find a control sequence that minimises the expected long-term cost.
We provide theoretical guarantees for first-order optimality in the GP-based
transition models with deterministic approximate inference for long-term
planning. We demonstrate that our approach does not only achieve
state-of-the-art data efficiency, but also is a principled way for RL in
constrained environments.Comment: Accepted at AISTATS 2018
Algorithm for Pocket Milling using Zig-zag Tool Path
Pocket-milling operations are widely used for scooping out materials during the machiningof aircraft components. This paper presents a tool-path planning algorithm for pocket-millingusing zig-zag method. The algorithm consists of basically three modules, viz., generating toolpathelements using pocket geometry entities as input, finding out intersection points (edgepoints), and rearranging points in a zig-zag fashion. OPTPATH algorithm1,2 is used for generatingtool-path elements. These elements thus generated are used to find out the intersection pointswith all entities. The valid points are arranged in a zig-zag way, which are used for machiningany pocket considered. This algorithm works satisfactorily for all the pocket boundaries havingline-line, line-arc, and arc-arc geometry entities
Automation of On-Board Flightpath Management
The status of concepts and techniques for the design of onboard flight path management systems is reviewed. Such systems are designed to increase flight efficiency and safety by automating the optimization of flight procedures onboard aircraft. After a brief review of the origins and functions of such systems, two complementary methods are described for attacking the key design problem, namely, the synthesis of efficient trajectories. One method optimizes en route, the other optimizes terminal area flight; both methods are rooted in optimal control theory. Simulation and flight test results are reviewed to illustrate the potential of these systems for fuel and cost savings
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