2,801 research outputs found
Valuing Fuelwood Resources Using a Site Choice Model of Fuelwood Collection
Resource /Energy Economics and Policy,
Optimal Control of Robotic Systems and Biased Riemannian Splines
In this paper, we study mechanical optimal control problems on a given
Riemannian manifold in which the cost is defined by a general cometric
. This investigation is motivated by our studies in robotics, in
which we observed that the mathematically natural choice of cometric -- the dual of -- does not always capture the true cost of the
motion. We then, first, discuss how to encode the system's torque-based
actuators configuration into a cometric . Second, we provide and
prove our main theorem, which characterizes the optimal solutions of the
problem associated to general triples in terms of a 4th
order differential equation. We also identify a tensor appearing in this
equation as the geometric source of "biasing" of the solutions away from
ordinary Riemannian splines and geodesics for . Finally, we provide
illustrative examples and practical demonstration of the biased splines as
providing the true optimizers in a concrete robotics system
Geometric Gait Optimization for Inertia-Dominated Systems With Nonzero Net Momentum
Inertia-dominated mechanical systems can achieve net displacement by 1)
periodically changing their shape (known as kinematic gait) and 2) adjusting
their inertia distribution to utilize the existing nonzero net momentum (known
as momentum gait). Therefore, finding the gait that most effectively utilizes
the two types of locomotion in terms of the magnitude of the net momentum is a
significant topic in the study of locomotion. For kinematic locomotion with
zero net momentum, the geometry of optimal gaits is expressed as the equilibria
of system constraint curvature flux through the surface bounded by the gait,
and the cost associated with executing the gait in the metric space. In this
paper, we identify the geometry of optimal gaits with nonzero net momentum
effects by lifting the gait description to a time-parameterized curve in
shape-time space. We also propose the variational gait optimization algorithm
corresponding to the lifted geometric structure, and identify two distinct
patterns in the optimal motion, determined by whether or not the kinematic and
momentum gaits are concentric. The examples of systems with and without
fluid-added mass demonstrate that the proposed algorithm can efficiently solve
forward and turning locomotion gaits in the presence of nonzero net momentum.
At any given momentum and effort limit, the proposed optimal gait that takes
into account both momentum and kinematic effects outperforms the reference
gaits that each only considers one of these effects.Comment: 8 pages, 9 figures, accepted to IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS) 202
Towards Geometric Motion Planning for High-Dimensional Systems: Gait-Based Coordinate Optimization and Local Metrics
Geometric motion planning offers effective and interpretable gait analysis
and optimization tools for locomoting systems. However, due to the curse of
dimensionality in coordinate optimization, a key component of geometric motion
planning, it is almost infeasible to apply current geometric motion planning to
high-dimensional systems. In this paper, we propose a gait-based coordinate
optimization method that overcomes the curse of dimensionality. We also
identify a unified geometric representation of locomotion by generalizing
various nonholonomic constraints into local metrics. By combining these two
approaches, we take a step towards geometric motion planning for
high-dimensional systems. We test our method in two classes of high-dimensional
systems - low Reynolds number swimmers and free-falling Cassie - with up to
11-dimensional shape variables. The resulting optimal gait in the
high-dimensional system shows better efficiency compared to that of the
reduced-order model. Furthermore, we provide a geometric optimality
interpretation of the optimal gait.Comment: 7 pages, 6 figures, submitted to the 2024 IEEE International
Conference on Robotics and Automation (ICRA 2024
A meta-analysis of transdiagnostic cognitive behavioural therapy in the treatment of child and young person anxiety disorders
Background: Previous meta-analyses of cognitive-behavioural therapy (CBT) for children and young people with anxiety disorders have not considered the efļ¬cacy of transdiagnostic CBT for the remission of childhood anxiety. Aim: To provide a meta-analysis on the efļ¬cacy of transdiagnostic CBT for children and young people with anxiety disorders. Methods: The analysis included randomized controlled trials using transdiagnostic CBT for children and young people formally diagnosed with an anxiety disorder. An electronic search was conducted using the following databases: ASSIA, Cochrane Controlled Trials Register, Current Controlled Trials, Medline, PsycArticles, PsychInfo, and Web of Knowledge. The search terms included āanxiety disorder(s)ā, āanxiāā, ācognitive behavioā, āCBTā, āchildāā, āchildrenā, āpaediatricā, āadolescent(s)ā, āadolescenceā, āyouthā and āyoung peāā. The studies identiļ¬ed from this search were screened against the inclusion and exclusion criteria, and 20 studies were identiļ¬ed as appropriate for inclusion in the current meta-analysis. Pre- and posttreatment (or control period) data were used for analysis. Results: Findings indicated signiļ¬cantly greater odds of anxiety remission from pre- to posttreatment for those engaged in the transdiagnostic CBT intervention compared with those in the control group, with children in the treatment condition 9.15 times more likely to recover from their anxiety diagnosis than children in the control group. Risk of bias was not correlated with study effect sizes. Conclusions: Transdiagnostic CBT seems effective in reducing symptoms of anxiety in children and young people. Further research is required to investigate the efļ¬cacy of CBT for children under the age of 6
Clinical Use of PPARĪ³ Ligands in Cancer
The role of PPARĪ³ in adipocyte differentiation has fueled intense interest in the function of this steroid nuclear receptor for regulation of malignant cell growth and differentiation. Given the antiproliferative and differentiating effects of PPARĪ³ ligands on liposarcoma cells, investigation of PPARĪ³ expression and ligand activation in other solid tumors such as breast, colon, and prostate cancers ensued. The anticancer effects of PPARĪ³ ligands in cell culture and rodent models of a multitude of tumor types suggest broad applicability of these agents to cancer therapy. This review focuses on the clinical use of PPARĪ³ ligands, specifically the thiazolidinediones, for the treatment and prevention of cancer
Optimal Gait Families using Lagrange Multiplier Method
The robotic locomotion community is interested in optimal gaits for control.
Based on the optimization criterion, however, there could be a number of
possible optimal gaits. For example, the optimal gait for maximizing
displacement with respect to cost is quite different from the maximum
displacement optimal gait. Beyond these two general optimal gaits, we believe
that the optimal gait should deal with various situations for high-resolution
of motion planning, e.g., steering the robot or moving in "baby steps." As the
step size or steering ratio increases or decreases, the optimal gaits will
slightly vary by the geometric relationship and they will form the families of
gaits. In this paper, we explored the geometrical framework across these
optimal gaits having different step sizes in the family via the Lagrange
multiplier method. Based on the structure, we suggest an optimal locus
generator that solves all related optimal gaits in the family instead of
optimizing each gait respectively. By applying the optimal locus generator to
two simplified swimmers in drag-dominated environments, we verify the behavior
of the optimal locus generator.Comment: 6 page
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