2,478 research outputs found
Beyond Reynolds: A Constraint-Driven Approach to Cluster Flocking
In this paper, we present an original set of flocking rules using an
ecologically-inspired paradigm for control of multi-robot systems. We translate
these rules into a constraint-driven optimal control problem where the agents
minimize energy consumption subject to safety and task constraints. We prove
several properties about the feasible space of the optimal control problem and
show that velocity consensus is an optimal solution. We also motivate the
inclusion of slack variables in constraint-driven problems when the global
state is only partially observable by each agent. Finally, we analyze the case
where the communication topology is fixed and connected, and prove that our
proposed flocking rules achieve velocity consensus.Comment: 6 page
Optimal learning under robustness and time-consistency
We model learning in a continuous-time Brownian setting where there is prior ambiguity. The associated model of preference values robustness and is time-consistent. It is applied to study optimal learning when the choice between actions can be postponed, at a per-unit-time cost, in order to observe a signal that provides information about an unknown parameter. The corresponding optimal stopping problem is solved in closed form, with a focus on two specific settings: Ellsberg’s two-urn thought experiment expanded to allow learning before the choice of bets, and a robust version of the classical problem of sequential testing of two simple hypotheses about the unknown drift of a Wiener process. In both cases, the link between robustness and the demand for learning is studied.Accepted manuscrip
Autonomous navigation of ships by combining optimal trajectory planning with informed graph search
Autonomous trajectory generation plays an essential role in the navigation of vehicles in space as well as in terrestrial scenarios, i.e. in the air, on solid ground, or water. For the latter, the navigation of ships in ports has specific challenges since ship dynamics are highly nonlinear with limited agility, while the manoeuvre space in ports is limited. Nevertheless, for providing support to humanly designed control strategies, autonomously generated trajectories have not only to be feasible, i.e. collision-free but shall also be optimal with respect to manoeuvre time and control effort. This article presents a novel approach to autonomous trajectory planning on the basis of precomputed and connectable trajectory segments, the so-called motion primitives, and an A*-search algorithm. Sequences of motion primitives provide an initial guess for a subsequent optimization by which optimal trajectories are found even in terrains with many obstacles. We illustrate the approach with different navigation scenarios
3LP: a linear 3D-walking model including torso and swing dynamics
In this paper, we present a new model of biped locomotion which is composed
of three linear pendulums (one per leg and one for the whole upper body) to
describe stance, swing and torso dynamics. In addition to double support, this
model has different actuation possibilities in the swing hip and stance ankle
which could be widely used to produce different walking gaits. Without the need
for numerical time-integration, closed-form solutions help finding periodic
gaits which could be simply scaled in certain dimensions to modulate the motion
online. Thanks to linearity properties, the proposed model can provide a
computationally fast platform for model predictive controllers to predict the
future and consider meaningful inequality constraints to ensure feasibility of
the motion. Such property is coming from describing dynamics with joint torques
directly and therefore, reflecting hardware limitations more precisely, even in
the very abstract high level template space. The proposed model produces
human-like torque and ground reaction force profiles and thus, compared to
point-mass models, it is more promising for precise control of humanoid robots.
Despite being linear and lacking many other features of human walking like CoM
excursion, knee flexion and ground clearance, we show that the proposed model
can predict one of the main optimality trends in human walking, i.e. nonlinear
speed-frequency relationship. In this paper, we mainly focus on describing the
model and its capabilities, comparing it with human data and calculating
optimal human gait variables. Setting up control problems and advanced
biomechanical analysis still remain for future works.Comment: Journal paper under revie
Learning Motion Primitives Automata for Autonomous Driving Applications
Motion planning methods often rely on libraries of primitives. The selection of primitives
is then crucial for assuring feasible solutions and good performance within the motion planner.
In the literature, the library is usually designed by either learning from demonstration, relying
entirely on data, or by model-based approaches, with the advantage of exploiting the dynamical
system’s property, e.g., symmetries. In this work, we propose a method combining data with a
dynamical model to optimally select primitives. The library is designed based on primitives with
highest occurrences within the data set, while Lie group symmetries from a model are analysed
in the available data to allow for structure-exploiting primitives. We illustrate our technique in
an autonomous driving application. Primitives are identified based on data from human driving,
with the freedom to build libraries of different sizes as a parameter of choice. We also compare
the extracted library with a custom selection of primitives regarding the performance of obtained
solutions for a street layout based on a real-world scenario
A dissipativity characterization of velocity turnpikes in optimal control problems for mechanical systems
Turnpikes have recently gained significant research interest in optimal control, since they allow for pivotal insights into the structure of solutions to optimal control problems. So far, mainly steady state solutions which serve as optimal operation points, are studied. This is in contrast to time-varying turnpikes, which are in the focus of this paper. More concretely, we analyze symmetry-induced velocity turnpikes, i.e. controlled relative equilibria, called trim primitives, which are optimal operation points regarding the given cost criterion. We characterize velocity turnpikes by means of dissipativity inequalities. Moreover, we study the equivalence between optimal control problems and steady-state problems via the corresponding necessary optimality conditions. An academic example is given for illustration
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