Technical session talks from ICRA 2012
TechTalks from event: Technical session talks from ICRA 2012
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Stochastic in Robotics and Biological Systems
Low-Cost Collaborative Localization for Large-Scale Multi-Robot SystemsLarge numbers of collaborating robots are advantageous for solving distributed problems. In order to efficiently solve the task at hand, the robots often need accurate localization. In this work, we address the localization problem by developing a solution that has low computational and sensing requirements, and that is easily deployed on large robot teams composed of cheap robots. We build upon a real-time, particle-filter based localization algorithm that is completely decentralized and scalable, and accommodates realistic robot assumptions including noisy sensors, and asynchronous and lossy communication. In order to further reduce this algorithmâ€™s overall complexity, we propose a low-cost particle clustering method, which is particularly well suited to the collaborative localization problem. Our approach is experimentally validated on a team of ten real robots.
Robotic Manifold Tracking of Coherent Structures in FlowsTracking Lagrangian coherent structures in dynamical systems is important for many applications such as oceanography and weather prediction. In this paper, we present a collaborative robotic control strategy designed to track stable and unstable manifolds. The technique does not require global information about the fluid dynamics, and is based on local sensing, prediction, and correction. The collaborative control strategy is implemented on a team of three robots to track coherent structures and manifolds on static flows as well as a noisy time-dependent model of a wind-driven double-gyre often seen in the ocean. We present simulation and experimental results and discuss theoretical guarantees of the collaborative tracking strategy.
Ensemble Synthesis of Distributed Control and Communication StrategiesWe present an ensemble framework for the design of distributed control and communication strategies for the dynamic allocation of a team of robots to a set of tasks. In this work, we assume individual robot controllers are sequentially composed of individual task controllers. This assumption enables the representation of the robot ensemble dynamics as a class of stochastic hybrid systems that can be modeled as continuous-time Markov jump processes where feedback strategies can be derived to control the team's distribution across the tasks. Since the distributed implementation of these feedback strategy requires the estimation of certain population variables, we show how the ensemble model can be expanded to incorporate the dynamics of the information exchange. This then enables us to optimize the individual robot control policies to ensure overall system robustness given some likelihood of resource failures. We consider the assignment of a team of homogeneous robots to a collection of spatially distributed tasks and validate our approach via high-fidelity simulations.
Almost-Uniform Sampling of Rotations for Conformational Searches in Robotics and Structural BiologyWe propose a new method for sampling the rotation group that involves decomposing it into identical Voronoi cells centered on rotational symmetry operations of the Platonic solids. Within each cell, Cartesian coordinates in exponential coordinates are used to achieve almost-uniform sampling at any level of resolution, without having to store large numbers of coordinates, and without requiring sophisticated data structures. We analyze the shape of these cells, and explain how this can be used in the context of conformational searches in the fields of Robotics and Structural Biology.
Randomly Distributed Delayed Communication and Coherent Swarm PatternsPreviously we showed how delay communication between globally coupled self-propelled agents causes new spatio-temporal patterns to arise when the delay coupling is fixed among all agents . In this paper, we show how discrete, randomly distributed delays affect the dynamical patterns. In particular, we investigate how the standard deviation of the time delay distribution affects the stability of the different patterns as well as the switching probability between coherent states.  E. Forgoston and I. Schwartz, â€œDelay-induced instabilities in selfpropelling swarms,â€ Phy. Rev. E, vol. 77, 2008.
Real-Time Automated Modeling and Control of Self-Assembling SystemsWe present the M<sup>3</sup> framework, a formal and generic computational framework for modeling and controlling stochastic distributed systems of purely reactive robots in an automated and real-time fashion. Based on the trajectories of the robots, the framework builds up an internal microscopic representation of the system, which then serves as a blueprint of models at higher abstraction levels. These models are then calibrated using a Maximum Likelihood Estimation (MLE) approach. We illustrate the structure and performance of the framework by performing the online optimization of a simple bang-bang controller for the stochastic self-assembly of water-floating passive modules. The experimental results demonstrate that the generated models can successfully optimize the assembly of desired structures.