We propose two contrasting approaches to the scalable distributed control of a swarm of self-assembling miniaturized robots, specifically the formation of chains of a desired length: (1) a deterministic controller in ...
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We propose two contrasting approaches to the scalable distributed control of a swarm of self-assembling miniaturized robots, specifically the formation of chains of a desired length: (1) a deterministic controller in which robots communicate with each other in order to directly limit the size of each chain, and (2) a probabilistic controller where the average chain size is controlled by the probability a robot will choose to leave its chain. We demonstrate the feasibility of both approaches by implementing them on a real swarm of Alice robots. Using Webots, a realistic simulator for mobile robotics, and macroscopic models based on the Chemical Reaction Network (CRN) framework, we investigate the limitations of the deterministic controller and demonstrate the existence of optimal parameters for the probabilistic controller where exploration and exploitation are well balanced, thus favoring the formation of larger chains.
Using graph theory, this paper investigates how a group of robots, endowed with local positioning (range and bearing from other robots), can be engaged in a leader-following mission whilst keeping a predefined configu...
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Using graph theory, this paper investigates how a group of robots, endowed with local positioning (range and bearing from other robots), can be engaged in a leader-following mission whilst keeping a predefined configuration. The possibility to locally change the behaviors of the follower team to accomodate both tasks is explored. In particular, a methodology to automatically adjust the parameters of the inter-robot interactions and a nonlinear PI controller are explained and implemented. Our approach is supported by a mathematical analysis as well as real robot experiments.
The number of vehicles hitting the road each day is rapidly increasing, and several problems, such as traffic congestion or driver safety, can no longer be solved in the same fashion as before. intelligent transportat...
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The number of vehicles hitting the road each day is rapidly increasing, and several problems, such as traffic congestion or driver safety, can no longer be solved in the same fashion as before. intelligent transportation systems could potentially solve part of these problems, but prototyping, designing and testing cooperative smart vehicles is a cumbersome task. This paper presents a realistic simulator, capable of operating both at microscopic and sub-microscopic level, where intelligent vehicles can be designed and analyzed with a pragmatic approach. A number of advances in robotics have already been transferred to vehicular technology, with a potential increase of this trend into the future. Here, we develop a plugin for a well-established robotics simulator (Webots), in order to reinforce at the virtual level this cross-fertilization between the two areas and create a baseline for realistic studies of future solutions in real intelligent vehicles.
Using graph theory, this paper investigates how a group of vehicles, endowed with local positioning capabilities (range and bearing to other vehicles), can keep a predefined formation. We propose a longitudinal and la...
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Using graph theory, this paper investigates how a group of vehicles, endowed with local positioning capabilities (range and bearing to other vehicles), can keep a predefined formation. We propose a longitudinal and lateral controller that stabilizes a system of several vehicles as well as a collision avoidance mechanism. The stability of our approach is supported by a mathematical analysis as well as realistic simulations.
Designing effective behavioral controllers for mobile robots can be difficult and tedious;this process can be circumvented by using online learning techniques which allow robots to generate their own controllers onlin...
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Formation building and keeping among vehicles has been studied for many years, since 1987 with Reynolds' rules [1]. This paper presents a control algorithm, based on recent work in graph theory, able to reconfigur...
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ISBN:
(纸本)9789639799516
Formation building and keeping among vehicles has been studied for many years, since 1987 with Reynolds' rules [1]. This paper presents a control algorithm, based on recent work in graph theory, able to reconfigure static formations of non-holonomic vehicles endowed solely with local positioning capabilities. The convergence of our approach is mathematically proven and applied to a realistic robotic platform.
We report on real‐robot odor source localization experiments carried out in an environment with obstacles in the odor plume. The robot was equipped with an ethanol sensor and a wind direction sensor, and the experime...
We report on real‐robot odor source localization experiments carried out in an environment with obstacles in the odor plume. The robot was equipped with an ethanol sensor and a wind direction sensor, and the experiments were carried out in a wind tunnel (controlled environment). An enhanced version of the surge‐spiral algorithm was used, which was augmented with a dedicate behavior to manage obstacles (avoid them, or follow their contour). We compare the results in terms of distance overhead and success rate, and discuss the impact of obstacles on plume traversal.
Formation building and keeping among vehicles has been studied for many years, since 1987 with Reynolds' rules [1]. This paper presents a control algorithm, based on recent work in graph theory, able to reconfigur...
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Formation building and keeping among vehicles has been studied for many years, since 1987 with Reynolds' rules [1]. This paper presents a control algorithm, based on recent work in graph theory, able to reconfigure static formations of non-holonomic vehicles endowed solely with local positioning capabilities. The convergence of our approach is mathematically proven and applied to a realistic robotic platform.
We investigate and model the dynamics of two-dimensional stochastic self-assembly of intelligent micro-systems with minimal requirements in terms of sensing, actuation, and control. A microscopic agent-based model acc...
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ISBN:
(纸本)9781615673346
We investigate and model the dynamics of two-dimensional stochastic self-assembly of intelligent micro-systems with minimal requirements in terms of sensing, actuation, and control. A microscopic agent-based model accounts for spatiality and serves as a baseline for assessing the accuracy of models at higher abstraction level. Spatiality is relaxed in Monte Carlo simulations, which still capture the binding energy of each individual aggregate. Finally, we introduce a macroscopic model that only keeps track of the average number of aggregates in each energy state. This model is able to quantitatively and qualitatively predict the dynamics observed at lower, more detailed modeling levels. Since we investigate an idealized system, thus making very few assumptions about the exact nature of the final target system, our framework is potentially applicable to a large body of self-assembling agents ranging from functional micro.-robots endowed with simple sensors and actuators to elementary microfabricated parts. In particular, we show how our suite of models at different abstraction levels can be used for optimizing both the design of the building blocks and the control of the stochastic process.
We present an investigation of specialization when considering the execution of collaborative tasks by a robot swarm. Specifically, we consider the stick-pulling problem first proposed by Martinoli et al. [1], [2] and...
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