Probabilistic density control for swarm of decentralized ON-OFF agents with safety constraints

Demir, Nazlı; Açıkmeşe, Behçet

doi:10.1109/acc.2015.7172157

Cited by 7 publications

(7 citation statements)

References 31 publications

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“…Top-down approaches emphasize developing a macroscopic model for the whole system. For instance, population fractions associated with given tasks are represented as states, and the dynamics of the population fractions is modeled by Markov chains [12], [24]- [26] or differential equations [27]- [31]. Given a desired fraction distribution over the tasks, agents can converge to the desired status by following local decision policies (e.g., the associated rows or columns of the current Markov matrix).…”

Section: A Decentralized Coordination Of Robotic Swarmsmentioning

confidence: 99%

Anonymous Hedonic Game for Task Allocation in a Large-Scale Multiple Agent System

2018

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This paper proposes a novel game-theoretical autonomous decision-making framework to address a task allocation problem for a swarm of multiple agents. We consider cooperation of self-interested agents, and show that our proposed decentralized algorithm guarantees convergence of agents with social inhibition to a Nash stable partition (i.e., social agreement) within polynomial time. The algorithm is simple and executable based on local interactions with neighbor agents under a stronglyconnected communication network and even in asynchronous environments. We analytically present a mathematical formulation for computing the lower bound of suboptimality of the solution, and additionally show that 50% of suboptimality can be at least guaranteed if social utilities are non-decreasing functions with respect to the number of co-working agents. The results of numerical experiments confirm that the proposed framework is scalable, fast adaptable against dynamical environments, and robust even in a realistic situation.

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Section: A Decentralized Coordination Of Robotic Swarmsmentioning

confidence: 99%

Anonymous Hedonic Game for Task Allocation in a Large-Scale Multiple Agent System

2018

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“…This paper investigates global control of particles that have no onboard computation. This prevents us from applying controllers that require computation on the agents, as in [7]- [9]. Another control paradigm is to construct robots with physical heterogeneity so that they respond differently to a global broadcast control signal.…”

Section: A Global Control Of Microrobotsmentioning

confidence: 99%

Steering a Swarm of Particles Using Global Inputs and Swarm Statistics

et al. 2018

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Microrobotics has the potential to revolutionize many applications including targeted material delivery, assembly, and surgery. The same properties that promise breakthrough solutions-small size and large populations-present unique challenges for controlling motion.Robotic manipulation usually assumes intelligent agents, not particle systems manipulated by a global signal. To identify the key parameters for particle manipulation, we used a collection of online games where players steer swarms of up to 500 particles to complete manipulation challenges. We recorded statistics from over ten thousand players. Inspired by techniques where human operators performed well, we investigate controllers that use only the mean and variance of the swarm. We prove the mean position is controllable and provide conditions under which variance is controllable. We next derive automatic controllers for these and a hysteresis-based switching control to regulate the first two moments of the particle distribution. Finally, we employ these controllers as primitives for an object manipulation task and implement all controllers on 100 kilobots controlled by the direction of a global light source.

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“…In this section, we introduce the probabilistic density control problem for ON/OFF agents. First, we consider the simplified model of mode-switching ON/OFF agents to demonstrate the concept and formulation that first appeared in [10]. Then, we generalize this model in the next section.…”

Section: Markov Chain Model For Density Control Of On/off Agents: Sin...mentioning

confidence: 99%

“…This assumption makes the initial formulation more transparent. Furthermore, this simpler model also captures an interesting interpretation of the density control problem as presented in [10], that is, ON/OFF agents are moving with the dynamics induced by the stochastic environment when they are ON and they are staying stationary when they are OFF.…”

Section: Markov Chain Model For Density Control Of On/off Agents: Sin...mentioning

confidence: 99%

“…Among many possible applications of the theoretical framework presented, we illustrate the results with a swarm control example. Our previous research has developed methods for swarm control policy synthesis without the notion of agent modes or actions [8], [3], [9], then the idea is extended to control of a swarm of partially controlled ON/OFF agents in [10], where we only considered the case when there is only a single action for the ON mode and a deterministic action for the OFF mode. In this paper, we generalize this model for the case when there is an ON mode with multiple actions and an OFF mode with stochastic transitions, through a new Markov decision model with additional measurements for state transitions.…”

Section: Introductionmentioning

confidence: 99%

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Safe Markov Chains for ON/OFF Density Control With Observed Transitions

Demirer

Chamie

Açıkmeşe

2018

IEEE Trans. Automat. Contr.

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This paper presents a convex optimization approach to control the density distribution of autonomous mobile agents with two control modes: ON and OFF. The main new characteristic distinguishing this model from standard Markov decision models is the existence of the ON control mode and its observed actions. When an agent is in the ON mode, it can measure the instantaneous outcome of one of the actions corresponding to the ON mode and decides whether it should take this action or not based on this new observation. If it does not take this action, it switches to the OFF mode where it transitions to the next state based on a predetermined set of transitional probabilities, without making any additional observations. In this decisionmaking model, each agent acts autonomously according to an ON/OFF decision policy, and the discrete probability distribution for the agent's state evolves according to a discrete-time Markov chain that is a linear function of the stochastic environment (i.e., transition probabilities) and the ON/OFF decision policy. The relevant policy synthesis is formulated as a convex optimization problem where hard safety and convergence constraints are imposed on the resulting Markov matrix. We first consider the case where the ON mode has a single action and the OFF mode has deterministic transitions (rather than stochastic) to demonstrate the model and the ideas behind our approach, which is then generalized to the case where ON mode has multiple actions and OFF mode has stochastic transitions.

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Probabilistic density control for swarm of decentralized ON-OFF agents with safety constraints

Cited by 7 publications

References 31 publications

Anonymous Hedonic Game for Task Allocation in a Large-Scale Multiple Agent System

Anonymous Hedonic Game for Task Allocation in a Large-Scale Multiple Agent System

Steering a Swarm of Particles Using Global Inputs and Swarm Statistics

Safe Markov Chains for ON/OFF Density Control With Observed Transitions

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