Stochastic recruitment: Controlling state distribution among swarms of hybrid agents

Abstract

This paper introduces a control architecture for centrally controlling the ensemble behavior of many identical agents. A swarm of robots or other agents performing a variety of tasks is often modeled as a collection of hybrid-state agents, whose discrete switching behaviors are controlled by finite state machines. The number of agents in the swarm in a particular discrete state is a function of the rate at which agents transition between state. These state transitions are often modeled as stochastic interactions with the environment. We show that effective control over the distribution of agents in each discrete state can be achieved by designing the agents to transition between tasks randomly, according to a centrally determined state transition probability graph. The centrally-determined policy varies with time and with feedback information by rebroadcasting the probability graph to all agents. Feedback policies will be presented for the case in which the central controller has limited or no knowledge of the states of each agent.

Stochastic recruitment limited control of shape memory alloy based on Markov chain model

“…Each sarcomere is activated by calcium ion through diffusion process, which is a random process. The probability of each sarcomere being activated depends on the ion density and diffusion characteristics (Asada, 2010; Odhner and Asada, 2008). The stochastic recruitment control imitates the control mode of biological muscle, and adopts the control method similar to that of neurons in muscle to activate biological cells randomly to control many identical sub units.…”

Supervisory control for cost-effective redistribution of robotic swarms

“…The objectives of the previous research can be categorized into three types -minimize convergence time or number of state switches [2], optimize energy [3] and only propose a general control law [4]. The methods in the previous literature can be also categorized as closed loop (feedback) control laws and open-loop control laws.…”

“…The authors study the problem of controlling cellular artificial muscles which has the similar problem formulation as the redistribution of robotic swarm across multiple sites in [2], [9] and [10]. The cellular artificial muscle consists of multiple cellular units(agents) with binary state -'ON' and 'OFF'.…”

“…Then we should get the control signal P by minimizing the expected total movement distance L c (N l (0), N tf ). The definition of expected movement distance is shown in (2). So the formulation of our problem is shown in (3).…”

“…We have to design the stochastic control laws for redistribution of the robotic swarm accross multiple sites. Some of the previous literature considers that all the robots will stop moving when the desired configuration is achieved [2], [9], [10], [4]. This literature only presents control laws for redistribution of a robotic swarm across multipe sites without any optimization of any types of cost.…”