Thermodynamics-Inspired Macroscopic States of Bounded Swarms

Abstract

The collective behavior of swarms is extremely difficult to estimate or predict, even when the local agent rules are known and simple. The presented work seeks to leverage the similarities between fluids and swarm systems to generate a thermodynamics-inspired characterization of the collective behavior of robotic swarms. While prior works have borrowed tools from fluid dynamics to design swarming behaviors, they have usually avoided the task of generating a fluids-inspired macroscopic state (or macrostate) description of the swarm. This work will bridge the gap by seeking to answer the following question: is it possible to generate a small set of thermodynamics-inspired macroscopic properties that may later be used to quantify all possible collective behaviors of swarm systems? In this paper, we present three macroscopic properties analogous to pressure, temperature, and density of a gas to describe the behavior of a swarm that is governed by only attractive and repulsive agent interactions. These properties are made to satisfy an equation similar to the ideal gas law and also generalized to satisfy the virial equation of state for real gases. Finally, we investigate how swarm specifications such as density and average agent velocity affect the system macrostate.

Design and implementation of a maxi-sized mobile robot (Karo) for rescue missions

“…However, these sets of specifications provide a comprehensive Fig. 17 This figure illustrates a merged 2D map by Karo and another complementary robot in the mission which exemplifies a collective behavior [47,48]. In this map, the blue lines represent the obstacles where blue and red spots stand for decoded QR codes using image caption generation method [49] and the detected victims, respectively.…”

“…This has been done by finding transformation between two maps, generated by two different robots, and merging them accordingly. [45,46]. In this map, the blue lines represent the obstacles where blue and red spots stand for decoded QR codes using image caption generation method [47] and the detected victims, respectively.…”

Swarm analytics: Designing information markers to characterise swarm systems in shepherding contexts

“…Physics-based approaches are distinct from other methods in that they treat the swarm as a continuous collective, in contrast to techniques discussed that consider the swarm as an aggregate of individual agents. Haeri et al, (2020) employ a thermodynamics approach to assess collective behaviour, using the context of fluid flow to define macroscopic swarm states (Haeri et al, 2020). Such approaches are aimed to enable more accessible state information representation and classification of emergent behaviours, especially for unknown swarms (Haeri et al, 2020).…”

Exploration and Coordination of Complementary Multirobot Teams in a Hunter-and-Gatherer Scenario

“…In lines 5 and 9, the agent utilizes (3) and ( 5) respectively to calculate EGs. Further, the hunter uses (4) and (6) to choose a frontier with highest value of EG in lines 7 and 9, respectively. e frontier selection function explained is Algorithm 1 needs to be invoked in the hunter's main algorithm.…”

“…Multirobot systems are expected to complete tasks that are infeasible, laborious, or inefficient for a single agent to accomplish [1]. Employing multirobot systems entails addressing various problems on the subjects of task allocation [2], exploration [3], coordination [4], learning [5], swarm behavior [6,7], and heterogeneity [8]. Among all of these problems, the problem of multirobot task allocation (MRTA), that is assigning a group of tasks to individual robots, is the most deep-seated problem where its complexity increases considerably in dynamic environments.…”