The ability of robots to promptly avoid obstacles and reach a goal while accurately maintaining a formation is extremely important for many swarm robotic tasks such as terrain analysis, search and rescue, and chemical plume source tracing. In prior work, using simulation, we established how Physicomimetics can be used to maintain swarms of mobile robots in formations that move towards a goal through an obstacle field. In this paper, we demonstrate the obstacle avoidance capability of Physicomimetics control algorithm using two different mobile robot platforms with limited hardware, Maxelbot and X80Pro.

This paper describes an approach to using evolutionary algorithms for reasoning about paths through network data. The paths investigated in the context of this research are functional paths wherein the characteristics (e.g., path length, morphology, location) of the path are integral to the objective purpose of the path. Using two datasets of combined surface and road networks, the research demonstrates how an evolutionary algorithm can be used to reason about functional paths. We present the algorithm approach, the parameters and fitness function that drive the functional aspects of the path, and an approach for using the algorithm to respond to dynamic changes in the search space. The results of the search process are presented in terms of the overall success based on the response of the search to variations in the environment and through the use of an occupancy grid characterizing the overall search process. The approach offers a great deal of flexibility over more conventional heuristic path finding approaches and offers additional perspective on dynamic network analysis.

This book introduces the term "physicomimetics" to refer to physics-based swarm approaches. It consolidates over a decade of work on swarm intelligence and swarm robotics.

In order to better understand the physics-based swarms examined in this book, we will use a special simulation tool referred to as NetLogo. NetLogo is designed specifically to graphically demonstrate how swarms behave. These simulations can be run directly on your computer or through your browser. This chapter provides an introduction to NetLogo and to physics. By working with simulations of a spring and a small solar system, you will be shown how to use NetLogo. For those who are adventurous, we will also show you how to modify the actual code so that you can further explore concepts that interest you. While doing this, you will also learn important physics concepts such as the center of mass, kinetic energy, potential energy and conservation laws. Unlike traditional textbooks on physics, however, you will learn these concepts by observing them in action. The result is a deeper level of understanding and an appreciation for how elegant physics truly is.

Abstract : In this article we present a novel algorithm for uniform coverage of a region. Surveillance, cleaning, and mine detection are some applications that would benefit from this type of algorithm. Prior work has claimed that this task is impossible to solve for non-convex regions. Our algorithm enables robots to uniformly cover arbitrary path-connected regions, such that the robot movements are not predictable and the region periphery is not neglected. The algorithm assumes that robots are independent and is physics-based, relying on an analogy with mean free paths of particles. Validation of the algorithm is rigorously provided via simulation and real robot experiments.

The most common versions of particle swarm optimization PSO algorithms are rotationally variant. It has also been pointed out that PSO algorithms can concentrate particles along paths parallel to the coordinate axes. In this paper, the authors explicitly connect these two observations by showing that the rotational variance is related to the concentration along lines parallel to the coordinate axes. Based on this explicit connection, the authors create fitness functions that are easy or hard for PSO to solve, depending on the rotation of the function.

We present a framework for modeling the spread of pathogens throughout a population and generating policies that minimize the impact of those pathogens on the population. This framework is used to study the spread of human viruses between cities via airplane travel. It combines agent-based simulation, mathematical analysis, and an Evolutionary Algorithm (EA) optimizer. The goal of this study is to develop tools that determine the optimal distribution of a vaccine supply in the model. Using plausible benchmark vaccine allocation policies of uniform and proportional distribution, we compared their effectiveness to policies found by the EA. We then designed and tested a new, more effective policy which increased the importance of vaccinating smaller cities that are flown to more often. This "importance factor" was validated using U.S. influenza data from the last four years.

Despite decades of work in evolutionary algorithms, there remains an uncertainty as to the relative benefits and detriments of using recombination or mutation. This book provides a characterization of the roles that recombination and mutation play in evolutionary algorithms. It integrates important prior work and introduces new theoretical techniques for studying evolutionary algorithms. Consequences of the theory are explored and a novel method for comparing search and optimization algorithms is introduced. The focus allows the book to bridge multiple communities, including evolutionary biologists and population geneticists.

One important task in cooperative robotics is the self–organization of robotic chain formations in unknown environments. Surveillance and reconnaissance in sewers, ducts, tunnels, caves or narrow passageways, in general, are some of the applications for this type of formation. In Hettiarachchi et al. (2008) we provided simulation results for a novel chain formation algorithm that addresses this task. This paper presents the results of the chain formation algorithm implementation on five robots.

This paper is concerned with assuring the safety of a swarm of agents (simulated robots). Such behavioral assurance is provided with the physics method called kinetic theory. Kinetic theory formulas are used to predict the macroscopic behavior of a simulated swarm of individually controlled agents. Kinetic theory is also the method for controlling the agents. In particular, the agents behave like particles in a moving gas.The coverage task addressed here involves a dynamic search through a bounded region, while avoiding multiple large obstacles, such as buildings. In the case of limited sensors and communication, maintaining spatial coverage - especially after passing the obstacles - is a challenging problem. Our kinetic theory solution simulates a gas-like swarm motion., which provides excellent coverage. Finally, experimental results are presented that determine how well the macroscopic-level theory, mentioned above, predicts simulated swarm behavior on this task.

Purpose The purpose of this paper is to demonstrate a novel use of a generalized Lennard‐Jones (LJ) force law in Physicomimetics , combined with offline evolutionary learning, for the control of swarms of robots moving through obstacle fields towards a goal. The paper then extends the paradigm to demonstrate the utility of a real‐time online adaptive approach named distributed agent evolution with dynamic adaptation to local unexpected scenarios (DAEDALUS). Design/methodology/approach To achieve the best performance, the parameters of the force law used in the Physicomimetics approach are optimized, using an evolutionary algorithm (EA) (offline learning). A weighted fitness function is utilized consisting of three components: a penalty for collisions, lack of swarm cohesion, and robots not reaching the goal. Each robot of the swarm is then given a slightly mutated copy of the optimized force law rule set found with offline learning and the robots are introduced to a more difficult environment. The online learning framework (DAEDALUS) is used for swarm adaptation in this more difficult environment. Findings The novel use of the generalized LJ force law combined with an EA surpasses the prior state‐of‐the‐art in the control of swarms of robots moving through obstacle fields. In addition, the DAEDALUS framework allows the swarms of robots to not only learn and share behavioral rules in changing environments (in real time), but also to learn the proper amount of behavioral exploration that is appropriate. Research limitations/implications There are significant issues that arise with respect to “wall following methods” and “local minimum trap” problems. “Local minimum trap” problems have been observed in this paper, but this issue is not addressed in detail. The intention is to explore other approaches to develop more robust adaptive algorithms for online learning. It is believed that the learning of the proper amount of behavioral exploration can be accelerated. Practical implications In order to provide meaningful comparisons, this paper provides a more complete set of metrics than prior papers in this area. The paper examines the number of collisions between robots and obstacles, the distribution in time of the number of robots that reach the goal, and the connectivity of the formation as it moves. Originality/value This paper addresses the difficult task of moving a large number of robots in formation through a large number of obstacles. The important real‐world constraint of “obstructed perception” is modeled. The obstacle density is approximately three times the norm in the literature. The paper shows how concepts from population genetics can be used with swarms of agents to provide fast online adaptive learning in these challenging environments. In addition, this paper also presents a more complete set of metrics of performance.

The research work presented herein is part of a large effort in R&D of a physics-based approach to develop networks of mobile sensing agents for monitoring, tracking, reporting and responding to hazardous conditions such as those resulting from the release of a WMD. We present the development of an efficient and robust distributed collaborative search algorithm for a team of unmanned robots that must locate the emitter of a toxic plume in an urban setting. We aim to provide a scientific, yet practical, approach to the design and analysis of rapidly deployable, scalable, adaptive, cost-effective groups of autonomous robots to replace or complement humans in the hazardous task of localizing the source of toxic chemical, biological or radiological plumes. In our approach, the robot group coordination and control are based on a physics-based framework called physicomimetics, and the collaborative search algorithm, called fluxotaxis, is based on fluid mechanics.

The ability of robots to quickly and accurately localize their neighbors is extremely important for robotic teams. Prior approaches typically rely either on global information provided by GPS, beacons, and landmarks, or on complex local information provided by vision systems. In this paper we describe our trilateration approach to multi-robot localization, which is fully distributed, inexpensive, scalable, and robust. Our prior research [14] focused on maintaining multi-robot formations indoors using trilateration. This paper pushes the limits of our trilateration technology by testing formations of robots in an uncontrolled outdoor setting with relatively large inter-robot distances and high speeds. Rigorous experimental results demonstrate surprising robustness, as well as the limits

This paper is concerned with assuring the safety of a swarm of agents (simulated robots). Such behavioral assurance is provided with the physics method called kinetic theory. Kinetic theory formulas are used to predict the macroscopic behavior of a simulated swarm of individually controlled agents. Kinetic theory is also the method for controlling the agents. In particular, the agents behave like particles in a moving gas. The coverage task addressed here involves a dynamic search through a bounded region, while avoiding multiple large obstacles, such as buildings. In the case of limited sensors and communication, maintaining spatial coverage – especially after passing the obstacles – is a challenging problem. Our kinetic theory solution simulates a gas-like swarm motion, which provides excellent coverage. Finally, experimental results are presented that determine how well the macroscopic-level theory, mentioned above, predicts simulated swarm behavior on this task.

This paper is concerned with assuring the safety of a swarm of agents (simulated robots). Such behavioral assurance is provided with the physics method called kinetic theory. Kinetic theory formulas are used to predict the macroscopic behavior of a simulated swarm of individually controlled agents. Kinetic theory is also the method for controlling the agents. In particular, the agents behave like particles in a moving gas.