Simulation is frequently used in the study of multi-agent systems. Unfortunately, in many cases, it is not necessarily clear how faithfully the details of the simulated model represent the behavior of the physical system. Often, the effects of the environment in which the system is to be placed are even neglected entirely. Taking into account theentiresystem (including interactions with the target environment), establishing a clear hierarchy amongmultiplelevels of modeling not only enhances the fidelity of the individual models, but also emphasizes the tradeoffs inherent in each. Understanding and leveraging the full spectrum of models allows the use of fast, high-level models for exploration in the parameter space, the results of which can be verified on more precise low-level models. Here, we demonstrate the generation of a family of models for a robotic wireless sensor network engaged in an acoustic detection task. Quantitative correspondence is shown between modeling levels and with the physical system.

This article considers the problem of robustly emulating a shared atomic memory over a distributed message-passing system where processes can fail by crashing and possibly recover. We revisit the notion of atomicity in the crash-recovery context and introduce a generic algorithm that emulates an atomic memory. The algorithm is instantiated for various settings according to whether processes have access to local stable storage, and whether, in every execution of the algorithm, a sufficient number of processes are assumed not to crash. We establish the optimality of specific instances of our algorithm in terms of resilience , log complexity (number of stable storage accesses needed in every read or write operation), as well as time complexity (number of communication steps needed in every read or write operation). The article also discusses the impact of considering a multiwriter versus a single-writer memory, as well as the impact of weakening the consistency of the memory by providing safe or regular semantics instead of atomicity.

Designing effective behavioral controllers for mobile robots can be difficult and tedious; this process can be circumvented by using unsupervised learning techniques which allow robots to evolve their own controllers in an automated fashion. In multi-robot systems, robots learning in parallel can share information to dramatically increase the evolutionary rate. However, manufacturing variations in robotic sensors may result in perceptual differences between robots, which could impact the learning process. In this paper, we explore how varying sensor offsets and scaling factors affects parallel swarm-robotic learning of obstacle avoidance behavior using both Genetic Algorithms and Particle Swarm Optimization. We also observe the diversity of robotic controllers throughout the learning process in an attempt to better understand the evolutionary process.

Designing algorithms for multi-robot systems can be a complex and difficult process: the cost of such systems can be very high, collecting experimental data can be time-consuming, and individual robots may malfunction, invalidating experiments. These constraints make it very tempting to work using high-level abstractions of the robots and their environment. While these high-level models can be useful for initial design, it is important to verify techniques in more realistic scenarios that include real-world effects that may have been ignored in the abstractions. In this paper, we take a simple, coordinated, multi-robot search algorithm and illustrate problems that it encounters in environments which incorporate real-world factors, such as probabilistic target detection and positional noise. We compare the performance to that of several simple randomized approaches, which are better able to deal with these constraints.

Within the field of multi-robot systems, multi-robot search is one area which is currently receiving a lot of research attention. One major challenge within this area is to design effective algorithms that allow a team of robots to work together to find their targets. Techniques have been adopted for multi-robot search from the particle swarm optimization algorithm, which uses a virtual multi-agent search to find optima in a multi-dimensional function space. We present here a multi-search algorithm inspired by particle swarm optimization. Additionally, we exploit this inspiration by modifying the particle swarm optimization algorithm to mimic the multi-robot search process, thereby allowing us to model at an abstracted level the effects of changing aspects and parameters of the system such as number of robots and communication range

We characterize and improve an existing infrared relative localization/communication module used to find range and bearing between robots in small-scale multi-robot systems. Modifications to the algorithms of the original system are suggested which offer better performance. A mathematical model which accurately describes the system is presented and allows us to predict the performance of modules with augmented sensorial capabilities. Finally, the usefulness of the module is demonstrated in a multi-robot self-localization task using both a realistic robotic simulator and real robots, and the performance is analyzed

We explore using particle swarm optimization on problems with noisy performance evaluation, focusing on unsupervised robotic learning. We adapt a technique of overcoming noise used in genetic algorithms for use with particle swarm optimization, and evaluate the performance of both the original algorithm and the noise-resistant method for several numerical problems with added noise, as well as unsupervised learning of obstacle avoidance using one or more robots.