Simulation Platform for the Evaluation of Robotic Swarm Algorithms

One major problem in the development of robotic swarms is the slow process of testing. Testing different algorithms or variations of a single one using physical robots requires reprogramming every robot in the swarm before every run. Hence, the speed at which a robotic swarm can be tested is highly dependent on the time taken to reprogram the entire swarm and the physical speed at which the swarm operates. This paper details the development of a computer-based simulation platform for rapid development and testing of swarm-intelligence algorithms in an effort to mitigate the current bottleneck imposed by testing. The simulator uses an object-oriented programming environment to facilitate the implementation and modification of swarm algorithms. Simulation of food foraging in an ant-hill scenario is used to demonstrate the effectiveness of the simulator

Swarm robotics: Cooperative navigation in unknown environments

Swarm Robotics is garnering attention in the robotics field due to its substantial benefits. It has been proven to outperform most other robotic approaches in many applications such as military, space exploration and disaster search and rescue missions. It is inspired by the behavior of swarms of social insects such as ants and bees. It consists of a number of robots with limited capabilities and restricted local sensing. When deployed, individual robots behave according to local sensing until the emergence of a global behavior where they, as a swarm, can accomplish missions individuals cannot. In this research, we propose a novel exploration and navigation method based on a combination of Probabilistic Finite Sate Machine (PFSM), Robotic Darwinian Particle Swarm Optimization (RDPSO) and Depth First Search (DFS). We use V-REP Simulator to test our approach. We are also implementing our own cost effective swarm robot platform, AntBOT, as a proof of concept for future experimentation. We prove that our proposed method will yield excellent navigation solution in optimal time when compared to methods using either PFSM only or RDPSO only. In fact, our method is proved to produce 40% more success rate along with an exploration speed of 1.4x other methods. After exploration, robots can navigate the environment forming a Mobile Ad-hoc Network (MANET) and using the graph of robots as network nodes

Investigating biocomplexity through the agent-based paradigm.

Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines--or agents--to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex

Programming platform for distributed robotics: primitives and portability

The Stabilizing Robotics Language (StarL) programming framework aims to simplify development of distributed robotic applications by providing programming abstractions and building blocks for communication, motion control and coordination between robots. It has been used to develop applications such as formation control, automatic intersection protocol, and distributed collaborative search. In this thesis, we introduce the programming abstractions as StarL primitives that are platform independent and useful across hardware platforms, resulting in portability. We first introduce the primitives as building blocks to easily develop, simulate and debug distributed robotic applications in StarL. Then, we discuss the design of the StarL framework which enables us to achieve portability of robot programs across hardware platforms. Thus, the same application program, say, for formation control, can now be ported and deployed on multiple, heterogeneous robotic platforms. We evaluate the design of these new features by simulating several applications

Physically Embedded Genetic Algorithm Learning in Multi-Robot Scenarios: The PEGA algorithm

We present experiments in which a group of autonomous mobile robots learn to perform fundamental sensor-motor tasks through a collaborative learning process. Behavioural strategies, i.e. motor responses to sensory stimuli, are encoded by means of genetic strings stored on the individual robots, and adapted through a genetic algorithm (Mitchell, 1998) executed by the entire robot collective: robots communicate their own strings and corresponding fitness to each other, and then execute a genetic algorithm to improve their individual behavioural strategy. The robots acquired three different sensormotor competences, as well as the ability to select one of two, or one of three behaviours depending on context ("behaviour management"). Results show that fitness indeed increases with increasing learning time, and the analysis of the acquired behavioural strategies demonstrates that they are effective in accomplishing the desired task

Bio-inspired distributed sensors to autonomous search of gas leak source

This work presents multiple small robots in an unhealthy industrial environment responsible for detecting harmful gases to humans, avoiding possible harmful effects on the body. Mixed reality is widely used, considering that the environment and gases are virtual and real small robots. Essential components for the experiments are virtual, such as gases and BioCyber-Sensors. The results establish the great potential for applications in several areas, such as industrial, biomedical, and services. The entire system was developed based on ROS (Robot Operating System), thus the ease in diversifying different applications and approaches with multiple agents. The main objective of small robots is to guaranty a healthy work environment.info:eu-repo/semantics/publishedVersio

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 324)

This bibliography lists 200 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during May, 1989. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance