Bacterium-inspired Robots for Environmental Monitoring

Locating gradient sources and tracking them over time has important applications to environmental monitoring and studies of the ecosystem. We present an approach, inspired by bacterial chemotaxis, for robots to navigate to sources using gradient measurements and a simple actuation strategy (biasing a random walk). Extensive simulations show the efficacy of the approach in varied conditions including multiple sources, dissipative sources, and noisy sensors and actuators. We also show how such an approach could be used for boundary finding. We validate our approach by testing it on a small robot (the robomote) in a phototaxis experiment. A comparison of our approach with gradient descent shows that while gradient descent is faster, our approach is better suited for boundary coverage, and performs better in the presence of multiple and dissipative sources

Improving Bacteria Controller Efficiency

We present a novel approach that would enable the placement of dynamic sensor platforms in the most optimal areas for data collection in an environment of any size. Our approach would ensure that more sensors are placed in areas that contain interesting data and less in areas with little or no data. In this paper, we use a bacteria controller to navigate the environment in the search of interesting data and show that the addition of a flocking algorithm improves the chances of finding data

Modeling and Mathematical Analysis of Swarms of Microscopic Robots

The biologically-inspired swarm paradigm is being used to design self-organizing systems of locally interacting artificial agents. A major difficulty in designing swarms with desired characteristics is understanding the causal relation between individual agent and collective behaviors. Mathematical analysis of swarm dynamics can address this difficulty to gain insight into system design. This paper proposes a framework for mathematical modeling of swarms of microscopic robots that may one day be useful in medical applications. While such devices do not yet exist, the modeling approach can be helpful in identifying various design trade-offs for the robots and be a useful guide for their eventual fabrication. Specifically, we examine microscopic robots that reside in a fluid, for example, a bloodstream, and are able to detect and respond to different chemicals. We present the general mathematical model of a scenario in which robots locate a chemical source. We solve the scenario in one-dimension and show how results can be used to evaluate certain design decisions.Comment: 2005 IEEE Swarm Intelligence Symposium, Pasadena, CA June 200

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 212

A bibliography listing 146 reports, articles, and other documents introduced into the NASA scientific and technical information system is presented. The subject coverage concentrates on the biological, psychological, and environmental factors involved in atmospheric and interplanetary flight. Related topics such as sanitary problems, pharmacology, toxicology, safety and survival, life support systems, and exobiology are also given attention

Multiple robot co-ordination using particle swarm optimisation and bacteria foraging algorithm

The use of multiple robots to accomplish a task is certainly preferable over the use of specialised individual robots. A major problem with individual specialized robots is the idle-time, which can be reduced by the use of multiple general robots, therefore making the process economical. In case of infrequent tasks, unlike the ones like assembly line, the use of dedicated robots is not cost-effective. In such cases, multiple robots become essential. This work involves path-planning and co-ordination between multiple mobile agents in a static-obstacle environment. Multiple small robots (swarms) can work together to accomplish the designated tasks that are difficult or impossible for a single robot to accomplish. Here Particle Swarm Optimization (PSO) and Bacteria Foraging Algorithm (BFA) have been used for coordination and path-planning of the robots. PSO is used for global path planning of all the robotic agents in the workspace. The calculated paths of the robots are further optimized using a localised BFA optimization technique. The problem considered in this project is coordination of multiple mobile agents in a predefined environment using multiple small mobile robots. This work demonstrates the use of a combinatorial PSO algorithm with a novel local search enhanced by the use of BFA to help in efficient path planning limiting the chances of PSO getting trapped in the local optima. The approach has been simulated on a graphical interface

Data Optimization on Multi Robot Sensing System with RAM Based Neural Network Method

Monitoring the environment activities is an attractive Abstract— Monitoring the environment activities is an attractive thing for development. That is because the human life would affect the surrounding environtment. There\u27s a lot of research of environment has been done, one of those is the changes of air quality in urban areas. To measure the level of air quality, the data and information from field measurements and laboratory analysis result was needed. This paper review the research result that focus on sensor data processing in multi robot using RAM based neural network. There are 11 pattern input data were processed by temperature data optimization from 250C until 350C, humadity data from 20% until 60% and gas data from 350ppm until 450ppm. The obtained result is from 8 bits and 9 bits become 6 bits in certain level with optimazion percentage is25% and 33,3%. This result effect to the computationan load, it\u27s become more simple, the execution time and data communication becomes faster

Distributed Control of Microscopic Robots in Biomedical Applications

Current developments in molecular electronics, motors and chemical sensors could enable constructing large numbers of devices able to sense, compute and act in micron-scale environments. Such microscopic machines, of sizes comparable to bacteria, could simultaneously monitor entire populations of cells individually in vivo. This paper reviews plausible capabilities for microscopic robots and the physical constraints due to operation in fluids at low Reynolds number, diffusion-limited sensing and thermal noise from Brownian motion. Simple distributed controls are then presented in the context of prototypical biomedical tasks, which require control decisions on millisecond time scales. The resulting behaviors illustrate trade-offs among speed, accuracy and resource use. A specific example is monitoring for patterns of chemicals in a flowing fluid released at chemically distinctive sites. Information collected from a large number of such devices allows estimating properties of cell-sized chemical sources in a macroscopic volume. The microscopic devices moving with the fluid flow in small blood vessels can detect chemicals released by tissues in response to localized injury or infection. We find the devices can readily discriminate a single cell-sized chemical source from the background chemical concentration, providing high-resolution sensing in both time and space. By contrast, such a source would be difficult to distinguish from background when diluted throughout the blood volume as obtained with a blood sample