Industries are faced with the choice of suitable process control policies to improve costs, quality and raw material consumption. In the paper pulp industry, it is important to estimate quickly the Chemical Oxygen Demand (COD), a parameter that is highly correlated to product quality. Soft Sensors (SSs) have been established as alternative to hardware sensors and laboratory measurements for monitoring and control purposes. However, in real setups it is often difficult to get sufficient data for SS development. This work proposes Ensemble Methods (EMs) as a way to improve the SS performance for small datasets. EMs use a set of models to obtain better prediction. Their success is usually attributed to the diversity. Bootstrap and noise injection are used to produce diverse models. Several combinations of EMs are compared. The SS is successfully applied to estimate COD in a pulp process.

This paper presents a novel robot swarming navigation algorithm in order to find the odor sources in an unknown environment, based on the ability of each swarm member to sense the odor. Each robot in the swarm has a cooperative localization system which uses wireless network as a mean of measuring the distance from the other robots. In this method, at least three robots act as stationary measurement beacons while the other robots of the swarm navigate in the environment towards the odor source. In the next step, the roles of the robots will be switched and some other robots will act as beacons. The experimental tests report a good result in finding the odor source and also the accuracy of localization system.

This paper describes a Player/Stage simulation framework for mobile robot olfactory experiments. The PlumeSim framework simulates odor transport in the environment, as well as olfactory detection systems. The odor transport can be based on analytical theoretical models, data generated by dedicated computational fluid dynamics (CFD) software packages, or data acquired in a real environment. The framework is validated through comparison of the results of simulated and real mobile robot olfactory experiments.

In this article the main objective is to perform a search in an unknown area with multiple robots in order to determine the region with highest chemical gas concentration as well as to build the chemical gas concentration map. The searching and map building tasks are accomplished by using mobile robots equipped with smart transducers for gas sensing. Robots perform the search autonomously by using their own data and the information (position information and sensor readings) obtained from the other robots. Moreover, simultaneously the robots send their sensor readings of the chemical concentration and their position data to a remote computer (a base station), where the data is combined, interpolated, and filtered to form an real-time map of the chemical gas concentration in the environment. To achieve this task as a high-level path planning algorithm we use a decentralized and asynchronous version of the Particle Swarm Optimization (PSO) algorithm which also allows for time-varying neighborhood.

In this article we present implementation of Bacterial Foraging Optimization algorithm inspired search by multiple robots in an unknown area in order to find the region with highest chemical gas concentration as well as to build the chemical gas concentration map. The searching and map building tasks are accomplished by using mobile robots equipped with smart transducers for gas sensing called “KheNose”. Robots perform the search autonomously via bacterial chemotactic behavior. Moreover, simultaneously the robots send their sensor readings of the chemical concentration and their position data to a remote computer (a base station), where the data is combined, interpolated, and filtered to form an real-time map of the chemical gas concentration in the environment.

This paper presents new advances in the area of chemical trail following with mobile robots. For that purpose, the properties of chemical volatiles and their evaporation rates were tested in order to discover an appropriate candidate to mark chemical trails and at the same time with ability to be detected by the robot olfactory system along the time. The capability of chemical detection was provide by a metal oxide-based olfactory system, mounted in a Khepera III mobile robot. An algorithm to search and track chemical trails is proposed. This algorithm was validated by simulations and by laboratory tests with a Khepera III mobile robot.

Mobile robotics is a challenging research area, with produced results that were unthinkable several years ago. There exist algorithms and methods capable of performing difficult tasks such as detect/classify objects, skill learning and SLAM. From the initial design steps, the real-time software architecture of a robotic platform requires great attention. The problem is difficult, because various components, such as sensing, perception, localization, and motor control, are required to operate and interact in real-time. This makes the system a very complex one. This paper presents a real-time control architecture designed for mobile robots and intelligent vehicles. Moreover, an example of application of the control structure consisting on a system for learning to classify places, using laser range data, is reported.