Lino Marques

Olfaction-based mobile robot navigation

It is well known that insects and other animals use olfactory senses in a wide variety of behavioural processes, namely to recognize and locate food sources, detect predators, and find mates. This article discusses the gathering of olfactive information and its utilization by a mobile robot to find a specific odour source in a room with turbulent phenomenas and multiple sources of odour. Three navigation algorithms are compared with a simple gas sensor and with an electronic nose. Their performance in finding an ethanol source in a room with obstacles is evaluated. The first navigation strategy is based on bacteria chemotaxis. The second strategy is based on the male silkworm moth algorithm that is used to search and track a female moth pheromone plume. The last strategy is based on the estimation of odour geometry and gradient tracking. The electronic nose utilized is composed by an array of different and weakly selective metal oxide gas sensors. The odours are identified and quantified by a pattern recognition algorithm based on an artificial neural network. The test bed for the navigation algorithms was a Nomad Super Scout II mobile robot.

Particle swarm-based olfactory guided search

This article presents a new algorithm for searching odour sources across large search spaces with groups of mobile robots. The proposed algorithm is inspired in the particle swarm optimization (PSO) method. In this method, the search space is sampled by dynamic particles that use their knowledge about the previous sampled space and share this knowledge with other neighbour searching particles allowing the emergence of efficient local searching behaviours. In this case, chemical searching cues about the potential existence of upwind odour sources are exchanged. By default, the agents tend to avoid each other, leading to the emergence of exploration behaviours when no chemical cue exists in the neighbourhood. This behaviour improves the global searching performance.The article explains the relevance of searching odour sources with autonomous agents and identifies the main difficulties for solving this problem. A major difficulty is related with the chaotic nature of the odour transport in the atmosphere due to turbulent phenomena. The characteristics of this problem are described in detail and a simulation framework for testing and analysing different odour searching algorithms was constructed. The proposed PSO-based searching algorithm and modified versions of gradient-based searching and biased random walk-based searching strategies were tested in different environmental conditions and the results, showing the effectiveness of the proposed strategy, were analysed and discussed.

Multi-robot exploration and fire searching

Exploration of an unknown environment is a fundamental concern in mobile robotics. This paper presents an approach for cooperative multi-robot exploration, fire searching and mapping in an unknown environment. The proposed approach aims to minimize the overall exploration time, making it possible to localize fire sources in an efficient way. In order to achieve this goal, the robots should cooperate in an effective way, so they can individually and simultaneously explore different areas of the environment while they identify fire sources. The proposed approach employs a decentralized frontier based exploration method which evaluates the cost-gain ratio to navigate to target way-points. The target way-points are obtained by an A* search variant algorithm. The potential field method is used to control the robots motion while avoiding obstacles. When a robot detects a fire, it estimates the flames position by triangulation. The communication between the robots is done in a decentralized control way where they share the necessary data to generate the map of the environment and to perform cooperative actions in a behavioral decision making way. This paper presents simulation and experimental results of the proposed exploration and fire search method and concludes with a discussion of the obtained results and future improvements.

Multi-robot olfactory search in structured environments

This paper presents a cooperative distributed approach for searching odor sources in unknown structured environments with multiple mobile robots. While searching and exploring the environment, the robots independently generate on-line local topological maps and by sharing them with each other they construct a global map. The proposed method is a decentralized frontier based algorithm enhanced by a cost/utility evaluation function that considers the odor concentration and airflow at each frontier. Therefore, frontiers with higher probability of containing an odor source will be searched and explored first. The method also improves path planning of the robots for the exploration process by presenting a priority policy. Since there is no global positioning system and each robot has its own coordinate reference system for its localization, this paper uses topological graph matching techniques for map merging. The proposed method was tested in both simulation and real world environments with different number of robots and different scenarios. The search time, exploration time, complexity of the environment and number of double-visited map nodes were investigated in the tests. The experimental results validate the functionality of the method in different configurations.

OmniClimbers: Omni-directional magnetic wheeled climbing robots for inspection of ferromagnetic structures

This paper introduces Omniclimber, a new climbing robot with high maneuverability for inspection of ferromagnetic flat and convex human made structures. In addition to maneuverability, adaptability to various structures with different curvatures and materials are addressed. The conceptual and detailed design of OmniClimbers are presented and two prototypes of the robot are introduced. Several laboratory and field tests are reported, and the results are discussed.

Multisensor Demining Robot

The paper describes an advanced multisensor demining robot. The robot transport system is based on a simple structure using pneumatic drive elements. The robot has robust design and can carry demining equipment up to 100 kg over rough terrains. Due to the adaptive possibilities of pedipulators to obstacles, the robot can adjust the working position of the demining sensors while searching for mines. The detection block consists of a metal detector, an infrared detector, and a chemical explosive sensor. The robot is controlled by means of an on-board processor and by an operator remote station in an interactive mode. Experimental results of the transport, control, and detection systems of the robot are presented.

An olfactory-based robot swarm navigation method

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 system1.

3DCLIMBER: A climbing robot for inspection of 3D human made structures

3DCLIMBER is a running project in the University of Coimbra for developing a climbing robot with the capability of manipulating over 3D human-made structures. This paper mainly discuss the conceptual and detailed design and development of a Pole Climbing robot with minimum degrees of freedom which can climb over 3D structures with bends and branches followed by Preliminary test results of the robot performance. Electronics architecture and control algorithms are briefly described. The paper finishes with discussion of the current results and identifies some future works.

Assessment of Laser Range Finders in risky environments

This paper characterizes four commercial laser range finders (LRF) while operating under adverse conditions, namely: low visibility, and multiple types of target surfaces, including different optical properties, angles and radiant surfaces. The study considered two scanning LRF commonly used in mobile robotics: the Sick LMS200 and the Hokuyo URG-04LX, and two industrial punctual LRFs: the IFM Efector O1D100 and the Sick DT60. Based on the results obtained, a set of conclusions and recommendations are taken considering the utilization of LRF in mobile robots operating in risky and adverse environments, like firefighting applications.

Electronic nose-based odour source localization

It is well known that insects and other animals use their olfactory sense in a wide variety of behavioural processes, namely to recognize and locate food sources, detect predators, and find mates. This paper discusses the gathering of olfactive information and its utilization by a mobile robot to find a specific odour source in a room with turbulent phenomena and multiple sources of odour. Three navigation algorithms are compared both with a simple gas sensor and with an electronic nose. Their performance in finding an ethanol source in a room with obstacles is evaluated. The first navigation strategy is based on bacterial chemotaxis. The second strategy is based in the silk-worm moth algorithm to search and track a female moth pheromone plume. The last strategy is based on odour map estimation and maximum concentration following. The electronic nose utilized is composed by an array of different and weakly selective metal oxide gas sensors. The odours are identified and quantified by a pattern recognition algorithm based on artificial neural networks (ANN). The test bed for the navigation algorithms was a Nomad Super Scout II mobile robot.