Odour Source Localisation (OSL) is the process of finding the origin of releasing events by tracking chemical cues dispersed throughout the environment. Cognitive search strategies performed by mobile agents are reliable in turbulent environments where measurements are sparse and intermittent. These methods estimate a belief over the source location using Bayesian inference and guide movement by evaluating expected entropy reduction at admissible locations, enabling informed decisions instead of reacting to direct sensor readings. However, the computational cost of estimating information gains is often prohibitive for real-time use with resource-constrained agents. Interestingly, search trajectories generated by cognitive methods often resemble those of small insects. Given that these organisms achieve effective search despite limited processing capabilities, analysing cognitive decisions could enable the synthesis of efficient bio-inspired behaviours. This work investigates this possibility by analysing trajectories from cognitive OSL algorithms such as Infotaxis, aiming to identify informative movement patterns and search behaviours. Results from 600 simulation experiments across three environmental scenarios (stable flow, moderate turbulence and high turbulence) show that motion pattern direction and length can be systematically related to the agent’s belief state and plume geometry. Markov chain analysis reveals context-dependent behavioural adaptation and the spontaneous emergence of behaviours similar to bio-inspired motions. These findings support the feasibility of synthesizing reliable and computationally efficient hybrid search strategies. • Informative motion patterns and search behaviours emerge from cognitive decisions • Cognitive OSL decisions can be decomposed into parametric belief-state functions • Movement angle correlates with belief uncertainty, step length with plume width • Markov chains model adaptive behavioural transitions • Bio-inspired behaviours arise naturally from information-theoretic decisions

Locating odour sources with mobile robots is a difficult task with many applications. Over the years, researchers have devised bio-inspired and cognitive methods to enable mobile robots to fulfil this task. Cognitive approaches are effective in large spaces, but computationally heavy. On the other hand, bio-inspired ones are lightweight, but they are only effective in the presence of frequent stimuli. One of the most popular cognitive approaches is Infotaxis, which iteratively computes a probability map of the source location. Another strand of work uses Genetic Programming to produce complete search strategies from bio-inspired behaviours. This work combines the two approaches by allowing Genetic Programming to evolve search strategies that include infotactic and bio-inspired behaviours. The proposed method is tested in a set of environments with distinct airflow and chemical dispersion patterns. Its performance is compared to that of evolved strategies without infotactic behaviours and to the standard infotaxis approach. The statistically validated results show that the proposed method produces search strategies that have significantly higher success rates, whilst being faster than those produced by any of the original approaches. Moreover, the best evolved strategies are analysed, providing insight into when infotaxis is more beneficial.

Locating odour sources with mobile robots is a difficult task with many real world applications. Over the years, researchers have devised bio-inspired and cognitive methods to enable mobile robots to fulfil this task. One of the most popular cognitive approaches is Infotaxis, which computes a probability map for the location of the chemical source and, on each time step, moves the robot in the direction that minimises the entropy of that probability map. The main difficulty for applying Infotaxis in the real world is selecting proper values for the parameters of its internal gas dispersion model, as it has been shown that its performance is greatly influenced by the accuracy of said model. This work proposes a Genetic Algorithm for optimising those parameters for specific environments. The proposed method is applied to environments with distinct wind and odour dispersion characteristics and the resulting parameters are compared. Moreover, the performance of Infotaxis is compared to that of reactive search strategies evolved by Geometric Syntactic Genetic Programming. The statistically validated results show that the evolved reactive strategies achieve equivalent success rates to Infotaxis, while being significantly faster. Real world experiments conducted in a controlled wind tunnel validated the simulation results.

Coverage performance in a coverage path planning problem depends both on the path created and on the footprint of the sensor used. The footprint can be increased either by increasing the size of the sensor, or by mounting the sensor on a robotic arm to allow scanning over larger areas as the platform moves, effectively creating a virtual sensor with a larger footprint than the physical sensor's. However, the virtual footprint comes at a cost requiring formulating an optimization problem for the area of interest. In this work, three common strategies to use a metal detector on a platform are discussed, their time and energy performances are formulated and the corresponding optima are found.

The power consumed by skid-steered mobile robots varies enormously depending on their operating regimes and environments. Therefore, energy optimal planning of field missions can be accomplished only if the operating environment and an accurate model of the robot's power consumption are known in advance. This paper studies influences of location of the center of mass, surface type, terrain relief, platform geometry, radius of curvature, speed, and temperature on the power consumption of a skid-steered mobile platform and derives a physics-based power model that uses friction to accurately model the power consumed for the most relevant parameters inside a wide range of operating conditions. This paper also shows how friction can be measured using power consumed by the motors and how motor constants can be measured in a noninvasive end-to-end manner. Our findings were validated with a Clearpath's Husky A200 platform, driving it indoors and outdoors on different surfaces, varying the speed, the radius of curvature, the mass, and the center of mass location, resulting in a dense dataset on which the derived model as well as the temperature dependence of the power consumption was verified.

Metal detectors are widely used to detect and localize land mines, as well as metallic clutter that causes many false alarms. These false alarms are handled by manual inspection with a prodder or using extra features like size and depth as well as fusion with other sensors like ground penetrating radar or chemical sensors. Directly shape itself has never been used, mainly due to infeasibility of the results of the few existing studies, which require controlled environments and dense sampling of the target objects. In this paper, we propose a new method for shape reconstruction that works with sparse data, and show its feasibility using field data collected by a mobile robot equipped with a commercial pulse induction metal detector carried by a custom 2DoF arm. This paper also describes the method employed to sweep natural terrains with the mobile manipulator and provides results of imaging a set of different metallic objects.

Aiming at increasing team efficiency, mobile robots may act as a node of a Robotic Cluster to assist their teammates in computationally demanding tasks. Having this in mind, we propose two distributed architectures for the Simultaneous Localization And Mapping (SLAM) problem, our main case study. The analysis focuses especially on the efficiency gain that can be obtained. It is shown that the proposed architectures enable us to raise the workload up to values that would not be possible in a single robot solution, thus gaining in localization precision and map accuracy. Furthermore, we assess the impact of network bandwidth. All the results are extracted from frequently used SLAM datasets available in the robotics community and a real world testbed is described to show the potential of using the proposed philosophy.

This paper tackles the problem of multi-robot odor distribution mapping through time series analysis. Considering the conditions of real world environments where the chemical concentration distribution is patchy, intermittent and time-variant, we propose a method to incorporate the temporal and spatial aspect of sensory data into the problem of odor distribution mapping. Despite the previous works in this field, the method gives more importance to the recent acquired measurements and also to the measurements which have been spatially closer to the place of the sensors (at the time of their acquisition). Real experiments were done in a realistic small-scale controlled environment (designed for systematic olfactory tests), considering up to five real robots and two different navigation algorithms. Experiments show that the generated odor maps are remarkably more accurate than the results of the conventional spatial interpolation method. Studying various spatio-temporal neighborhoods in the time series analysis concluded that a proper definition of the neighborhood (in time and space) provides accurate results in gas distribution mapping.

In this work we explore multiprocessor computer architectures to propose an effective method for solving the Simultaneous Localization and Mapping Problem. The proposed method makes use of multithreading to parallelize a Rao-Blackwellized Particle Filter approach. By applying the method in common computers found in robots, it is shown that a significant gain in efficiency can be obtained. Furthermore, the parallel method enables us to raise the number of particles up to values that would not be possible in a single threaded solution, thus gaining in localization precision and map accuracy. In order to analyze SLAM results, frequently used datasets by the robotics community were used, and a benchmarking metric was applied.

This paper aims to analyze the number of actuators as well as the actuation strategy for an underactuated prosthetic hands. Two comprehensive analysis were made for this purpose. 16 possible actuation strategies in five categories of one to five actuators were defined. Based on these 16 strategies two analysis were performed: grasp diversity and grasp functionality. For the first analysis we defined a performance metric based on all possible grasps by the human hand, while in the second analysis only the top grasps with the highest frequency of usage were considered. By comparing the performance of these strategies we obtained some interesting results regarding the best actuation strategies for the underactuated anthropomorphic hands.

This paper presents an analytical approach to the problem of odor plume finding by a network of swarm robotic gas sensors, and finds an optimal configuration for them, given a set of assumptions. Considering cross-wind movement for the swarm, we found that the best spatial formation of robots in finding odor plumes is diagonal line configuration with equal distance between each pair of neighboring robots. We show that the distance between neighboring pairs in the line topology depends mainly on the wind speed and the environmental conditions, whereas, the number of robots and the swarm's crosswind movement distance do not show significant impact on optimal configurations. These solutions were analyzed and verified by simulations and experimentally validated in a reduced scale realistic environment using a set of mobile robots.

This paper introduces, OmniClimber II, an evolution of OmniClimber I, a novel climbing robot with high maneuverability for inspection of ferromagnetic 3D human made structures. The robot takes advantage of novelties which allows an excellent maneuverability on the structure and can adapt to flat and non-flat structures. In this paper we present the conceptual and detailed design of the robot, the implementation and improvements over the first version and a set of tests: laboratory tests on a flat surface; and a set of field tests performed on a wind turbine foundation and another field test on a curved steel structure in Mechanical Engineering, to prove the applicability of the robot on both structures.

This work presents a methodology and practical implementation of sensor fusion for land-mine detection using a novel multi-sensor hand-held device composed by a triple coil metal detector and a gas sensor. The proposed approach consists on merging data from both sensors in order to reduce the false alarm rate, particularly by using odor information. A Bayesian approach is proposed for the sensor fusion. Results show a false alarm rate of 1.4 to 1, a mine detection rate of 100% and a mine localization mean absolute error of 3 cm. Furthermore the resulting mine presence probability distribution maps represent an important visualization tool for mine clearance hand-held device users.

This article presents a novel algorithm for multiple odor source localization by a multi-robot system based on a virtual cancelation plume approach. The proposed method is based on rendering a previously declared odor source invisible to the robots so that the declared source and the odor plume it generates do not interfere with the effects of other existing plumes, allowing the localization of the remaining sources. Exploration and plume tracking by the robots is achieved using a decentralized asynchronous particle swarm optimization algorithm. The divergence operator is used to declare the odor sources. A set of simulations and real world experiments are performed on two different scenarios on a controlled environment using a swarm of 5 robots to validate the proposed methodology. Results show that the virtual plume cancelation algorithm can be successfully used to find multiple odor sources, even when two plumes overlap. It can also extend the operation of many odor source localization algorithms developed for single source localization.

This paper introduces a novel grasping mechanism that combines caging and force closure approaches in order to grasp an object. The main advantage of the gripper is its adaptability to various object shapes and sizes with low DOF. The two DOF gripper takes advantage of two tendon driven trunks. Inspired from the continuum manipulator concept, the trunks do not include discrete joints and rather an elastomer plays the role of the joints. The tendon driven trunks in the first phase of grasping act as rigid links and in the second phase act as flexible mechanisms. Two prototype of the gripper are developed, tested and evaluated. The underactuated prototypes showed a very good adaptability to different object shapes and sizes.

This paper describes design and development of omnidirectional magnetic climbing robots with high maneuverability for inspection of ferromagnetic 3D human made structures. The main focus of this article is design, analysis and implementation of magnetic omnidirectional wheels for climbing robots. We discuss the effect of the associated problems of such wheels, e.g. vibration, on climbing robots. This paper also describes the evolution of magnetic omnidirectional wheels throughout the design and development of several solutions, resulting in lighter and smaller wheels which have less vibration and adapt better to smaller radius structures. These wheels are installed on a chassis which adapts passively to flat and curved structures, enabling the robot to climb and navigate on such structures.

This paper introduces a novel climbing robot with high maneuverability for inspection of ferromagnetic 3D human made structures. The robot takes advantage of novelties which allows an excellent maneuverability on the structure and can adapt to flat and non-flat structures. In this paper we present the conceptual and detailed design of the robot, the implementation and two tests: laboratory tests on a flat surface; and a set of field tests performed on a wind turbine foundation to prove the applicability of the robot on both structures.

This paper proposes a method based on cooperation between climbing and ground robots in order to address the mapping problem for autonomous inspection of 3D structures. In the proposed method ground robots act as a mobile observer with a wide coverage for a climbing robot, to detect and estimate the size of the structure being climbed. We will present a case study in which multiple terrain robots provide the model of a structure which should be explored by a pole climbing robot. Each terrain robot is equipped with a low cost wide angle VGA camera, and some markers are fixed on the climbing robot. At each navigation step, the climbing robot and terrain robots cooperate to model a part of the structure which should be explored by the climbing robot.

There are many different forms of human-robot interactions, allowing a team of humans and robots to take advantage of skills of each team member. A developing area of research is focused upon the potential of robot swarms working in emergency settings. In particular we consider a swarm of robots that are capable of supporting and enhancing fire fighting operations co-operatively. This paper outlines some of the key characteristics of this emergency setting, robot swarms within it and the work conducted to develop effective human-robot interaction.

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.

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 flame's 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.

Fine manipulation of large industrial manipulators faces many problems due to well known error sources. Most climbing robots can be seen as mobile manipulators whose base is also moving across the climbing structure, and consequently adds some additional positioning errors. 3DCLIMBER is a serial mechanism pole climbing robot, developed at ISR-UC. The preliminary tests of the robot showed that it is particularly important to position the grippers precisely in the appropriate pose before grasping the structure. Otherwise the accumulating error will impair autonomous climbing process by forcing the operator to stop the operation after a couple of steps in order to calibrate the robot. This paper describes a self calibrating method proposed to measure and compensate these errors.

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.

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.

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.

This paper proposes a multi-stage approach for landmine detection. It is based on a feature-level data fusion which combines data from several nonspecific sensors. The sensor fusion process is analysed and divided in three stages in order to improve the final result. During the first stage all suspected objects are detected against a background. Then, the detected objects are classified to be a man-made or natural object. And, finally, the landmines are distinguished among the identified manmade objects. The last two stages are described in detail in this paper, demonstrating the advantages for their separation. Classification features, which enable the sensor fusion, are also presented in this work together with an approach for their integration. The proposed ideas are tested using real experimental data obtained from pulsed and continuous metal detectors, infrared camera and ground penetrating radar

To enable automated landmine detection a number of practical problems must be overcome. One of them is the mapping of data from landmine detection sensors using a mobile demining robot in an unstructured outdoor environment. The odometry of the robot used for mapping can be improved by using an additional vision system. It is proposed in this paper to utilize natural landmarks which can be found on the ground for this purpose. New simple algorithms for detection and association of natural landmarks are developed and described. Finally, the experimental results of applying these algorithms on a set of images obtained from a camera mounted on the robot are presented. The results show their robustness for different environmental conditions and robot motions

Humanitarian demining is a dangerous and time consuming task which leads to more victims among deminers. Thus large efforts are being made worldwide in order to develop autonomous robots able to detect landmines without human participation. Any single sensor currently available for antipersonnel landmine detection can not provide the required detection/false alarm ratio, thus a combination of several sensors together with sensor fusion techniques is used. One of the main problems in the practical implementation of sensor fusion for a demining robot is the lack of experimental data for training the algorithms. Formally using a small training and evaluation set does not allow to take conclusions about the results of sensor fusion. This work presents a sensor fusion strategy which helps to overcome the problem of having few experimental data by creating an unified landmine signatures database. Using this database for sensor fusion in general and for features selection in particular is described. In addition to commonly used statistical features several new features are proposed which reflect the shape and the nature of the object. Moreover the designed ROIs extraction algorithm provides additional information allowing to improve features extraction. The results of features selection using a combination of Mutual information and Hausdorff distance are presented. Preliminary results of sensor fusion for pulsed and continuous metal detectors are also presented.

This paper describes an architecture for estimating environmental odor maps and presents experimental results obtained using that architecture with five mobile robots inside a large laboratory with two odor sources and forced ventilation. The mobile sensing agents employed in the experiments have self localization capabilities and carry an electronic nose and a thermal anemometer. The proposed architecture allows integrating sparse olfaction data gathered by the mobile agents along their trajectories and dynamically estimate the spatial concentration of different odor fields. The data assimilation process is made centrally by a PC that polls periodically each robot through a RF network in order to get the data gathered during the previous acquisition period. The estimation of odor fields is made in two steps: first each agent estimates the odor mixture and concentration by means of a neural network-based regression algorithm that converts values from gas sensor space to the corresponding odor space. Then the sensed data is assimilated into an advection-diffusion model by means of a reduced order Kalman filter. In the current implementation the central controller, responsible for the maps estimation, specifies to each agent their target area to explore. The proposed architecture was validated with a set of experiments that demonstrated its ability to estimate and capture the dynamics of overlapping odor fields. This work can easily be adapted to city buses or other local transportation systems in order to monitor the pollution or quickly detect hazardous chemicals inside cities.

This paper presents a pre-processing method to allow the implementation of landmine sensor fusion techniques in a pneumatic demining robot. The proposed method is based on a two-level strategy for the sensor fusion. This strategy allows separating two different classification tasks: object/background and mine/another object. This work is focused in the first stage of the process, particularly in the identification of regions-of-interest (ROI). The proposed ROI extraction algorithm decreases the number of parameters, is reliable in different environmental conditions and can be implemented online. Results of experimental implementation and conclusions are presented.

The enormous amount of anti-personal land mines scattered over many countries worldwide is a major humanitarian problem asking for an efficient solution at an affordable cost. The article describes a robust and low cost mobile robot that can transport equipment of about 1.5 times its own weight over natural terrains with inclinations superior to 40 degrees. The robot mechanical structure is optimised for precisely scanning large areas of natural environments with land mine detection sensors. The paper also describes the control architecture of the robot with the possibility to correct the motion direction. The land mine sensing system of the robot is presented.