In this article we describe implementations of various bio-inspired algorithms for obtaining the chemical gas concentration map of an environment filled with a contaminant. The experiments are performed using Khepera III and miniQ miniature mobile robots equipped with chemical gas sensors in an environment with ethanol gas. We implement and investigate the performance of decentralized and asynchronous particle swarm optimization (DAPSO), bacterial foraging optimization (BFO), and ant colony optimization (ACO) algorithms. Moreover, we implement sweeping (sequential search algorithm) as a base case for comparison with the implemented algorithms. During the experiments at each step the robots send their sensor readings and position data to a remote computer where the data is combined, filtered, and interpolated to form the chemical concentration map of the environment. The robots also exchange this information among each other and cooperate in the DAPSO and ACO algorithms. The performance of the implemented algorithms is compared in terms of the quality of the maps obtained and success of locating the target gas sources.TÜBİTAK ; European Commissio

This article proposes two methods based on cooperation between climbing and ground robots in order to address the mapping problem for autonomous inspection of three-dimensional (3D) structures. A pole climbing robot was developed to autonomously inspect a 3D human-made structure. The robot is able to climb over 3D human-made structures with bends and T-junctions. In the previous version of the system, the robot operator had to provide a set of data, resembling the map of the 3D structure, to the path planning algorithm of the climbing robot. However, the necessity of a priori knowledge of the structure's geometry reduces the autonomy of the system. In this article we propose two solutions in which the structure in front of the robot is mapped autonomously. In the first 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 Visual Graphics Array (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. We also present a second approach in which a depth image from a Microsoft Kinect (a motion sensing input device by Microsoft) is fused with the information from the camera in order to eliminate the dependency of the system on color and light conditions.

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