Programming robots to perform complex tasks is a very expensive job. Traditional path planning and control are able to generate point to point collision free trajectories, but when the tasks to be performed are complex, traditional planning and control become complex tasks. This study focused on robotic operations in logistics, specifically, on picking objects in unstructured areas using a mobile manipulator configuration. The mobile manipulator has to be able to place its base in a correct place so the arm is able to plan a trajectory up to an object in a table. A deep reinforcement learning (DRL) approach was selected to solve this type of complex control tasks. Using the arm planner’s feedback, a controller for the robot base is learned, which guides the platform to such a place where the arm is able to plan a trajectory up to the object. In addition the performance of two DRL algorithms ((Deep Deterministic Policy Gradient (DDPG)) and (Proximal Policy Optimisation (PPO)) is compared within the context of a concrete robotic task.

Maintenance tasks are crucial for all kind of industries, especially in extensive industrial plants, like solar thermal power plants. The incorporation of robots is a key issue for automating inspection activities, as it will allow a constant and regular control over the whole plant. This paper presents an autonomous robotic system to perform pipeline inspection for early detection and prevention of leakages in thermal power plants, based on the work developed within the MAINBOT (http://www.mainbot.eu) European project. Based on the information provided by a thermographic camera, the system is able to detect leakages in the collectors and pipelines. Beside the leakage detection algorithms, the system includes a particle filter-based tracking algorithm to keep the target in the field of view of the camera and to avoid the irregularities of the terrain while the robot patrols the plant. The information provided by the particle filter is further used to command a robot arm, which handles the camera and ensures that the target is always within the image. The obtained results show the suitability of the proposed approach, adding a tracking algorithm to improve the performance of the leakage detection system.

In this research, a multi-view photogrammetric model was developed and tested in simulation in order to understand its capabilities for close-range photogrammetric applications. It was based on contour line detection and least squares geometrical fitting of a cylindrical geometry from multiple views. To feed and validate this model, synthetic data were created for several cylinders poses and camera network set-up. The simulation chain comprises three main stages: synthetic image creation, image data processing by means of shape-matching and cylinder pose estimation based on developed photogrammetric model. Beforehand, a priori data was theoretically established according to a common reference for both for intrinsic and extrinsic parameters of the cameras. The preliminary results highlight that the model is suitable for close-range photogrammetry and sensible to a priori known data as well as to image data quality. These results were compared against other validated geometrical methods to assure that the model is truthful. Preliminary results show that the accuracy of the model ranges between 1/1000 and 1/20 000 for multiple poses and cylinder dimensions. Moreover the simulation procedure has been enhanced with a Montecarlo approach to estimate more realistic pose uncertainties considering possible imaging error sources.

MAINBOT project has developed service robots based applications to autonomously execute inspection tasks in extensive industrial plants in equipment that is arranged horizontally (using ground robots) or vertically (climbing robots). The industrial objective has been to provide a means to help measuring several physical parameters in multiple points by autonomous robots, able to navigate and climb structures, handling non-destructive testing sensors. MAINBOT has validated the solutions in two solar thermal plants (cylindrical-parabolic collectors and central tower), that are very demanding from mobile manipulation point of view mainly due to the extension (e.g. a thermal solar plant of 50Mw, with 400 hectares, 400.000 mirrors, 180 km of absorber tubes, 140m height tower), the variability of conditions (outdoor, day-night), safety requirements, etc. Once the technology was validated in simulation, the system was deployed in real setups and different validation tests carried out. In this paper two of the achievements related with the ground mobile inspection system are presented: (1) Autonomous navigation localization and planning algorithms to manage navigation in huge extensions and (2) Non-Destructive Inspection operations: thermography based detection algorithms to provide automatic inspection abilities to the robots