In this study, the challenge of teleoperating robots in harsh environments such as underwater or in tunnels is addressed. In these environments, wireless communication networks are prone to congestion, leading to potential mission failures. Our approach integrates a Human–Robot Interface (HRI) with a network congestion control algorithm at the application level for conservative transmission of images using the Robot Operating System (ROS) framework. The system was designed to avoid network congestion by adjusting the image compression parameters and the transmission rate depending on the real-time network conditions. To evaluate its performance, the algorithm was tested in two wireless underwater use cases: pipe inspection and an intervention task. An Autonomous Underwater Vehicle for Intervention (I-AUV) equipped with a Visual Light Communication (VLC) modem was used. Characterization of the VLC network was performed while the robot performed trajectories in the tank. The results demonstrate that our approach allows an operator to perform wireless missions where teleoperation requires images and the network conditions are variable. This solution provides a robust framework for image transmission and network control in the application layer, which allows for integration with any ROS-based system.

There is a wide variety of motion planning techniques for robots; however, there are few tools to test and compare the performance of motion planners in various environments. This work proposes a test layout for a set of industrial robots to measure their performance in manipulation in a constrained environment. The developed work uses ROS and the benchmarking tool Robowflex. The comparison allowed to obtain the performance results of multiple motion planners in a shorter time than in previous works. These results facilitate the correct choice of a motion planner, which is critical in current industrial environments. The conducted benchmark provides a valuable tool for potential comparisons of new motion strategies to proven and established methods.

Reinforcement learning (RL) has become an interesting topic in robotics applications as it can solve complex problems in specific scenarios. The small amount of RL-tools focused on robotics, plus the lack of features such as easy transfer of simulated environments to real hardware, are obstacles to the widespread use of RL in robotic applications. FRobs_RL is a Python library that aims to facilitate the implementation, testing, and deployment of RL algorithms in intelligent robotic applications using robot operating system (ROS), Gazebo, and OpenAI Gym. FRobs_RL provides an Application Programming Interface (API) to simplify the creation of RL environments, where users can import a wide variety of robot models as well as different simulated environments. With the FRobs_RL library, users do not need to be experts in ROS, Gym, or Gazebo to create a realistic RL application. Using the library, we created and tested two environments containing common robotic tasks; one is a reacher task using a robotic manipulator, and the other is a mapless navigation task using a mobile robot. The library is available in GitHub ¹ .