A Vision-based Quadrotor Swarm for the participation in the 2013 International Micro Air Vehicle Competition

This paper presents a completely autonomous solution to participate in the 2013 International Micro Air Vehicle Indoor Flight Competition (IMAV2013). Our proposal is a modular multi-robot swarm architecture, based on the Robot Operating System (ROS) software framework, where the only information shared among swarm agents is each robot's position. Each swarm agent consists of an AR Drone 2.0 quadrotor connected to a laptop which runs the software architecture. In order to present a completely visual-based solution the localization problem is simplified by the usage of ArUco visual markers. These visual markers are used to sense and map obstacles and to improve the pose estimation based on the IMU and optical data flow by means of an Extended Kalman Filter localization and mapping method. The presented solution and the performance of the CVG UPM team were awarded with the First Prize in the Indoors Autonomy Challenge of the IMAV2013 competition

Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning

Developing a safe and efficient collision avoidance policy for multiple robots is challenging in the decentralized scenarios where each robot generate its paths without observing other robots' states and intents. While other distributed multi-robot collision avoidance systems exist, they often require extracting agent-level features to plan a local collision-free action, which can be computationally prohibitive and not robust. More importantly, in practice the performance of these methods are much lower than their centralized counterparts. We present a decentralized sensor-level collision avoidance policy for multi-robot systems, which directly maps raw sensor measurements to an agent's steering commands in terms of movement velocity. As a first step toward reducing the performance gap between decentralized and centralized methods, we present a multi-scenario multi-stage training framework to find an optimal policy which is trained over a large number of robots on rich, complex environments simultaneously using a policy gradient based reinforcement learning algorithm. We validate the learned sensor-level collision avoidance policy in a variety of simulated scenarios with thorough performance evaluations and show that the final learned policy is able to find time efficient, collision-free paths for a large-scale robot system. We also demonstrate that the learned policy can be well generalized to new scenarios that do not appear in the entire training period, including navigating a heterogeneous group of robots and a large-scale scenario with 100 robots. Videos are available at https://sites.google.com/view/drlmac

A System for the Design and Development of Vision-based Multi-robot Quadrotor Swarms

This paper presents a cost-effective framework for the prototyping of vision-based quadrotor multi-robot systems, which core characteristics are: modularity, compatibility with different platforms and being flight-proven. The framework is fully operative, which is shown in the paper through simulations and real flight tests of up to 5 drones, and was demonstrated with the participation in an international micro-aerial vehicles competition3 where it was awarded with the First Prize in the Indoors Autonomy Challenge. The motivation of this framework is to allow the developers to focus on their own research by decoupling the development of dependent modules, leading to a more cost-effective progress in the project. The basic instance of the framework that we propose, which is flight-proven with the cost-efficient and reliable platform Parrot AR Drone 2.0 and is open-source, includes several modules that can be reused and modified, such as: a basic sequential mission planner, a basic 2D trajectory planner, an odometry state estimator, localization and mapping modules which obtain absolute position measurements using visual markers, a trajectory controller and a visualization module

A System for the Design and Development of Vision-based Multi-robot Quadrotor Swarms

This paper presents a cost-effective framework for the prototyping of vision-based quadrotor multi-robot systems, which core characteristics are: modularity, compatibility with different platforms and being flight-proven. The framework is fully operative, which is shown in the paper through simulations and real flight tests of up to 5 drones, and was demonstrated with the participation in an international micro-aerial vehicles competition3 where it was awarded with the First Prize in the Indoors Autonomy Challenge. The motivation of this framework is to allow the developers to focus on their own research by decoupling the development of dependent modules, leading to a more cost-effective progress in the project. The basic instance of the framework that we propose, which is flight-proven with the cost-efficient and reliable platform Parrot AR Drone 2.0 and is open-source, includes several modules that can be reused and modified, such as: a basic sequential mission planner, a basic 2D trajectory planner, an odometry state estimator, localization and mapping modules which obtain absolute position measurements using visual markers, a trajectory controller and a visualization module

Fleet Management System for an Industry Environment

The article deals with the management of a fleet of AMR robots that perform logistics in production. The entire system design is implemented in the ROS environment - state of the art for the development in robotics. Four already available solutions for fleet management in ROSe are analyzed in detail in the article. These solutions fail when there is a need to change the route plan in a dynamically changing environment. Likewise, some did not sufficiently synchronize the movement of the robots and collisions occurred or, with a larger number of robots, represented an enormous computational load. Our solution was designed to be as simple and reliable as possible for industrial use. It is based on a combination of semi-autonomous and centralized approach. A hybrid map is used for planning the movement of the robot fleet, which provides the advantages of both a metric and a topological map. This route map for a fleet of robots can be easily drawn in readily available CAD software. Synchronization of robots was designed on the principle of semaphore or mutex, which enabled the use of bidirectional paths. The results are verified in simulations and were aimed at verifying the proposed robot synchronization. It was confirmed that the proposed synchronization slows down the robots, but there were no collision situations. By separating route planning from synchronization, we simplified the entire fleet management process and thus created a very efficient system for network and hardware resources. In addition, the system is easily expandable