Team Of Micro Aerial Vehicles Research Articles

Shared Control of an Aerial Cooperative Transportation System with a Cable-suspended Payload

This paper presents a novel bilateral shared framework for a cooperative aerial transportation and manipulation system composed by a team of micro aerial vehicles with a cable-suspended payload. The human operator is in charge of steering the payload and he/she can also change online the desired shape of the formation of robots. At the same time, an obstacle avoidance algorithm is in charge of avoiding collisions with the static environment. The signals from the user and from the obstacle avoidance are blended together in the trajectory generation module, by means of a tracking controller and a filter called dynamic input boundary (DIB). The DIB filters out the directions of motions that would bring the system too close to singularities, according to a suitable metric. The loop with the user is finally closed with a force feedback that is informative of the mismatch between the operator’s commands and the trajectory of the payload. This feedback intuitively increases the user’s awareness of obstacles or configurations of the system that are close to singularities. The proposed framework is validated by means of realistic hardware-in-the-loop simulations with a person operating the system via a force-feedback haptic interface.

Cooperative Transportation of Cable Suspended Payloads With MAVs Using Monocular Vision and Inertial Sensing

Micro Aerial Vehicles (MAVs) have the great potential to be deployed in commercial or health care services such as e-commerce package delivery, transportation of medicines, same-day food delivery, and other time-sensitive transportation tasks. A team of MAVs can cooperatively transport objects to overcome the physical limitations of a single vehicle, while concurrently increasing the system's resilience to vehicles' failures. In this letter, we address the state estimation, control and trajectory tracking problems of cooperative transportation of cable suspended rigid body payloads with MAVs using monocular vision and inertial sensing. The key contributions are (a) a distributed vision-based coordinated control of the cable-suspended rigid body payload on SE(3), (b) a distributed estimation approach that allows each agent to estimate its cable direction and velocity independently, and (c) a new cooperative estimation scheme that can infer the payload's full 6-DoF states. This is obtained by sharing the robots' local position estimates and their relative position with respect to their corresponding attachment points on the payload. It allows to infer the payload's 6-DoF state when it is not directly measurable by each vehicle. The proposed solution runs in real-time on board and is validated through experimental results with multiple quadrotors transporting a rigid body payload via cables.

Team NimbRo at MBZIRC 2017: Fast landing on a moving target and treasure hunting with a team of micro aerial vehicles

AbstractThe Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 has defined ambitious new benchmarks to advance the state‐of‐the‐art in autonomous operation of ground‐based and flying robots. This study covers our approaches to solve the two challenges that involved micro aerial vehicles (MAV). Challenge 1 required reliable target perception, fast trajectory planning, and stable control of an MAV to land on a moving vehicle. Challenge 3 demanded a team of MAVs to perform a search and transportation task, coined “Treasure Hunt,” which required mission planning and multirobot coordination as well as adaptive control to account for the additional object weight. We describe our base MAV setup and the challenge‐specific extensions, cover the camera‐based perception, explain control and trajectory‐planning in detail, and elaborate on mission planning and team coordination. We evaluated our systems in simulation as well as with real‐robot experiments during the competition in Abu Dhabi. With our system, we—as part of the larger team NimbRo—won the MBZIRC Grand Challenge and achieved a third place in both subchallenges involving flying robots.

Localization, Grasping, and Transportation of Magnetic Objects by a Team of MAVs in Challenging Desert-Like Environments

Autonomous Micro Aerial Vehicles (MAVs) have the potential to assist in real-life tasks involving grasping and transportation, but not before solving several difficult research challenges. In this work, we address the design, control, estimation, and planning problems for cooperative localization, grasping, and transportation of objects in challenging outdoor scenarios. We demonstrate an autonomous team of MAVs able to plan safe trajectories for manipulation of ferrous objects, while guaranteeing interrobot collision avoidance and automatically creating a map of the objects in the environment. Our solution is predominantly distributed, allowing the team to pick and transport ferrous disks to a final destination without collisions. This result is achieved using a new magnetic gripper with a novel feedback approach, enabling the detection of successful grasping. The gripper design and all the components to build a platform are clearly provided as open-source hardware for reuse by the community. Finally, the proposed solution is validated through experimental results, where difficulties include inconsistent wind, uneven terrain, and sandy conditions.

Search and Rescue Using Multiple Drones in Post-Disaster Situation

We present the development and application of multiple autonomous aerial vehicles in urban search and rescue missions. The missions are designed by the 2014 International Micro Aerial Vehicle Competition, held in Delft, the Netherlands, August 2014. Different mission tasks are identified for search and rescue missions, such as aerial photography, low altitude flight in urban environment, indoor navigation and rooftop landing. These tasks are all of paramount importance for rescuers in a disaster-hit place. We have designed a team of micro aerial vehicles with specific configurations to meet the mission requirements. A range of key technologies have been developed, including robust controller design, real-time map stitching, indoor navigation and roof-top perching. The proposed solutions are successfully demonstrated in the competition.

Optimal surveillance coverage for teams of micro aerial vehicles in GPS-denied environments using onboard vision

This paper deals with the problem of deploying a team of flying robots to perform surveillance-coverage missions over a terrain of arbitrary morphology. In such missions, a key factor for the successful completion of the mission is the knowledge of the terrain's morphology. The focus of this paper is on the implementation of a two-step procedure that allows us to optimally align a team of flying vehicles for the aforementioned task. Initially, a single robot constructs a map of the area using a novel monocular-vision-based approach. A state-of-the-art visual-SLAM algorithm tracks the pose of the camera while, simultaneously, autonomously, building an incremental map of the environment. The map generated is processed and serves as an input to an optimization procedure using the cognitive, adaptive methodology initially introduced in Renzaglia et al. (Proceedings of the IEEE international conference on robotics and intelligent system (IROS), Taipei, Taiwan, pp. 3314---3320, 2010). The output of this procedure is the optimal arrangement of the robots team, which maximizes the monitored area. The efficiency of our approach is demonstrated using real data collected from aerial robots in different outdoor areas.