A Decentralized Interactive Architecture for Aerial and Ground Mobile Robots Cooperation

This paper presents a novel decentralized interactive architecture for aerial and ground mobile robots cooperation. The aerial mobile robot is used to provide a global coverage during an area inspection, while the ground mobile robot is used to provide a local coverage of ground features. We include a human-in-the-loop to provide waypoints for the ground mobile robot to progress safely in the inspected area. The aerial mobile robot follows continuously the ground mobile robot in order to always keep it in its coverage view.Comment: Submitted to 2015 International Conference on Control, Automation and Robotics (ICCAR

Vision Based Target Tracking Using An Unmanned Aerial Vehicle

International audience— We present in this paper a backstepping controller for vision based target tracking with an Unmanned Aerial Vehicle. A down facing camera is used with a pose estimation algorithm to extract the position of the target (an Unmanned Ground Vehicle). The output is then fed into the developed controller to generate the necessary movements (pitch and roll) of the Unmanned Aerial Vehicle in order to keep the target in the coverage view of the camera (following it constantly). The developed scheme is used to help the Unmanned Ground Vehicle to navigate among obstacles, and the overall system is designed in order to help human operator to supervise the Aerial and Ground vehicles for area inspection or object transportation in industrial areas (when using multiple Unmanned Ground Vehicles)

Decentralized Control Architecture for UAV-UGV Cooperation

2nd AETOS international conference on "Research challenges for future RPAS/UAV systems"— We present a decentralized control architecture for an heterogeneous group of mobile robots made of one Unmanned Aerial Vehicles (UAV) and several Unmanned Ground Vehicles (UGVs) performing collaborative tasks (area inspection, object transportation, etc...). The UAV is used to help a human operator to supervise and guide a group of UGVs by providing an aerial coverage view of the navigation area. Our control scheme is based on minimalistic computation and communication requirements, as well as an architecture complexity kept at a simple level regardless of the deployed number of grounds robots. Simulation and experimentations are performed and show the efficiency of our proposed control architecture

UAV-UGV Cooperation For Objects Transportation In An Industrial Area

International audience— We present in this paper a decentralized multi-robot (aerial and ground) cooperation scheme for objects transportation. A team of ground mobile robots guided by a drone and a human operator moves in a coordinated way keeping a predefined formation in order to carry objects (tools, gas masks,...) in unsafe industrial areas. One ground mobile robot (leader) navigates among obstacles thanks to the waypoints provided by the drone and the human operator. The other ground mobile robots (followers) use a predictive vision based target tracking controller to keep a certain distance and bearing to the leader

Towards An Autonomous Warehouse Inventory Scheme

International audienceWe present in this paper a novel warehouse inventory scheme. The main purpose of this work is to make the inventory process completly autonomous. To this end, an Unmanned Ground Vehicle (UGV) and an Unmanned Aerial Vehicle (UAV) works together. The UGV is used as the carrying platform, and considered as a ground reference for indoor flight of the UAV. The UAV is used as the mobile scanner. The UGV navigates among rows of racks carrying the UAV. At each rack to be scanned, the UGV stops at the bottom, the UAV takes off and flies vertically scanning goods in that rack. Once at the top, the UGV moves to the next rack, and since the UAV takes the UGV as the ground reference, it will follow it, this results in placing the UAV at the top of the second rack, and scanning from top to bottom starts. the process is repeated until the row of racks is fully scanned, and the UAV lands on the UGV, and recharge its batteries while the UGV moves to the next row of racks. We present in this paper the proposed architecture, as well as the first experimental results of the proposed schem

Fuzzy logic controller for predictive vision-based target tracking with an unmanned aerial vehicle

International audienceWe present in this paper a Fuzzy Logic Controller (FLC) combined with a predictive algorithm to track an Unmanned Ground Vehicle (UGV), using an Unmanned Aerial Vehicle (UAV). The UAV is equipped with a down facing camera. The video flow is sent continuously to a ground station to be processed in order to extract the location of the UGV and send the commands back to the UAV to follow autonomously the UGV. To emulate an experienced UAVs pilot, we propose a fuzzy-logic set of rules. Double Exponential Smoothing algorithm is used to filter the measurements and give the predictive value of the errors in the image plan. The FLC inputs are the filtered errors (UGV position) in the image plan and the derivative of its predicted value. The outputs are pitch and roll commands to be sent to the UAV. We show the efficiency of the proposed controller experimentally, and discuss the improvement of the tracking results compared to our previous work

Fuzzy logic controller for predictive vision-based target tracking with an unmanned aerial vehicle

<p>We present in this paper a Fuzzy Logic Controller (FLC) combined with a predictive algorithm to track an Unmanned Ground Vehicle (UGV), using an Unmanned Aerial Vehicle (UAV). The UAV is equipped with a down facing camera. The video flow is sent continuously to a ground station to be processed in order to extract the location of the UGV and send the commands back to the UAV to follow autonomously the UGV. To emulate an experienced UAVs pilot, we propose a fuzzy-logic set of rules. Double Exponential Smoothing algorithm is used to filter the measurements and give the predictive value of the errors in the image plan. The FLC inputs are the filtered errors (UGV position) in the image plan and the derivative of its predicted value. The outputs are pitch and roll commands to be sent to the UAV. We show the efficiency of the proposed controller experimentally, and discuss the improvement of the tracking results compared to our previous work.</p