We present in this paper a decentralized multirobot (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.
Abstract:The key factor for autonomous navigation is efficient perception of the surroundings, while being able to move safely from an initial to a final point. We deal in this paper with a wheeled mobile robot working in a GPS-denied environment typical for a greenhouse. The Hector Simultaneous Localization and Mapping (SLAM) approach is used in order to estimate the robots' pose using a LIght Detection And Ranging (LIDAR) sensor. Waypoint following and obstacle avoidance are ensured by means of a new artificial potential field (APF) controller presented in this paper. The combination of the Hector SLAM and the APF controller allows the mobile robot to perform periodic tasks that require autonomous navigation between predefined waypoints. It also provides the mobile robot with a robustness to changing conditions that may occur inside the greenhouse, caused by the dynamic of plant development through the season. In this study, we show that the robot is safe to operate autonomously with a human presence, and that in contrast to classical odometry methods, no calibration is needed for repositioning the robot over repetitive runs. We include here both hardware and software descriptions, as well as simulation and experimental results.
Fuzzy logic controller for predictive vision-based target tracking with an unmanned aerial vehicle.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 sets of rules. Double Exponential Smoothing Algorithm (DES) is used to filter the measurements and give the predictive value of the UGV pose. The FLC inputs are the filtered 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.
We 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 scheme
Abstract-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-inthe-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.
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