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Design of an Open-Source Micro Aerial Vehicle for Onboard Autonomous NavigationAuthor: Gourav Kumar Date: 2019-11-08 Report no: IIIT/TH/2019/113 Advisor:Madhava Krishna AbstractAutonomous micro-aerial vehicles have potential to assist us in several critical tasks like exploration, inspection, cooperative construction and mapping, but before they can be used for those tasks we need to solve several practical challenges dealing with localization, mapping, planning and trajectory gener- ation. With the sharp rise in research along various applications using aerial platforms, the need for an affordable, simple, modular and open-source design which can be used as a template to easily accom- modate and test state-of-the-art innovations and research became of paramount importance. To design a system that is fully autonomous and capable of flying in constrained environments, we have to keep in mind the size constraints while satisfying the required sensing and processing needs. In this work we present a complete onboard system for autonomous navigation of a micro-aerial vehicle using vision- based flight in indoor cluttered environments. We develop a single integrated aerial platform capable of onboard localization, mapping and planning for autonomous exploration using a single monocular cam- era and an IMU(Inertial Measurement Unit). We demonstrate the utility of this platform by deploying it for the task of autonomous exploration and payload transfer. Although a lot of work has been done in the domain of localization, mapping, planning and trajectory generation, there is a large scope for improvement in each of these. One of the problems that is frequently encountered by an autonomous explorative robot is scenarios where multiple future trajectories can be pursued. Often these are cases with multiple paths around an obstacle or trajectory options towards various frontiers. Classical tra- jectory generation methods either requires a prior information about immediate goal/goals or perform an exhaustive search of the known region to sample possible immediate goals which takes significant computation time and resources. Another method of acquiring subsequent goals is based on heuristics which computes the information gain on the available occupancy map and provide optimal immediate goal locations. Humans in such situations can inherently perceive and reason about the surrounding environment to identify several possibilities of either maneuvering around the obstacles or moving to- wards various frontiers. Inspired from the above fact we propose a two-stage Convolutional Neural Network architecture which mimics such an ability to map the perceived surroundings to multiple trajectories that a robot can choose to traverse. The first stage is a Trajectory Proposal Network which suggests diverse regions in the environment that can be occupied in the future. The second stage is a Trajectory Sampling network which provides a fine grained trajectory over the regions proposed by Trajectory Proposal Network. We evaluate our framework in diverse and complicated real life settings.The above proposed technique was tested on standard datasets like KITTI dataset and our own outdoor driving dataset collected in the University campus. For exploration of the environment using the trajectories proposed by our frame- work on actual robots in real world scenarios we used our custom made autonomous drone for indoor scenarios, ClearPath Husky robot for outdoor alleys and a Mahindra E2O electric vehicle for road set- tings. Our experiments suggest that the framework is able to develop a semantic understanding of the obstacles, open regions and identify diverse trajectories that a robot can traverse. Our comparisons por- tray the performance gain of the proposed architecture over a diverse set of methods against which it is compared. Full thesis: pdf Centre for Robotics |
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