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1	Planning semi-autonomous drone photo missions in Google Earth Nilsson, Per Johan Fredrik January 2017 (has links) This report covers an investigation of the methods and algorithms required to plan and perform semi-autonomous photo missions on Apple iPad devices using data exported from Google Earth. Flight time was to be minimized, taking wind velocity and aircraft performance into account. Google Earth was used both to define what photos to take, and to define the allowable mission area for the aircraft. A benchmark mission was created containing 30 photo operations in a 250 by 500 m area containing several no-fly-areas. The report demonstrates that photos taken in Google Earth can be reproduced in reality with good visual resemblance. High quality paths between all possible photo operation pairs in the benchmark mission could be found in seconds using the Theta* algorithm in a 3D grid representation with six-edge connectivity (Up, Down, North, South, East, West). Smoothing the path in a post-processing step was shown to further increase the quality of the path at a very low computational cost. An optimal route between the operations in the benchmark mission, using the paths found by Theta, could be found in less than half a minute using a Branch-and-Bound algorithm. It was however also found that prematurely terminating the algorithm after five seconds yielded a route that was close enough to optimal not to warrant running the algorithm to completion. Google Earth Robotics Pathfinding A Theta* Path smoothing Traveling Salesman Problem Branch-and-Bound Swift C++ Software Engineering Programvaruteknik
2	Path Planning and Collision Avoidance for a 6-DOF Manipulator : A Comparative Study of Path Planning and Collision Avoidance Algorithms for the Saab Seaeye eM1-7 Electric Manipulator Ohlander, Hampus, Johnson, David January 2024 (has links) This project investigated the implementation and evaluation of various collision-free path planning algorithms for the Saab Seaeye eM1-7 6-DOF Electric Manipulator (eManip). The primary goal was to enhance the autonomous performance of the eManip by integrating efficient path planning methodologies, ultimately ensuring the avoidance of collisions and manipulator singularities during underwater operations. Key algorithms examined included the Rapidly-exploring Random Trees (RRT) algorithm and its enhanced variants. Through simulation tests in MATLAB and Gazebo, metrics such as planning time, path length, and the number of explored nodes were evaluated. The results highlighted the robustness of Goal-biased and Bidirectional RRT* (Gb-Bd-RRT*), which consistently performed well across various environments. The research also highlighted the correlation between algorithm effectiveness and specific task attributes, emphasizing their adaptability to complex environments. This research contributes valuable insights into the effectiveness of path planning algorithms, informing the selection and integration of viable strategies for 6-DOF robotic manipulators. Path Planning Collision Avoidance 6-DOF Manipulator Rapidly-exploring Random Trees (RRT) Goal-biased Bidirectional Saab Seaeye eM1-7 Underwater Robotics Autonomous Manipulation Robot Operating System (ROS) Sampling-based Algorithms Configuration Space Manipulator Singularity Path Smoothing Obstacle Avoidance Electric Manipulator Robotic Manipulators Autonomous Performance Robotics Robotteknik och automation

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Planning semi-autonomous drone photo missions in Google Earth

Path Planning and Collision Avoidance for a 6-DOF Manipulator : A Comparative Study of Path Planning and Collision Avoidance Algorithms for the Saab Seaeye eM1-7 Electric Manipulator