Design, simulate, and deploy algorithms for autonomous navigation
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Create 2D and 3D occupancy grids. Use multilayer maps to store generic data such as costs. Represent obstacles using capsule-based collision objects.
Implement customized multi-sensor SLAM solutions using robust pose graph optimization. Use interactive tools to review and modify loop closures.
Find paths through diverse environments using customizable sampling-based planners such as RRT and RRT*, or search-based planners such as A* and Hybrid A*.
Model and tune parameters for various sensors such as IMU, GPS, GNSS, wheel encoders, and range finders. Visualize sensor orientation, velocity, trajectories, and measurements.
Localize ground and aerial vehicles using inertial sensors with or without GPS. Automatically tune filters to minimize pose estimation error.
Plan local trajectories around a global path while avoiding moving obstacles. Follow the planned path or trajectories using Control algorithm.
“Using MATLAB and Simulink, we designed a prototype for the motion controller and tested it on the hardware within a month. The localization algorithm was evaluated and challenges were clarified by performing simulations.”Haruki Takemoto and Kenneth Renny Simba, Musashi Seimitsu Industry Co., Ltd.