Computing Large Convex Regions of Obstacle-Free Space Through Semidefinite Programming

By Robin Deits, Russ Tedrake. Workshop on the Algorithmic Foundations of Robotics, 2014

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Planning Dynamically Feasible Trajectories for Quadrotors Using Safe Flight Corridors in 3-D Complex Environments (2017, methodology): “Inspired by [14], we developed a novel convex decomposition”
Footstep planning on uneven terrain with mixed-integer convex optimization (2014, methodology): “The IRIS algorithm generates these regions efficiently, but currently requires user input to seed the position of each region.”
Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot (2015, methodology): “In order to compute these regions, we have developed IRIS, an algorithm for greedily computing a single large obstacle-free convex region [21].”
Multi-robot formation control and object transport in dynamic environments via constrained optimization (2017, unknown): “WeuseSNOPT by Gill et al. (2002) to solve the non-linear program via Sequential Quadratic Programming, a goal-directed version of IRIS by Deits and Tedrake (2014) to compute the large convex regions and the Drake toolbox 3 from MIT to handle quaternions.”