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Robot footstep planning strategies can be divided in two main approaches:discrete searches and continuous optimizations. While discrete searches havebeen broadly applied, continuous optimizations approaches have been restrictedfor humanoid platforms. This article introduces a generalizedcontinuous-optimization approach for multilegged footstep planning which can beadapted to different platforms, regardless the number and geometry of legs.This approach leverages Mixed-Integer Convex Programming to account for thenon-convex constraints that represent footstep rotation and obstacle avoidance.The planning problem is formulated as an optimization problem which considersrobot geometry and reachability with linear constraints, and can be efficientlysolved using optimization software. To demonstrate the functionality andadaptability of the planner, a set of tests are performed on a BH3R hexapod anda LittleDog quadruped on scenarios which can't be easily handled with discretesearches, such tests are solved efficiently in fractions of a second. This workrepresents, to the knowledge of the authors, the first successfulimplementation of a continuous optimization-based multilegged footstep planner.

A Generalized Mixed-Integer Convex Program for Multilegged Footstep Planning on Uneven Terrain