  Motion planning trajectories for a multi-limbed robot to climb up walls
requires a unique combination of constraints on torque, contact force, and
posture. This paper focuses on motion planning for one particular setup wherein
a six-legged robot braces itself between two vertical walls and climbs
vertically with end effectors that only use friction. Instead of motion
planning with a single nonlinear programming (NLP) solver, we decoupled the
problem into two parts with distinct physical meaning: torso postures and
contact forces. The first part can be formulated as either a mixed-integer
convex programming (MICP) or NLP problem, while the second part is formulated
as a series of standard convex optimization problems. Variants of the two wall
climbing problem e.g., obstacle avoidance, uneven surfaces, and angled walls,
help verify the proposed method in simulation and experimentation.
