Optimization Based Motion Planning for MultiLimbed Vertical Climbing Robots Motion planning trajectories for a multilimbed 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 sixlegged 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 mixedinteger 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.

Multicontroller multiobjective locomotion planning for legged robots Different legged robot locomotion controllers offer different advantages; from speed of motion to energy, computational demand, safety and others. In this paper we propose a method for planning locomotion with multiple controllers and subplanners, explicitly considering the multiobjective nature of the legged locomotion planning problem. The planner first obtains body paths extended with a choice of controller or subplanner, and then fills the gaps by subplanning. The method leads to paths with a mix of static and dynamic walking which only plan footsteps where necessary. We show that our approach is faster than pure footstep planning methods both in computation (2x) and mission time (1.4x), and safer than pure dynamicwalking methods. In addition, we propose two methods for aggregating the multiple objectives in searchbased planning and reach desirable tradeoffs without weight tuning. We show that they reach desirable Paretooptimal solutions up to 8x faster than fairlytuned traditional weightedsum methods. Our conclusions are drawn from a combination of planning, physics simulation, and real robot experiments.