Variable Configuration Planner for LeggedRolling Obstacle Negotiation Locomotion: Application on the CENTAURO Robot Hybrid leggedwheeled robots are able to adapt their leg configuration and height to vary their footprint polygons and go over obstacles or traverse narrow spaces. In this paper, we present a variable configuration wheeled motion planner based on the A* algorithm. It takes advantage of the agility of hybrid wheeledlegged robots and plans paths over lowlying obstacles and in narrow spaces. By imposing a symmetry on the robot polygon, the computed plans lie in a lowdimensional search space that provides the robot with configurations to safely negotiate obstacles by expanding or shrinking its footprint polygon. The introduced autonomous planner is demonstrated using simulations and realworld experiments with the CENTAURO robot.

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.