Motion planning for humanoid robots under obstacle and dynamic balance constraints

Abstract

We present an approach to path planning for humanoid robots that computes dynamically-stable, collision-free trajectories from full-body posture goals. Given a geometric model of the environment and a statically-stable desired posture, we search the configuration space of the robot for a collision-free path that simultaneously satisfies dynamic balance constraints. We adapt existing randomized path planning techniques by imposing balance constraints on incremental search motions in order to maintain the overall dynamic stability of the final path. A dynamics filtering function that constrains the ZMP (zero moment point) trajectory is used as a post-processing step to transform statically-stable, collision-free paths into dynamically-stable, collision-free trajectories for the entire body. Although we have focused our experiments on biped robots with a humanoid shape, the method generally applies to any robot subject to balance constraints (legged or not). The algorithm is presented along with computed examples using the humanoid robot "H6".

Toward Autonomous Free-Climbing Robots

“…Algorithms in [7,12,21,30,31] are based on assumptions that are not valid for climbing vertical terrain. In particular, in [7,21] massless and friction is not modeled.…”

“…In particular, in [7,21] massless and friction is not modeled. In [30,31] a randomized planner computes dynamically-stable motions of a humanoid biped robot among obstacles, but considers horizontal foot-placements only. Motion strategies for a planar quadruped robot are given in [45], but are restricted to horizontal tunnels, and require form-closure configurations during each motion step.…”

Integrating dynamics into motion planning for humanoid robots

“…We propose a method which consists of two stages. The first stage plans a collision-free path and it is transformed into a dynamically stable trajectory in the second stage like [2]. Here, path is a series of configurations and trajectory associates time and these configurations.…”

“…[3] also proposes a two stages approach for passing under obstacles. It plans a statically stable path in the first stage like [2] but the path is transformed into a dynamically stable trajectory without changing shape of the path. [4], [5] and [6] propose methods that integrate motion planning techniques and a bipedal walking pattern generator.…”

Path planning in 1000+ dimensions using a task-space Voronoi bias

“…This approach is commonly used in feedback control design, and is often called Task Space Control [5]. Consider a humanoid robot which needs to find its way from a start pose to cross a room full of obstacles, and pick up an object [6]. For such problems, it is natural to consider the task space.…”