Motion Planning for Legged Robots on Varied Terrain

Abstract

In this paper we study the quasi-static motion of large legged robots that have many degrees of freedom. While gaited walking may suffice on easy ground, rough and steep terrain requires unique sequences of footsteps and postural adjustments specifically adapted to the terrain's local geometric and physical properties. In this paper we present a planner that computes these motions by combining graph searching to generate a sequence of candidate footfalls with probabilistic sample-based planning to generate continuous motions that reach these footfalls. To improve motion quality, the probabilistic planner derives its sampling strategy from a small set of motion primitives that have been generated offline. The viability of this approach is demonstrated in simulation for the six-legged Lunar vehicle ATHLETE and the humanoid HRP-2 on several example terrains, including one that requires both hand and foot contacts and another that requires rappelling.

Decision Making Process of Hexapods in a Model of Complex Terrains

“…The proposed methods rely on the movement of most insects since they move slowly, and the problem of vertical size of obstacles has been partially solved in [22]. However, vertical movement of the legs is determined exclusively by the height of the obstacles in direction of walking [30] and does not affect the actions (i.e.…”

“…Normally, the feet must not touch the obstacles, [22]. If we know where the obstacles are located in LW, it is possible to identify possible step lengths within the LW.…”

“…A large number of methods focusing on the complexity of terrain has been designed in the field of robotic studies, and some of these are based on the description of rough terrain. Although procedures for planning foot trajectory and body trajectory of walking robots in 3D space have already been designed [22][23][24][25], no method or variable to describe a group of obstacles of different sizes in a complex terrain corresponding to the walked distance or number of actions has been developed yet. In contrast with methods describing obstacles of different heights corresponding to the dimensions of the robot's trunk already introduced [26], a method of assessing lengths (horizontal sizes) of obstacles, however, has never been described comprehensively.…”

Sampling-Based Methods for Motion Planning with Constraints

“…addition and removal of constraints (34,35). Combining constraints where only a subset need to be satisfied at any given time is an "or"-ing of the constraints together-creating a union of constraint manifolds that potentially intersect and overlap.…”

Probabilistic Completeness of Randomized Possibility Graphs Applied to Bipedal Walking in Semi-unstructured Environments

“…1. The Multi-modal Probabilistic Roadmap (MMPRM) approach by Hauser et al [9,10,11] uses randomized sampling to find whole body motions over a set of predetermined environmental contacts. Given a suitable set of environmental contact points, this method is proven to be probabilistically complete for the full range of quasi-static motion of the robot.…”