Dynamic Walking on Randomly-Varying Discrete Terrain with One-step Preview

Abstract

Abstract-An inspiration for developing a bipedal walking system is the ability to navigate rough terrain with discrete footholds like stepping stones. In this paper, we present a novel methodology to overcome the problem of dynamic walking over stepping stones with significant random changes to step length and step height at each step. Using a 2-step gait optimization, we not only consider the desired location of the next footstep but also the current configuration of the robot, thereby resolving the problem of step transition when we switch between different walking gaits. We then use gait interpolation to generate the desired walking gait in real-time. We demonstrate the method on a planar dynamical walking model of ATRIAS, an underactuated bipedal robot walking over a randomly generated stepping stones with step length and step height changing in the range of [30:80] (cm) and [-30:30] (cm) respectively. Experimental validation on the real robot was also successful for the problem of dynamic walking on stepping stones with step lengths varied within [23:78] (cm) and average walking speed of 0.6 (m/s).

Input-to-State Safety With Control Barrier Functions

“…Therefore, CLFs and CBFs can be encoded as constraints in a single QP that can either a) ensure both stability and safety or b) prioritize safety or stability over the other depending upon the applications. To date, this new notion of CBFs have been successfully implemented in automotive systems [8], flying systems [9], multi-robot systems [10], and also walking robots [11]. It has also been observed that in all these systems uncertainties were a common occurrence, and we had no means to characterize them in a formal manner.…”

Modeling the Kinematics of Human Locomotion Over Continuously Varying Speeds and Inclines

“…This method creates realistic gait at many speeds in simulation, and provided more power at higher speeds in experiments with an ankle prosthesis. Some biped robots depend on a finite set of optimal gaits, each designed for a specific task, and either define non-periodic transitional gaits to guide the robot back to a preprogrammed periodic gait [23] or interpolate the optimal kinematics for all other tasks in between [24]–[26]. This results in robust gait and the ability to handle a variety of terrains, but requires a large and well-structured set of optimal gaits to interpolate between.…”

Deep visual perception for dynamic walking on discrete terrain

“…We build a realistic visual simulator to generate the robot's first person view and combine it with an accurate physics simulator of the bipedal robot walking on discrete terrain. The physics simulator also contains an inner-loop safety-critical controller that can generate stable and safe limit cycle walking of a desired step length [19]. In this setup, we train a deep neural network to estimate the step length (distance to the next stepping location) using a single sampled image preview that is obtained at the beginning of each step.…”

“…Stone Location: From [19], we note that, the robot was able to walk on a discrete terrain where the step lengths ranged from [20 : 90] (cm). True step length is the distance from the robot's stance foot to the next stone's center.…”