Abstract-Legged robots that operate in the real world are inherently subject to stochasticity in their dynamics and uncertainty about the terrain. Due to limited energy budgets and limited control authority, these "disturbances" cannot always be canceled out with high-gain feedback. Minimally-actuated walking machines subject to stochastic disturbances no longer satisfy strict conditions for limit-cycle stability; however, they can still demonstrate impressively long-living periods of continuous walking. Here, we employ tools from stochastic processes to examine the "stochastic stability" of idealized rimless-wheel and compass-gait walking on randomly generated uneven terrain. Furthermore, we employ tools from numerical stochastic optimal control to design a controller for an actuated compass gait model which maximizes a measure of stochastic stability -the mean first-passage-time -and compare its performance to a deterministic counterpart. Our results demonstrate that walking is well-characterized as a metastable process, and that the stochastic dynamics of walking should be accounted for during control design in order to improve the stability of our machines.
Recent work in terrain classification has relied largely on 3D sensing methods and color based classification. We present an approach that works with a single, compact camera and maintains high classification rates that are robust to changes in illumination. Terrain is classified using a bag of visual words (BOVW) created from speeded up robust features (SURF) with a support vector machine (SVM) classifier. We present several novel techniques to augment this approach. A gradient descent inspired algorithm is used to adjust the SURF Hessian threshold to reach a nominal feature density. A sliding window technique is also used to classify mixed terrain images with high resolution. We demonstrate that our approach is suitable for small legged robots by performing real-time terrain classification on LittleDog. The classifier is used to select between predetermined gaits to traverse terrain of varying difficulty. Results indicate that real-time classification in-the-loop is faster than using a single all-terrain gait.
Abstract-In this paper, we explore the capabilities of actuated models of the compass gait walker on rough terrain. We solve for the optimal high-level feedback policy to negotiate a perfectly known but qualitatively complex terrain, using a fixed low-level controller which selects a high-level action onceper-step. We also demonstrate that a one-step time horizon control strategy using the same low-level controller can provide performance which is surprisingly comparable to that of the infinite time horizon optimal policy. The model presented here uses a torque at the hip and an axially-directed impulsive toe-off applied just before each ground collision. Our results provide compelling evidence that actuated robots based on passive dynamic principles (e.g. no ankle torque) should inherently be capable of walking on significantly rough terrain.
This article presents the hardware design and software algorithms of RoboSimian, a statically stable quadrupedal robot capable of both dexterous manipulation and versatile mobility in difficult terrain. The robot has generalized limbs and hands capable of mobility and manipulation, along with almost fully hemispherical 3D sensing with passive stereo cameras. The system is semi-autonomous, enabling low-bandwidth, high latency control operated from a standard laptop. Because limbs are used for mobility and manipulation, a single unified mobile manipulation planner is used to generate autonomous behaviors, including walking, sitting, climbing, grasping, and manipulating. The remote operator interface is optimized to designate, parameterize, sequence, and preview behaviors, which are then executed by the robot. RoboSimian placed fifth in the DARPA Robotics Challenge (DRC) Trials, demonstrating its ability to perform disaster recovery tasks in degraded human environments.
Abstract-In this paper, we present and verify methods for developing robust, high-level policies for metastable (i.e., rarely falling) rough-terrain robot walking. We focus on simultaneously addressing the important, real-world challenges of (1) use of a tractable mesh, to avoid the curse of dimensionality and (2) maintaining near-optimal performance that is robust to uncertainties. Toward our first goal, we present an improved meshing technique, which captures the step-to-step dynamics of robot walking as a discrete-time Markov chain with a small number of points. We keep our methods and analysis generic, and illustrate robustness by quantifying the stability of resulting control policies derived through our methods. To demonstrate our approach, we focus on the challenge of optimally switching among a finite set of low-level controllers for underactuated, rough-terrain walking. Via appropriate meshing techniques, we see that even terrain-blind switching between multiple controllers increases the stability of the robot, while lookahead (terrain information) makes this improvement dramatic. We deal with both noise on the lookahead information and on the state of the robot. These two robustness requirements are essential for our methods to be applicable to real high-DOF robots, which is the primary motivation of the authors.
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