Paul Vernaza

Search-based planning for a legged robot over rough terrain

We present a search-based planning approach for controlling a quadrupedal robot over rough terrain. Given a start and goal position, we consider the problem of generating a complete joint trajectory that will result in the legged robot successfully moving from the start to the goal. We decompose the problem into two main phases: an initial global planning phase, which results in a footstep trajectory; and an execution phase, which dynamically generates a joint trajectory to best execute the footstep trajectory. We show how R* search can be employed to generate high-quality global plans in the high-dimensional space of footstep trajectories. Results show that the global plans coupled with the joint controller result in a system robust enough to deal with a variety of terrains.

Proprioceptive localilzatilon for a quadrupedal robot on known terrain

We present a novel method for the localization of a legged robot on known terrain using only proprioceptive sensors such as joint encoders and an inertial measurement unit. In contrast to other proprioceptive pose estimation techniques, this method allows for global localization (i.e., localization with large initial uncertainty) without the use of exteroceptive sensors. This is made possible by establishing a measurement model based on the feasibility of putative poses on known terrain given observed joint angles and attitude measurements. Results are shown that demonstrate that the method performs better than dead-reckoning, and is also able to perform global localization from large initial uncertainty

Cooperative relative robot localization with audible acoustic sensing

We describe a method for estimating the relative poses of a team of mobile robots using only acoustic sensing. The relative distances and bearing angles of the robots are estimated using the time of arrival of audible sound signals on stereo microphones. The robots emit specially designed sound waveforms that simultaneously enable robot identification and time of arrival estimation. These acoustic observations are then combined with odometry to update a belief state describing the positions and heading angles of all the robots. To efficiently resolve the ambiguity in the heading angle of the observing robot as well as the back-front ambiguity of the observed robot, we employ a Rao-Blackwellised particle filter (RBPF) where the distribution over heading angles is represented by a discrete set of particles, and the uncertainty in the translational positions conditioned on each of these particles is described by a Gaussian. This approach combines the representational accuracy of conventional particle filters with the efficiency of Kalman filter updates in modeling the pose distribution over a number of robots. We demonstrate how the RBPF can quickly resolve uncertainties in the binaural acoustic measurements and yield a globally consistent pose estimate. Simulations as well as an experimental implementation on robots with generic sound hardware illustrate the accuracy and the convergence of the resulting pose estimates.

Rao-Blackwellized particle filtering for 6-DOF estimation of attitude and position via GPS and inertial sensors

The authors present an innovative method for the efficient joint estimation of attitude and position in six degrees of freedom via sensors such as GPS, inertial measurement units, and odometry. Traditional methods for attitude estimation via Kalman filtering are beset by many conceptual problems relating to the representation of orientations in linear spaces, leading to difficulties in implementation and the interpretation of uncertainty estimates, among other issues. These problems are compounded when it is necessary to jointly estimate position and attitude. We demonstrate how Rao-Blackwellized particle filtering provides a framework for approaching this estimation problem that is both conceptually appealing and practical. Results are shown that demonstrate the filters robustness to sensor outages and its ability to perform well even in situations with noisy sensors and high initial uncertainty in all state dimensions; these situations are precisely those in which traditional Kalman filtering approaches are most likely to experience problems

Planning under topological constraints using beam-graphs

We present a framework based on graph search for navigation in the plane with a variety of topological constraints. The method is based on modifying a standard graph-based navigation approach to keep an additional state variable that encodes topological information about the path. The topological information is represented by a sequence of virtual sensor beam crossings. By considering classes of beam crossing sequences to be equivalent under certain equivalence relations, we obtain a general method for planning with topological constraints that subsumes existing approaches while admitting more favorable representational characteristics. We provide experimental results that validate the approach and show how the planner can be used to find loop paths for autonomous surveillance problems, simultaneously satisfying minimum-cost objectives and in dynamic environments. As an additional application, we demonstrate the use of our planner on the PR2 robot for automated building of 3D object models.

Robust GPS/INS-Aided Localization and Mapping Via GPS Bias Estimation

We consider the problem of pose estimation in the context of outdoor robotic mapping. In such cases absolute position information from GPS is often available. However, the peculiarities of GPS can lead to significant inconsistencies in mapping when a naive approach is used. We thus present a two-stage pose estimation system to address this problem. The first stage consists of a best-effort “blind” pose estimator based on a robust and extensible Rao-Blackwellized particle filtering framework. The estimate from this stage is then fed to a “seeing” HMM-style filter that attempts to infer the uncorrected bias of the first stage by matching stereo maps under an assumption of scene rigidity. Results are shown that demonstrate a vast improvement in pose estimates and map consistency using this method over the naive approach.

Continuous planning with winding constraints using optimal heuristic-driven front propagation

Recent work has produced methods to solve the winding-constrained optimal feedback navigation problem. Given the start and the goal positions and the winding constraints, the solution to this problem is a feedback vector field such that, when integrated from the start, the trajectory is the shortest path connecting the start and the goal which satisfies given constraints. Such constraints intuitively restrict the direction and the number of times the path winds around given planar regions. We formulate a continuous version of this problem that contrasts with the discrete treatments previously presented. This leads to a geometrical characterization of the problem for which simplicial complex approximation is particularly useful. Thus, it yields theoretical insight as well as a practical algorithm for approximating the continuous problem using an efficient and high-accuracy heuristic-driven front propagation method on simplicial meshes. Experimental results are given evaluating the solution quality and efficiency of the method versus methods based on the discrete formulation and without using heuristics.

Efficiently finding optimal winding-constrained loops in the plane

We present a method to efficiently find winding-constrained loops in the plane that are optimal with respect to a minimumcost objective and in the presence of obstacles. Our approach is similar to a typical graph-based search for an optimal path in the plane, but with an additional state variable that encodes information about path homotopy. Upon finding a loop, the value of this state corresponds to a line integral over the loop that indicates how many times it winds around each obstacle, enabling us to reduce the problem of finding paths satisfying winding constraints to that of searching for paths to suitable states in this augmented state space. We give an intuitive interpretation of the method based on fluid mechanics and show how this yields a way to perform the necessary calculations efficiently. Results are given in which we use our method to find optimal routes for autonomous surveillance and intruder containment.

Efficient dynamic programming for high-dimensional, optimal motion planning by spectral learning of approximate value function symmetries

We demonstrate how to find high-quality motion plans for high-dimensional holonomic systems efficiently using dynamic programming in a learned subspace of vastly reduced dimension. Our approach (SLASHDP) learns the low-dimensional cost structure of an optimal control problem via an efficient spectral method. This structure results in a symmetric value function that serves as a an efficiently-computable surrogate for the true value function. High-quality feedback motion plans can then be obtained from the symmetric value function. Experimental results show that SLASHDP yields higher-quality plans than can be obtained by post-processing plans generated by a sampling-based motion planner, and with less computational effort for very high-dimensional problems. We demonstrate high-quality dynamic programming plans for an arm planning problem of up to 144 dimensions without using any domain-specific knowledge aside from that learned automatically by SLASHDP. Positive results are also shown for a high-dimensional deformable robot planning problem.

Learning product set models of fault triggers in high-dimensional software interfaces

We propose a method for generating interpretable descriptions of inputs that cause faults in high-dimensional software interfaces. Our method models the set of fault-triggering inputs as a Cartesian product and identifies this set by actively querying the system under test. The active sampling scheme is very efficient in the common case that few fields in the interface are relevant to causing the fault. This scheme also solves the problem of efficiently finding sufficient examples to model rare faults, which is problematic for other learning-based methods. Compared to other techniques, ours requires no parameter turning or post-processing in order to produce useful results. We analyze the method qualitatively, theoretically, and empirically. An experimental evaluation demonstrates superior performance and reliability compared to a basic decision tree approach. We also briefly discuss how the method has assisted in debugging a commercial autonomous ground vehicle system.