Safe Navigation of Quadruped Robots Using Density Functions

Safe navigation of mission-critical systems is of utmost importance in many modern autonomous applications. Over the past decades, the approach to the problem has consisted of using probabilistic methods, such as sample-based planners, to generate feasible, safe solutions to the navigation problem. However, these methods use iterative safety checks to guarantee the safety of the system, which can become quite complex. The navigation problem can also be solved in feedback form using potential field methods. Navigation function, a class of potential field methods, is an analytical control design to give almost everywhere convergence properties, but under certain topological constraints and mapping onto a sphere world. Alternatively, the navigation problem can be formulated in the dual space of density. Recent works have shown the use of linear operator theory on density to convexly approach the navigation problem. Inspired by those works, this work uses the physical-based interpretation of occupation through density to synthesize a safe controller for the navigation problem. Moreso, by using this occupation-based interpretation of density, we design a feedback density-based controller to solve the almost everywhere navigation problem. Furthermore, due to the recent popularity of legged locomotion for the navigation problem, we integrate this analytical feedback density-based controller into the quadruped navigation problem. By devising a density-based navigation architecture, we show in simulation and hardware the results of the density-based navigation

Safe Robot Planning and Control Using Uncertainty-Aware Deep Learning

In order for robots to autonomously operate in novel environments over extended periods of time, they must learn and adapt to changes in the dynamics of their motion and the environment. Neural networks have been shown to be a versatile and powerful tool for learning dynamics and semantic information. However, there is reluctance to deploy these methods on safety-critical or high-risk applications, since neural networks tend to be black-box function approximators. Therefore, there is a need for investigation into how these machine learning methods can be safely leveraged for learning-based controls, planning, and traversability. The aim of this thesis is to explore methods for both establishing safety guarantees as well as accurately quantifying risks when using deep neural networks for robot planning, especially in high-risk environments. First, we consider uncertainty-aware Bayesian Neural Networks for adaptive control, and introduce a method for guaranteeing safety under certain assumptions. Second, we investigate deep quantile regression learning methods for learning time-and-state varying uncertainties, which we use to perform trajectory optimization with Model Predictive Control. Third, we introduce a complete framework for risk-aware traversability and planning, which we use to enable safe exploration of extreme environments. Fourth, we again leverage deep quantile regression and establish a method for accurately learning the distribution of traversability risks in these environments, which can be used to create safety constraints for planning and control.Ph.D

Algorithms for rough terrain trajectory planning

| This paper deals with motion planning on rough terrain for mobile robots. The aim is to develop eecient algorithms, suitable for various types of robots. On rough terrain, the planned trajectory must verify several validity constraints : stability of the robot, mechanical limits and collision avoidance with the ground. Our approach relies on a static and kinematic model of the robot. EEcient geometric algorithms have been developed, taking advantage of each vehicle's speciicities. Motion planning relies on incremental search in the discretized connguration space and uses eecient heuristics based on terrain characteristic to limit the size of search space. Simulation results present trajectories planned in a few seconds. The second part takes into account uncertainties to improve trajectory robustness: uncertainties on the terrain model and the position of the robot. The adaptation of the previous algorithms allow to nd robust trajectories, without excessive time increase