arXiv subjects

Koushil Sreenath

Publications and source records attributed to Koushil Sreenath.

125 records · Page 7Linked to original sources

arXiv2021

Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects and Dynamics

This paper presents a method to design a min-norm Control Lyapunov Function (CLF)-based stabilizing controller for a control-affine system with uncertain dynamics using Gaussian Process (GP) regression. In order to estimate both state and input-dependent model uncertainty, we propose a novel compound kernel that captures the control-affine nature of the problem. Furthermore, by the use of GP Upper Confidence Bound analysis, we provide probabilistic bounds of the regression error, leading to the formulation of a CLF-based stability chance constraint which can be incorporated in a min-norm optimization problem. We show that this resulting optimization problem is convex, and we call it Gaussian Process-based Control Lyapunov Function Second-Order Cone Program (GP-CLF-SOCP). The data-collection process and the training of the GP regression model are carried out in an episodic learning fashion. We validate the proposed algorithm and controller in numerical simulations of an inverted pendulum and a kinematic bicycle model, resulting in stable trajectories which are very similar to the ones obtained if we actually knew the true plant dynamics.

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arXiv2021

Safety-Critical Control using Optimal-decay Control Barrier Function with Guaranteed Point-wise Feasibility

Safety is one of the fundamental problems in robotics. Recently, a quadratic program-based control barrier function (CBF) method has emerged as a way to enforce safety-critical constraints. Together with control Lyapunov function (CLF), it forms a safety-critical control strategy, named CLF-CBF-QP, which can mediate between achieving the control objective and ensuring safety, while being executable in real-time. However, once additional constraints such as input constraints are introduced, the CLF-CBF-QP may encounter infeasibility. In order to address the challenge that arises due to the infeasibility, we propose an optimal-decay form for safety-critical control wherein the decay rate of the CBF is optimized point-wise in time so as to guarantee point-wise feasibility when the state lies inside the safe set. The proposed control design is numerically validated using an adaptive cruise control example.

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arXiv2021

Rule-Based Safety-Critical Control Design using Control Barrier Functions with Application to Autonomous Lane Change

This paper develops a new control design for guaranteeing a vehicle's safety during lane change maneuvers in a complex traffic environment. The proposed method uses a finite state machine (FSM), where a quadratic program based optimization problem using control Lyapunov functions and control barrier functions (CLF-CBF-QP) is used to calculate the system's optimal inputs via rule-based control strategies. The FSM can make switches between different states automatically according to the command of driver and traffic environment, which makes the ego vehicle find a safe opportunity to do a collision-free lane change maneuver. By using a convex quadratic program, the controller can guarantee the system's safety at a high update frequency. A set of pre-designed typical lane change scenarios as well as randomly generated driving scenarios are simulated to show the performance of our controller.

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arXiv2020

Dynamic Legged Manipulation of a Ball Through Multi-Contact Optimization

The feet of robots are typically used to design locomotion strategies, such as balancing, walking, and running. However, they also have great potential to perform manipulation tasks. In this paper, we propose a model predictive control (MPC) framework for a quadrupedal robot to dynamically balance on a ball and simultaneously manipulate it to follow various trajectories such as straight lines, sinusoids, circles and in-place turning. We numerically validate our controller on the Mini Cheetah robot using different gaits including trotting, bounding, and pronking on the ball.

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arXiv2020

Staging energy sources to extend flight time of a multirotor UAV

Energy sources such as batteries do not decrease in mass after consumption, unlike combustion-based fuels. We present the concept of staging energy sources, i.e. consuming energy in stages and ejecting used stages, to progressively reduce the mass of aerial vehicles in-flight which reduces power consumption, and consequently increases flight time. A flight time vs. energy storage mass analysis is presented to show the endurance benefit of staging to multirotors. We consider two specific problems in discrete staging -- optimal order of staging given a certain number of energy sources, and optimal partitioning of a given energy storage mass budget into a given number of stages. We then derive results for two continuously staged cases -- an internal combustion engine driving propellers and a rocket engine. Notably, we show that a multicopter powered by internal combustion has an upper limit on achievable flight time independent of the available fuel mass, but no such limit exists for rocket propulsion. Lastly, we validate the analysis with flight experiments on a custom two-stage battery-powered quadcopter. This quadcopter can eject a battery stage after consumption in-flight using a custom-designed mechanism, and continue hovering using the next stage. The experimental flight times match well with those predicted from the analysis for our vehicle. We achieve a 19% increase in flight time using the batteries in two stages as compared to a single stage.

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arXiv2020

Learning Min-norm Stabilizing Control Laws for Systems with Unknown Dynamics

This paper introduces a framework for learning a minimum-norm stabilizing controller for a system with unknown dynamics using model-free policy optimization methods. The approach begins by first designing a Control Lyapunov Function (CLF) for a (possibly inaccurate) dynamics model for the system, along with a function which specifies a minimum acceptable rate of energy dissipation for the CLF at different points in the state-space. Treating the energy dissipation condition as a constraint on the desired closed-loop behavior of the real-world system, we use penalty methods to formulate an unconstrained optimization problem over the parameters of a learned controller, which can be solved using model-free policy optimization algorithms using data collected from the plant. We discuss when the optimization learns a stabilizing controller for the real world system and derive conditions on the structure of the learned controller which ensure that the optimization is strongly convex, meaning the globally optimal solution can be found reliably. We validate the approach in simulation, first for a double pendulum, and then generalize the framework to learn stable walking controllers for underactuated bipedal robots using the Hybrid Zero Dynamics framework. By encoding a large amount of structure into the learning problem, we are able to learn stabilizing controllers for both systems with only minutes or even seconds of training data.

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arXiv2020

Animated Cassie: A Dynamic Relatable Robotic Character

Creating robots with emotional personalities will transform the usability of robots in the real world. As previous emotive social robots are mostly based on statically stable robots whose mobility is limited, this paper develops an animation to real world pipeline that enables dynamic bipedal robots that can twist, wiggle, and walk to behave with emotions. First, an animation method is introduced to design emotive motions for the virtual robot character. Second, a dynamics optimizer is used to convert the animated motion to dynamically feasible motion. Third, real time standing and walking controllers and an automaton are developed to bring the virtual character to life. This framework is deployed on a bipedal robot Cassie and validated in experiments. To the best of our knowledge, this paper is one of the first to present an animatronic dynamic legged robot that is able to perform motions with desired emotional attributes. We term robots that use dynamic motions to convey emotions as Dynamic Relatable Robotic Characters.

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arXiv2020

Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functions

In this paper, the issue of model uncertainty in safety-critical control is addressed with a data-driven approach. For this purpose, we utilize the structure of an input-ouput linearization controller based on a nominal model along with a Control Barrier Function and Control Lyapunov Function based Quadratic Program (CBF-CLF-QP). Specifically, we propose a novel reinforcement learning framework which learns the model uncertainty present in the CBF and CLF constraints, as well as other control-affine dynamic constraints in the quadratic program. The trained policy is combined with the nominal model-based CBF-CLF-QP, resulting in the Reinforcement Learning-based CBF-CLF-QP (RL-CBF-CLF-QP), which addresses the problem of model uncertainty in the safety constraints. The performance of the proposed method is validated by testing it on an underactuated nonlinear bipedal robot walking on randomly spaced stepping stones with one step preview, obtaining stable and safe walking under model uncertainty.

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arXiv2020

Multiple quadrotors carrying a flexible hose: dynamics, differential flatness and control

Using quadrotors UAVs for cooperative payload transportation using cables has been actively gaining interest in recent years. Understanding the dynamics of these complex multi-agent systems would help towards designing safe and reliable systems. In this work, we study one such multi-agent system comprising of multiple quadrotors transporting a flexible hose. We model the hose as a series of smaller discrete links and derive a generalized coordinate-free dynamics for the same. We show that certain configurations of this under-actuated system are differentially-flat. We linearize the dynamics using variation-based linearization and present a linear time-varying LQR to track desired trajectories. Finally, we present numerical simulations to validate the dynamics, flatness, and control.

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arXiv2020

Improving Input-Output Linearizing Controllers for Bipedal Robots via Reinforcement Learning

The main drawbacks of input-output linearizing controllers are the need for precise dynamics models and not being able to account for input constraints. Model uncertainty is common in almost every robotic application and input saturation is present in every real world system. In this paper, we address both challenges for the specific case of bipedal robot control by the use of reinforcement learning techniques. Taking the structure of a standard input-output linearizing controller, we use an additive learned term that compensates for model uncertainty. Moreover, by adding constraints to the learning problem we manage to boost the performance of the final controller when input limits are present. We demonstrate the effectiveness of the designed framework for different levels of uncertainty on the five-link planar walking robot RABBIT.

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arXiv2020

Geometric L1 Adaptive Attitude Control for a Quadrotor Unmanned Aerial Vehicle

In this paper, we study the quadrotor UAV attitude control on SO(3) in the presence of unknown disturbances and model uncertainties. L1 adaptive control for UAVs using Euler angles/quaternions is shown to exhibit robustness and precise attitude tracking in the presence of disturbances and uncertainties. However, it is well known that dynamical models and controllers that use Euler angle representations are prone to singularities and typically have smaller regions of attraction while quaternion representations are subject to the unwinding phenomenon. To avoid such complexities, we present a Geometric L1 adaptation control law to estimate the uncertainties. A model reference adaptive control approach is implemented, with the attitude errors between the quadrotor model and the reference model defined on the manifold. Control laws for the quadrotor and reference models are developed directly on SO(3) to track the desired trajectory while rejecting the uncertainties. Control Lyapunov function based analysis is used to show the exponential input-to-state stability of the attitude errors. The proposed L1 adaptive controller is validated using numerical simulations. Preliminary experimental results are shown comparing a geometric PD controller to the geometric L1 adaptive controller. Experimental validation of the proposed controller is carried out on an Autel X-star quadrotor.

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arXiv2019

Feedback Control for Autonomous Riding of Hovershoes by a Cassie Bipedal Robot

Motivated towards achieving multi-modal locomotion, in this paper, we develop a framework for a bipedal robot to dynamically ride a pair of Hovershoes over various terrain. Our developed control strategy enables the Cassie bipedal robot to interact with the Hovershoes to balance, regulate forward and rotational velocities, achieve fast turns, and move over flat terrain, slopes, stairs, and rough outdoor terrain. Our sensor suite comprising of tracking and depth cameras for visual SLAM as well as our Dijkstra-based global planner and timed elastic band-based local planning framework enables us to achieve autonomous riding on the Hovershoes while navigating an obstacle course. We present numerical and experimental validations of our work.

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arXiv2019

Control Barrier Functions: Theory and Applications

This paper provides an introduction and overview of recent work on control barrier functions and their use to verify and enforce safety properties in the context of (optimization based) safety-critical controllers. We survey the main technical results and discuss applications to several domains including robotic systems.

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arXiv2018

Feedback Control of an Exoskeleton for Paraplegics: Toward Robustly Stable Hands-free Dynamic Walking

This manuscript presents control of a high-DOF fully actuated lower-limb exoskeleton for paraplegic individuals. The key novelty is the ability for the user to walk without the use of crutches or other external means of stabilization. We harness the power of modern optimization techniques and supervised machine learning to develop a smooth feedback control policy that provides robust velocity regulation and perturbation rejection. Preliminary evaluation of the stability and robustness of the proposed approach is demonstrated through the Gazebo simulation environment. In addition, preliminary experimental results with (complete) paraplegic individuals are included for the previous version of the controller.

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arXiv2017

Deep Visual Perception for Dynamic Walking on Discrete Terrain

Dynamic bipedal walking on discrete terrain, like stepping stones, is a challenging problem requiring feedback controllers to enforce safety-critical constraints. To enforce such constraints in real-world experiments, fast and accurate perception for foothold detection and estimation is needed. In this work, a deep visual perception model is designed to accurately estimate step length of the next step, which serves as input to the feedback controller to enable vision-in-the-loop dynamic walking on discrete terrain. In particular, a custom convolutional neural network architecture is designed and trained to predict step length to the next foothold using a sampled image preview of the upcoming terrain at foot impact. The visual input is offered only at the beginning of each step and is shown to be sufficient for the job of dynamically stepping onto discrete footholds. Through extensive numerical studies, we show that the robot is able to successfully autonomously walk for over 100 steps without failure on a discrete terrain with footholds randomly positioned within a step length range of 45-85 centimeters.

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arXiv2017

Differential-Flatness and Control of Quadrotor(s) with a Payload Suspended through Flexible Cable(s)

We present the coordinate-free dynamics of three different quadrotor systems : (a) single quadrotor with a point-mass payload suspended through a flexible cable; (b) multiple quadrotors with a shared point-mass payload suspended through flexible cables; and (c) multiple quadrotors with a shared rigid-body payload suspended through flexible cables. We model the flexible cable(s) as a finite series of links with spherical joints with mass concentrated at the end of each link. The resulting systems are thus high-dimensional with high degree-of-underactuation. For each of these systems, we show that the dynamics are differentially-flat, enabling planning of dynamically feasible trajectories. For the single quadrotor with a point-mass payload suspended through a flexible cable with five links (16 degrees-of-freedom and 12 degrees-of-underactuation), we use the coordinate-free dynamics to develop a geometric variation-based linearized equations of motion about a desired trajectory. We show that a finite-horizon linear quadratic regulator can be used to track a desired trajectory with a relatively large region of attraction.

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arXiv2013

Torque Saturation in Bipedal Robotic Walking through Control Lyapunov Function Based Quadratic Programs

This paper presents a novel method for directly incorporating user-defined control input saturations into the calculation of a control Lyapunov function (CLF)-based walking controller for a biped robot. Previous work by the authors has demonstrated the effectiveness of CLF controllers for stabilizing periodic gaits for biped walkers, and the current work expands on those results by providing a more effective means for handling control saturations. The new approach, based on a convex optimization routine running at a 1 kHz control update rate, is useful not only for handling torque saturations but also for incorporating a whole family of user-defined constraints into the online computation of a CLF controller. The paper concludes with an experimental implementation of the main results on the bipedal robot MABEL.

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