2,201 research outputs found
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
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
Safe Whole-Body Task Space Control for Humanoid Robots
Complex robotic systems require whole-body controllers to deal with contact
interactions, handle closed kinematic chains, and track task-space control
objectives. However, for many applications, safety-critical controllers are
important to steer away from undesired robot configurations to prevent unsafe
behaviors. A prime example is legged robotics, where we can have tasks such as
balance control, regulation of torso orientation, and, most importantly,
walking. As the coordination of multi-body systems is non-trivial, following a
combination of those tasks might lead to configurations that are deemed
dangerous, such as stepping on its support foot during walking, leaning the
torso excessively, or producing excessive centroidal momentum, resulting in
non-human-like walking. To address these challenges, we propose a formulation
of an inverse dynamics control enhanced with exponential control barrier
functions for robotic systems with numerous degrees of freedom. Our approach
utilizes a quadratic program that respects closed kinematic chains, minimizes
the control objectives, and imposes desired constraints on the Zero Moment
Point, friction cone, and torque. More importantly, it also ensures the forward
invariance of a general user-defined high Relative-Degree safety set. We
demonstrate the effectiveness of our method by applying it to the 3D biped
robot Digit, both in simulation and with hardware experiments.Comment: 8 pages, 12 figure
Risk-Sensitive Path Planning via CVaR Barrier Functions: Application to Bipedal Locomotion
Enforcing safety of robotic systems in the presence of stochastic uncertainty is a challenging problem. Traditionally,researchers have proposed safety in the statistical mean as a safety measure in this case. However, ensuring safety in the statistical mean is only reasonable if robot safe behavior in the large number of runs is of interest, which precludes the use of mean safety in practical scenarios. In this paper, we propose a risk sensitive notion of safety called conditional-value-at-risk (CVaR) safety, which is concerned with safe performance in the worst case realizations. We introduce CVaR barrier functions asa tool to enforce CVaR safety and propose conditions for their Boolean compositions. Given a legacy controller, we show that we can design a minimally interfering CVaR safe controller via solving difference convex programs. We elucidate the proposed method by applying it to a bipedal locomotion case study
Impact-Aware Online Motion Planning for Fully-Actuated Bipedal Robot Walking
The ability to track a general walking path with specific timing is crucial
to the operational safety and reliability of bipedal robots for avoiding
dynamic obstacles, such as pedestrians, in complex environments. This paper
introduces an online, full-body motion planner that generates the desired
impact-aware motion for fully-actuated bipedal robotic walking. The main
novelty of the proposed planner lies in its capability of producing desired
motions in real-time that respect the discrete impact dynamics and the desired
impact timing. To derive the proposed planner, a full-order hybrid dynamic
model of fully-actuated bipedal robotic walking is presented, including both
continuous dynamics and discrete lading impacts. Next, the proposed
impact-aware online motion planner is introduced. Finally, simulation results
of a 3-D bipedal robot are provided to confirm the effectiveness of the
proposed online impact-aware planner. The online planner is capable of
generating full-body motion of one walking step within 0.6 second, which is
shorter than a typical bipedal walking step
Safe Learning of Quadrotor Dynamics Using Barrier Certificates
To effectively control complex dynamical systems, accurate nonlinear models
are typically needed. However, these models are not always known. In this
paper, we present a data-driven approach based on Gaussian processes that
learns models of quadrotors operating in partially unknown environments. What
makes this challenging is that if the learning process is not carefully
controlled, the system will go unstable, i.e., the quadcopter will crash. To
this end, barrier certificates are employed for safe learning. The barrier
certificates establish a non-conservative forward invariant safe region, in
which high probability safety guarantees are provided based on the statistics
of the Gaussian Process. A learning controller is designed to efficiently
explore those uncertain states and expand the barrier certified safe region
based on an adaptive sampling scheme. In addition, a recursive Gaussian Process
prediction method is developed to learn the complex quadrotor dynamics in
real-time. Simulation results are provided to demonstrate the effectiveness of
the proposed approach.Comment: Submitted to ICRA 2018, 8 page
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