19 research outputs found
Multi-objective Compositions for Collision-Free Connectivity Maintenance in Teams of Mobile Robots
Compositional barrier functions are proposed in this paper to systematically
compose multiple objectives for teams of mobile robots. The objectives are
first encoded as barrier functions, and then composed using AND and OR logical
operators. The advantage of this approach is that compositional barrier
functions can provably guarantee the simultaneous satisfaction of all composed
objectives. The compositional barrier functions are applied to the example of
ensuring collision avoidance and static/dynamical graph connectivity of teams
of mobile robots. The resulting composite safety and connectivity barrier
certificates are verified experimentally on a team of four mobile robots.Comment: To appear in 55th IEEE Conference on Decision and Control, December
12-14, 2016, Las Vegas, NV, US
Multi-objective compositions for collision-free connectivity maintenance in teams of mobile robots
Compositional barrier functions are proposed in this paper to systematically compose multiple objectives for teams of mobile robots. The objectives are first encoded as barrier functions, and then composed using AND and OR logical operators. The advantage of this approach is that compositional barrier functions can provably guarantee the simultaneous satisfaction of all composed objectives. The compositional barrier functions are applied to the example of ensuring collision avoidance and static/dynamical graph connectivity of teams of mobile robots. The resulting composite safety and connectivity barrier certificates are verified experimentally on a team of four mobile robots
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
Integral Control Barrier Functions for Dynamically Defined Control Laws
This letter introduces integral control barrier functions (I-CBFs) as a means to enable the safety-critical integral control of nonlinear systems. Importantly, I-CBFs allow for the holistic encoding of both state constraints and input bounds in a single framework. We demonstrate this by applying them to a dynamically defined tracking controller, thereby enforcing safety in state and input through a minimally invasive I-CBF controller framed as a quadratic program
Safe certificate-based maneuvers for teams of quadrotors using differential flatness
Safety Barrier Certificates that ensure collision-free maneuvers for teams of differential flatness-based quadrotors are presented in this paper. Synthesized with control barrier functions, the certificates are used to modify the nominal trajectory in a minimally invasive way to avoid collisions. The proposed collision avoidance strategy complements existing flight control and planning algorithms by providing trajectory modifications with provable safety guarantees. The effectiveness of this strategy is supported both by the theoretical results and experimental validation on a team of five quadrotors