Safety of Dynamical Systems With Multiple Non-Convex Unsafe Sets Using Control Barrier Functions

@article{Notomista2021SafetyOD,
  title={Safety of Dynamical Systems With Multiple Non-Convex Unsafe Sets Using Control Barrier Functions},
  author={Gennaro Notomista and Matteo Saveriano},
  journal={IEEE Control Systems Letters},
  year={2021},
  volume={6},
  pages={1136-1141}
}
This letter presents an approach to deal with safety of dynamical systems in presence of multiple non-convex unsafe sets. While optimal control and model predictive control strategies can be employed in these scenarios, they suffer from high computational complexity in case of general nonlinear systems. Leveraging control barrier functions, on the other hand, results in computationally efficient control algorithms. Nevertheless, when safety guarantees have to be enforced alongside stability… Expand

Figures from this paper

References

SHOWING 1-10 OF 18 REFERENCES
Control Barrier Function-Based Quadratic Programs Introduce Undesirable Asymptotically Stable Equilibria
TLDR
This letter proposes an extension to the QP-based controller unifying CLFs and CBFs such that the resulting system trajectories avoid the undesirable equilibria problem on the boundary of the safe set. Expand
Control Barrier Function Based Quadratic Programs for Safety Critical Systems
TLDR
This paper develops a methodology that allows safety conditions—expression as control barrier functions—to be unified with performance objectives—expressed as control Lyapunov functions—in the context of real-time optimization-based controllers. Expand
Learning Barrier Functions for Constrained Motion Planning with Dynamical Systems
  • Matteo Saveriano, Dongheui Lee
  • Computer Science, Engineering
  • 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • 2019
TLDR
This work presents a novel approach to learn workspace constraints from human demonstrations and to generate motion trajectories for the robot that lie in the constrained workspace by considering the learned constraint subspaces as zeroing barrier functions. Expand
Lyapunov Design for Safe Reinforcement Learning
TLDR
This work proposes a method for constructing safe, reliable reinforcement learning agents based on Lyapunov design principles that ensures qualitatively satisfactory agent behavior for virtually any reinforcement learning algorithm and at all times, including while the agent is learning and taking exploratory actions. Expand
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks
TLDR
This work proposes a controller architecture that combines a model-free RL-based controller with model-based controllers utilizing control barrier functions (CBFs) and on-line learning of the unknown system dynamics, in order to ensure safety during learning. Expand
Provably safe and robust learning-based model predictive control
TLDR
A learning-based model predictive control scheme that provides deterministic guarantees on robustness, while statistical identification tools are used to identify richer models of the system in order to improve performance. Expand
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)Expand
Reactive collision avoidance for ASVs based on control barrier functions
TLDR
A reactive collision avoidance method for autonomous surface vehicles based on control barrier functions (CBFs) that is seen to handle head-on, overtaking and crossing situations with both give-way and stand-on duty in compliance with COLREGs rules 13-15 and 17. Expand
Real-time motion planning for agile autonomous vehicles
Planning the path of an autonomous, agile vehicle in a dynamic environment is a very complex problem, especially when the vehicle is required to use its full maneuvering capabilities. Recent effortsExpand
The construction of analytic diffeomorphisms for exact robot navigation on star worlds
  • E. Rimon, D. Koditschek
  • Computer Science, Mathematics
  • Proceedings, 1989 International Conference on Robotics and Automation
  • 1989
TLDR
A general methodology is described which extends the construction of navigation functions on sphere worlds to any smoothly deformable space and yields automatically a bounded torque feedback control law which is guaranteed to guide the robot to destination point from almost every initial position without hitting any obstacle. Expand
...
1
2
...