Provably-Safe Autonomous Navigation of Traffic Circles

Konda, Rohit; Squires, Eric; Pierpaoli, Pietro; Egerstedt, Magnus; Coogan, Samuel

doi:10.1109/ccta.2019.8920597

Cited by 6 publications

(7 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…for any Lipschitz continuous baseline controller u 0 (x) and u given in (9) enforces the forward invariance of the set  in (3) for the delay-free system (2) as established in corollary 2 of Ames et al 2…”

Section: Definition 1 (Control Barrier Function (Cbf)) a Continuously...mentioning

confidence: 92%

“…One increasingly popular approach for this problem is the use of what is now referred to as Control Barrier Functions (CBFs) [1][2][3][4][5][6] and in the preceding times was referred to as "non-overshooting control". 7 Like Lyapunov functions for stabilization, CBFs provide sufficient conditions on system input for enforcing forward invariance of a safe-set, and they have been used in various applications ranging from safe navigation 8,9 to infectious disease control. 10,11 As interest in CBFs have developed over time, several extensions have emerged that render CBFs applicable to systems of different forms.…”

Section: Background and Motivationmentioning

confidence: 99%

See 1 more Smart Citation

Constrained control of multi‐input systems with distinct input delays

Abel,

Janković,

Krstić

2024

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

We consider the problem of enforcing safety in multi‐input systems with distinct input delays via the use of Control Barrier Functions (CBFs). For systems with input delay(s), a popular approach for enforcing safety is the combination of a CBF designed for the delay‐free system with state‐predictors that compensate the input delays. Typically, this comes with the assumption that the system does not violate safety constraints before all input delays have been compensated and the system is fully under control. In this paper, we introduce two control approaches that enforce safety before all input delays have been compensated, whenever it is possible to do so. We do this by utilizing a robust CBF formulation that treats longer‐delayed inputs as known disturbances when determining control effort for shorter delayed inputs. This formulation ensures that, whenever possible, a subset of input channels with shorter delays will be utilized for keeping the system in the admissible safe set before longer input delays have been compensated. The effectiveness of our approaches is demonstrated with two numerical examples.

show abstract

Section: Definition 1 (Control Barrier Function (Cbf)) a Continuously...mentioning

confidence: 92%

Section: Background and Motivationmentioning

confidence: 99%

Constrained control of multi‐input systems with distinct input delays

Abel,

Janković,

Krstić

2024

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…The authors in [9] combine control barrier functions with Lyapunov control functions via quadratic programs and introduce new classes of barrier functions to construct safe controllers. The framework of control barrier functions is applied to the problem of autonomous navigation of traffic circles in [10]. In [11], the authors deal with the problem of human-robot interaction, specifically confronting safety issues via a dynamic invariance control framework.…”

Section: Related Workmentioning

confidence: 99%

“…In (10), the constrained state x can be written with respect to the unconstrained state s " rs 1 , . .…”

Section: Sub-problem Tracking a Certain Trajectorymentioning

confidence: 99%

Temporal-Logic-Based Intermittent, Optimal, and Safe Continuous-Time Learning for Trajectory Tracking

Kanellopoulos,

Fotiadis,

Sun

et al. 2021

Preprint

View full text Add to dashboard Cite

In this paper, we develop safe reinforcementlearning-based controllers for systems tasked with accomplishing complex missions that can be expressed as linear temporal logic specifications, similar to those required by search-andrescue missions. We decompose the original mission into a sequence of tracking sub-problems under safety constraints. We impose the safety conditions by utilizing barrier functions to map the constrained optimal tracking problem in the physical space to an unconstrained one in the transformed space. Furthermore, we develop policies that intermittently update the control signal to solve the tracking sub-problems with reduced burden in the communication and computation resources. Subsequently, an actor-critic algorithm is utilized to solve the underlying Hamilton-Jacobi-Bellman equations. Finally, we support our proposed framework with stability proofs and showcase its efficacy via simulation results.

show abstract

“…A second advance is to use machine learning techniques to evolve high-performing BTs for environments that can be simulated (Banerjee, 2018;Colledanchise, Parasuraman, &Ögren, 2019;Zhang, Yao, Yin, & Zha, 2018). Variants include safe learning algorithms that avoid potentially harmful states during training, e.g., by restricting controls to those that avoid disallowed states (Sprague &Ögren, 2018; for related work on safe navigation of traffic circles by autonomous vehicles, see Konda, Squires, Pierpaoli, Egerstedt, & Coogan, 2019). Safe learning of BTs or other controllers is especially valuable for robots that must learn by interacting with the real world.…”

Section: Behavior Trees (Bts) Enable Quick Responses To Unexpected Evmentioning

confidence: 99%

Answerable and Unanswerable Questions in Risk Analysis with Open‐World Novelty

Cox

2020

Risk Analysis

View full text Add to dashboard Cite

Decision analysis and risk analysis have grown up around a set of organizing questions: what might go wrong, how likely is it to do so, how bad might the consequences be, what should be done to maximize expected utility and minimize expected loss or regret, and how large are the remaining risks? In probabilistic causal models capable of representing unpredictable and novel events, probabilities for what will happen, and even what is possible, cannot necessarily be determined in advance. Standard decision and risk analysis questions become inherently unanswerable ("undecidable") for realistically complex causal systems with "open-world" uncertainties about what exists, what can happen, what other agents know, and how they will act. Recent artificial intelligence (AI) techniques enable agents (e.g., robots, drone swarms, and automatic controllers) to learn, plan, and act effectively despite open-world uncertainties in a host of practical applications, from robotics and autonomous vehicles to industrial engineering, transportation and logistics automation, and industrial process control. This article offers an AI/machine learning perspective on recent ideas for making decision and risk analysis (even) more useful. It reviews undecidability results and recent principles and methods for enabling intelligent agents to learn what works and how to complete useful tasks, adjust plans as needed, and achieve multiple goals safely and reasonably efficiently when possible, despite open-world uncertainties and unpredictable events. In the near future, these principles could contribute to the formulation and effective implementation of more effective plans and policies in business, regulation, and public policy, as well as in engineering, disaster management, and military and civil defense operations. They can extend traditional decision and risk analysis to deal more successfully with open-world novelty and unpredictable events in large-scale real-world planning, policymaking, and risk management.

show abstract

Provably-Safe Autonomous Navigation of Traffic Circles

Cited by 6 publications

References 23 publications

Constrained control of multi‐input systems with distinct input delays

Constrained control of multi‐input systems with distinct input delays

Temporal-Logic-Based Intermittent, Optimal, and Safe Continuous-Time Learning for Trajectory Tracking

Answerable and Unanswerable Questions in Risk Analysis with Open‐World Novelty

Contact Info

Product

Resources

About