Autonomous Racing with Multiple Vehicles using a Parallelized Optimization with Safety Guarantee using Control Barrier Functions

He, Suiyi; Zeng, Jun; Sreenath, Koushil

doi:10.1109/icra46639.2022.9811969

Cited by 26 publications

(13 citation statements)

References 31 publications

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“…In this way, several studies have been investigated to ensure safety hence stability as well. Several authors have refocused on AV's safety by introducing Control Barrier Function 'CBF' approach [47][48][49]. It is considered the most effective algorithm to guarantee AV stability and safety, thus solving such AV problems as lane-keeping in [50], obstacle avoidance, and free-collisions in [51].…”

Section: Neural Network-based Controllers: Artificial Neuralmentioning

confidence: 99%

“…In [52], a robust control for lateral dynamics of autonomous vehicles has been developed using Barrier Lyapunov-Function control. Furthermore, the CBF has been successfully combined with MPC and SMC for autonomous surface vehicles in presence of tire forces, road curvature, and parametric uncertainties as reported in [48] and [53] respectively. Due to recent activity in related fields and the necessity of safety associated with autonomous systems, the authors envision control gate functionality to become an integral part of modern control system design.…”

Section: Neural Network-based Controllers: Artificial Neuralmentioning

confidence: 99%

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Autonomous Vehicle Control Systems- State of the Art of Decision-Making and Maneuver execution

Abdallaoui

Ikaouassen

Kribèche

et al. 2023

Preprint

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As self-driving cars perform more tasks, new challenges arise. One of these challenging tasks is autonomous driving decision-making due to the uncertainty of the vehicle’s complex environment. This paper provides an overview of decision-making technology and trajectory control for autonomous vehicles. The main common goal in decision-making is to consider uncertainties, unpredictable situations, and driving tasks to propose a global and robust solution adapted to each situation. The main concern is safety. Decision-making falls into three categories. The first is the traditional approach, which often consists of building a rule system and deriving optimal operations. The advantages of such an approach are well known for being easy to understand and applicable to small problems. The second category of decision-making is based on a probabilistic process and, due to its efficiency, has several applications in this area. The third category is learning-based approaches. Once a decision has been made, manipulate the steering angle or accelerator/brake pedals to perform the appropriate action. Two approaches are existing to designing autonomous driving controllers. Either based on imitating human drivers that includes approaches based on the use of driver models such as AI, or the use of approach-based models

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Section: Neural Network-based Controllers: Artificial Neuralmentioning

confidence: 99%

Section: Neural Network-based Controllers: Artificial Neuralmentioning

confidence: 99%

Autonomous Vehicle Control Systems- State of the Art of Decision-Making and Maneuver execution

Abdallaoui

Ikaouassen

Kribèche

et al. 2023

Preprint

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“…Such constraints in trajectory optimization often makes the problem not scalable and hard to be solved online, especially as the number of robots increases. Therefore, inspired by [32], we introduce a parallelized optimization scheme where we simultaneously run multiple optimizations each with a different combination of the hybrid modes. The potential hybrid mode combination, i.e., binary vector δ ∈ Z n , can be selected from {0, 1} n where 0 represents the slack mode, 1 represents the taut mode, and n is the number of agents.…”

Section: B Parallelized Optimization For Hybrid Mode Switchesmentioning

confidence: 99%

Collaborative Navigation and Manipulation of a Cable-towed Load by Multiple Quadrupedal Robots

Yang¹,

Sue²,

Li³

et al. 2022

Preprint

Self Cite

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This paper tackles the problem of robots collaboratively towing a load with cables to a specified goal location while avoiding collisions in real time. The introduction of cables (as opposed to rigid links) enables the robotic team to travel through narrow spaces by changing its intrinsic dimensions through slack/taut switches of the cable. However, this is a challenging problem because of the hybrid mode switches and the dynamical coupling among multiple robots and the load. Previous attempts at addressing such a problem were performed offline and do not consider avoiding obstacles online. In this paper, we introduce a cascaded planning scheme with a parallelized centralized trajectory optimization that deals with hybrid mode switches. We additionally develop a set of decentralized planners per robot, which enables our approach to solve the problem of collaborative load manipulation online. We develop and demonstrate one of the first collaborative autonomy framework that is able to move a cable-towed load, which is too heavy to move by a single robot, through narrow spaces with real-time feedback and reactive planning in experiments.

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“…While CBFs have shown great promise in a variety of applications including bipedal locomotion [7], autonomous driving [8], and spacecraft rendezvous [9], challenges remain. One practical difficulty is that there may not be a control input to satisfy the barrier function constraint at each time instant, which may lead to safety violations.…”

Section: Introductionmentioning

confidence: 99%

Verification and Synthesis of Control Barrier Functions

Clark

2021

2021 60th IEEE Conference on Decision and Control (CDC)

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Safety is a critical property for control systems in medicine, transportation, manufacturing, and other applications, and can be defined as ensuring positive invariance of a predefined safe set. One approach to ensuring safety is to construct a Control Barrier Function (CBF) such that the system is safe when the CBF is nonnegative, and then choose a control input to ensure nonnegativity of the CBF at each time step, provided that such an input exists. This paper investigates the problems of verification and synthesis of control barrier functions. In the area of verification, we use the algebraic-geometric Positivstellensatz to construct convex equivalent conditions for a CBF to ensure safety. We extend these conditions to control policies based on unions and intersections of CBFs, highorder CBFs, and systems with trigonometric dynamics, actuation constraints, and Euler-Lagrange dynamics. In the area of synthesis, we propose algorithms for constructing CBFs, namely, an alternating-descent approach, a local CBF approach, and a Newton's method approach. We prove that the latter algorithm guarantees local convergence to a CBF at a quadratic rate.

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Autonomous Racing with Multiple Vehicles using a Parallelized Optimization with Safety Guarantee using Control Barrier Functions

Cited by 26 publications

References 31 publications

Autonomous Vehicle Control Systems- State of the Art of Decision-Making and Maneuver execution

Autonomous Vehicle Control Systems- State of the Art of Decision-Making and Maneuver execution

Collaborative Navigation and Manipulation of a Cable-towed Load by Multiple Quadrupedal Robots

Verification and Synthesis of Control Barrier Functions

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