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Found 35 papers, 8 papers with code
Reinforcement Learning-based Receding Horizon Control using Adaptive Control Barrier Functions for Safety-Critical Systems
Unfortunately, both performance and solution feasibility can be significantly impacted by two key factors: (i) the selection of the cost function and associated parameters, and (ii) the calibration of parameters within the CBF-based constraints, which capture the trade-off between performance and conservativeness.
Spline Trajectory Tracking and Obstacle Avoidance for Mobile Agents via Convex Optimization
We propose an output feedback control-based motion planning technique for agents to enable them to converge to a specified polynomial trajectory while imposing a set of safety constraints on our controller to avoid collisions within the free configuration space (polygonal environment).
Performance-Guaranteed Solutions for Multi-Agent Optimal Coverage Problems using Submodularity, Curvature, and Greedy Algorithms
We consider a class of multi-agent optimal coverage problems in which the goal is to determine the optimal placement of a group of agents in a given mission space so that they maximize a coverage objective that represents a blend of individual and collaborative event detection capabilities.
Optimal Sequencing and Motion Control in a Roundabout with Safety Guarantees
This paper develops a controller for Connected and Automated Vehicles (CAVs) traversing a single-lane roundabout.
Secure Control of Connected and Automated Vehicles Using Trust-Aware Robust Event-Triggered Control Barrier Functions
We address the security of a network of Connected and Automated Vehicles (CAVs) cooperating to safely navigate through a conflict area (e. g., traffic intersections, merging roadways, roundabouts).
Safe Optimal Interactions Between Automated and Human-Driven Vehicles in Mixed Traffic with Event-triggered Control Barrier Functions
This paper studies safe driving interactions between Human-Driven Vehicles (HDVs) and Connected and Automated Vehicles (CAVs) in mixed traffic where the dynamics and control policies of HDVs are unknown and hard to predict.
Scaling up the Optimal Safe Control of Connected and Automated Vehicles to a Traffic Network: A Hierarchical Framework of Modular Control Zones
We consider the problem of scaling up optimal and safe controllers for Connected and Automated Vehicles (CAVs) from a single Control Zone (CZ) around a traffic conflict area to an entire network.
Maximizing Safety and Efficiency for Cooperative Lane-Changing: A Minimally Disruptive Approach
This paper addresses cooperative lane-changing maneuvers in mixed traffic, aiming to minimize traffic flow disruptions while accounting for uncooperative vehicles.
Merging control in mixed traffic with safety guarantees: a safe sequencing policy with optimal motion control
We address the problem of merging traffic from two roadways consisting of both Connected Autonomous Vehicles (CAVs) and Human Driven Vehicles (HDVs).
Trust-Aware Resilient Control and Coordination of Connected and Automated Vehicles
We address the security of a network of Connected and Automated Vehicles (CAVs) cooperating to navigate through a conflict area.
Scalable Adaptive Traffic Light Control Over a Traffic Network Including Transit Delays
We study the Traffic Light Control (TLC) problem for a traffic network with multiple intersections in an artery, including the effect of transit delays for vehicles moving from one intersection to the next.
Cooperative Lane Changing in Mixed Traffic can be Robust to Human Driver Behavior
We derive time and energy-optimal control policies for a Connected Autonomous Vehicle (CAV) to complete lane change maneuvers in mixed traffic.
Adaptive Traffic Light Control for Competing Vehicle and Pedestrian Flows
We study the Traffic Light Control (TLC) problem for a single intersection, considering both straight driving vehicle flows and corresponding crossing pedestrian flows with the goal of achieving a fair jointly optimal sharing policy in terms of average waiting times.
Learning Feasibility Constraints for Control Barrier Functions
It has been shown that optimizing quadratic costs while stabilizing affine control systems to desired (sets of) states subject to state and control constraints can be reduced to a sequence of Quadratic Programs (QPs) by using Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs).
Optimal Transport Perturbations for Safe Reinforcement Learning with Robustness Guarantees
Robustness and safety are critical for the trustworthy deployment of deep reinforcement learning.
Optimal Merging Control of an Autonomous Vehicle in Mixed Traffic: an Optimal Index Policy
We derive an optimal index policy which prescribes the merging position of the AV within the group of HDVs.
Optimal Control of Connected Automated Vehicles with Event/Self-Triggered Control Barrier Functions
We address the problem of controlling Connected and Automated Vehicles (CAVs) in conflict areas of a traffic network subject to hard safety constraints.
Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse
Data-driven, learning-based control methods offer the potential to improve operations in complex systems, and model-free deep reinforcement learning represents a popular approach to data-driven control.
Sequential Cooperative Energy and Time-Optimal Lane Change Maneuvers for Highway Traffic
We derive optimal control policies for a Connected Automated Vehicle (CAV) and cooperating neighboring CAVs to carry out a lane change maneuver consisting of a longitudinal phase where the CAV properly positions itself relative to the cooperating neighbors and a lateral phase where it safely changes lanes.
Self-Triggered Coordination Control of Connected Automated Vehicles in Traffic Networks
The safety constraints and the vehicle limitations are considered using the Control Barrier Function (CBF) framework and a self-triggered scheme is proposed using the CBF constraints.
Optimal Control of Connected Automated Vehicles with Event-Triggered Control Barrier Functions
We address the problem of controlling Connected and Automated Vehicles (CAVs) in conflict areas of a traffic network subject to hard safety constraints.
Feasibility Guaranteed Traffic Merging Control Using Control Barrier Functions
We consider the merging control problem for Connected and Automated Vehicles (CAVs) aiming to jointly minimize travel time and energy consumption while providing speed-dependent safety guarantees and satisfying velocity and acceleration constraints.
Minimax Multi-Agent Persistent Monitoring of a Network System
For a given visiting sequence, we prove that in an optimal dwelling time allocation the peak uncertainty is the same among all the targets.
Generalized Proximal Policy Optimization with Sample Reuse
In real-world decision making tasks, it is critical for data-driven reinforcement learning methods to be both stable and sample efficient.
Decentralized Time and Energy-Optimal Control of Connected and Automated Vehicles in a Roundabout
The paper considers the problem of controlling Connected and Automated Vehicles (CAVs) traveling through a three-entry roundabout so as to jointly minimize both the travel time and the energy consumption while providing speed-dependent safety guarantees, as well as satisfying velocity and acceleration constraints.
A Semidefinite Programming Approach to Discrete-time Infinite Horizon Persistent Monitoring
We investigate the problem of persistent monitoring, where a mobile agent has to survey multiple targets in an environment in order to estimate their internal states.
Event-Triggered Safety-Critical Control for Systems with Unknown Dynamics
We define a HOCBF for a safety requirement on the unmodelled system based on the adaptive dynamics and error states, and reformulate the safety-critical control problem as the above mentioned QP.
Event-Driven Receding Horizon Control of Energy-Aware Dynamic Agents For Distributed Persistent Monitoring
This paper addresses the persistent monitoring problem defined on a network where a set of nodes (targets) needs to be monitored by a team of dynamic energy-aware agents.
Uncertainty-Aware Policy Optimization: A Robust, Adaptive Trust Region Approach
In order for reinforcement learning techniques to be useful in real-world decision making processes, they must be able to produce robust performance from limited data.
Event-Driven Receding Horizon Control for Distributed Estimation in Network Systems
We consider the problem of estimating the states of a distributed network of nodes (targets) through a team of cooperating agents (sensors) persistently visiting the nodes so that an overall measure of estimation error covariance evaluated over a finite period is minimized.
Explainability of Intelligent Transportation Systems using Knowledge Compilation: a Traffic Light Controller Case
Usage of automated controllers which make decisions on an environment are widespread and are often based on black-box models.
Feasibility-Guided Learning for Robust Control in Constrained Optimal Control Problems
In this paper, we further improve the feasibility robustness (i. e., feasibility maintenance in the presence of time-varying and unknown unsafe sets) through the definition of a High Order CBF (HOCBF) that works for arbitrary relative degree constraints; this is achieved by a proposed feasibility-guided learning approach.
Data-driven Estimation of Origin-Destination Demand and User Cost Functions for the Optimization of Transportation Networks
In earlier work (Zhang et al., 2016) we used actual traffic data from the Eastern Massachusetts transportation network in the form of spatial average speeds and road segment flow capacities in order to estimate Origin-Destination (OD) flow demand matrices for the network.
Systems and Control 90B06
Optimal control for a robotic exploration, pick-up and delivery problem
This paper addresses an optimal control problem for a robot that has to find and collect a finite number of objects and move them to a depot in minimum time.
Network Anomaly Detection: A Survey and Comparative Analysis of Stochastic and Deterministic Methods
We present five methods to the problem of network anomaly detection.
