arXiv subjects

Hossein Rastgoftar

Publications and source records attributed to Hossein Rastgoftar.

At least 37 records · Page 2Linked to original sources

arXiv2024

Multi-Agent Coordination Fluid Flow Modeling and Experimental Evaluation

Reliability is a critical aspect of multi-agent system coordination as it ensures that the system functions correctly and consistently. If one agent in the system fails or behaves unexpectedly, it can negatively impact the performance and effectiveness of the entire system. Therefore, it is important to design and implement multi-agent systems with a high level of reliability to ensure that they can operate safely and move smoothly in the presence of unforeseen agent failure or lack of communication with some agent teams moving in a shared motion space. This paper presents a novel fluid flow navigation model that, in an ideal fluid flow, divides agents into cooperative (non-singular) and noncooperative (singular) agents, with cooperative agents sliding along streamlines safely enclosing noncooperative agents in a shared motion space. A series of flight experiments utilizing crazyflie quadcopters will experimentally validate the suggested model.

eess.SY↗

arXiv2024

Sandwich Approach for Motion Planning and Control

This paper develops a new approach for robot motion planning and control in obstacle-laden environments that is inspired by fundamentals of fluid mechanics. For motion planning, we propose a novel transformation between motion space, with arbitrary obstacles of random sizes and shapes, and an obstacle-free planning space with geodesically-varying distances and constrained transitions. We then obtain robot desired trajectory by A* searching over a uniform grid distributed over the planning space. We show that implementing the A* search over the planning space can generate shorter paths when compared to the existing A* searching over the motion space. For trajectory tracking, we propose an MPC-based trajectory tracking control, with linear equality and inequality safety constraints, enforcing the safety requirements of planning and control.

cs.RO↗

arXiv2023

Online Equitable Ride-sharing Car Distribution in a Congested Traffic

The usability of ride-sharing services like Uber and Lyft has been considerably improved by advancements in cellular communications. Such a tech-driven transportation system can reduce the number of private cars, in roads with limited physical capacity, effectively match drivers with passengers requesting service, and save drivers and passengers time by optimal scheduling and estimating of the trips. However, the existing services offered by ride-sharing companies may not necessarily reduce congestion, if they are not equitably accessed in urban areas. This paper addresses this important issue by classifying cars as ride-sharing and non-ride-sharing cars and developing a novel dissensus-based traffic evolution dynamics to effectively model their distribution in a shared network of interconnected roads (NOIR). This new dynamics models the ride-sharing car evolution as a non-stationary Markov process with uncertainty matrices decomposed into outflow and tendency probability matrices, where outflow probabilities are constrained by the non-ride-sharing cars behavior, but the tendency probabilities are considered as decision variables (or distributed controls) for achieving an equitable distribution of ride-sharing cars in a congested traffic.

eess.SY↗

arXiv2023

Deep and Decentralized Multi-Agent Coverage of a Target with Unknown Distribution

This paper proposes a new architecture for multi-agent systems to cover an unknowingly distributed fast, safely, and decentralizedly. The inter-agent communication is organized by a directed graph with fixed topology, and we model agent coordination as a decentralized leader-follower problem with time-varying communication weights. Given this problem setting, we first present a method for converting communication graph into a neural network, where an agent can be represented by a unique node of the communication graph but multiple neurons of the corresponding neural network. We then apply a mass-cetric strategy to train time-varying communication weights of the neural network in a decentralized fashion which in turn implies that the observation zone of every follower agent is independently assigned by the follower based on positions of in-neighbors. By training the neural network, we can ensure safe and decentralized multi-agent coordination of coverage control. Despite the target is unknown to the agent team, we provide a proof for convergence of the proposed multi-agent coverage method.

eess.SY↗

arXiv2023

Deep Continuum Deformation Coordination and Optimization with Safety Guarantees

In this paper, we develop and present a novel strategy for safe coordination of a large-scale multi-agent team with ``\textit{local deformation}" capabilities. Multi-agent coordination is defined by our proposed method as a multi-layer deformation problem specified as a Deep Neural Network (DNN) optimization problem. The proposed DNN consists of $p$ hidden layers, each of which contains artificial neurons representing unique agents. Furthermore, based on the desired positions of the agents of hidden layer $k$ ($k=1,\cdots,p-1$), the desired deformation of the agents of hidden layer $k + 1$ is planned. In contrast to the available neural network learning problems, our proposed neural network optimization receives time-invariant reference positions of the boundary agents as inputs and trains the weights based on the desired trajectory of the agent team configuration, where the weights are constrained by certain lower and upper bounds to ensure inter-agent collision avoidance. We simulate and provide the results of a large-scale quadcopter team coordination tracking a desired elliptical trajectory to validate the proposed approach.

cs.MA↗

arXiv2023

Multi-Layer Continuum Deformation Optimization of Multi-Agent Systems

This paper studies the problem of safe and optimal continuum deformation of a large-scale multi-agent system (MAS). We present a novel approach for MAS continuum deformation coordination that aims to achieve safe and efficient agent movement using a leader-follower multi-layer hierarchical optimization framework with a single input layer, multiple hidden layers, and a single output layer. The input layer receives the reference (material) positions of the primary leaders, the hidden layers compute the desired positions of the interior leader agents and followers, and the output layer computes the nominal position of the MAS configuration. By introducing a lower bound on the major principles of the strain field of the MAS deformation, we obtain linear inequality safety constraints and ensure inter-agent collision avoidance. The continuum deformation optimization is formulated as a quadratic programming problem. It consists of the following components: (i) decision variables that represent the weights in the first hidden layer; (ii) a quadratic cost function that penalizes deviation of the nominal MAS trajectory from the desired MAS trajectory; and (iii) inequality safety constraints that ensure inter-agent collision avoidance. To validate the proposed approach, we simulate and present the results of continuum deformation on a large-scale quadcopter team tracking a desired helix trajectory, demonstrating improvements in safety and efficiency.

cs.MA↗

arXiv2023

Can a Laplace PDE Define Air Corridors through Low-Altitude Airspace?

This paper develops a high-density air corridor traffic flow model for Uncrewed Aircraft System (UAS) operation in urban low altitude airspace. To maximize throughput with safe separation guarantees, we define an airspace spatiotemporal planning problem. For the spatial planning, we propose a multi-floor UAS coordination structure divided into a finite number of air corridors safely wrapping buildings and obstacles. We use the USGS Lidar data to map buildings and in turn generate air corridors by modeling UAS coordination as ideal fluid flow with the streamlines obtained by solving the Laplace partial differential equation (PDE). Proper boundary conditions for the differential equations are imposed to direct air corridors along the floors desired motion direction. For temporal planning, we use 4-dimensional path-finding through the corridor network with A* search to maximize airspace usability given each UAS initial and destination waypoint pair.

eess.SY↗

arXiv2022

Quadcopter Tracking Using Euler-Angle-Free Flatness-Based Control

Quadcopter trajectory tracking control has been extensively investigated and implemented in the past. Available controls mostly use the Euler angle standards to describe the quadcopters rotational kinematics and dynamics. As a result, the same rotation can be translated into different roll, pitch, and yaw angles because there are multiple Euler angle standards for characterization of rotation in a 3-dimensional motion space. Additionally, it is computationally expensive to convert a quadcopters orientation to the associated roll, pitch, and yaw angles, which may make it difficult to track quick and aggressive trajectories. To address these issues, this paper will develop a flatness-based trajectory tracking control without using Euler angles. We assess and test the proposed controls performance in the Gazebo simulation environment and contrast its functionality with the existing Mellinger controller, which has been widely adopted by the robotics and unmanned aerial system (UAS) communities.

cs.RO↗

arXiv2022

Fast and Safe Aerial Payload Transport in Urban Areas

This paper studies the problem of fast and safe aerial payload transport by a single quadcopter in urban areas. The quadcopter payload system (QPS) is considered as a rigid body and modeled with a nonlinear dynamics. The urban area is modeled as an obstacle-laden environment with obstacle geometries obtained by incorporating realistic LIDAR data. Our approach for payload transport is decomposed into high-level motion planning and low-level trajectory control. For the low-level trajectory tracking, a feedback linearization control is applied to stably track the desired trajectory of the quadcopter. For high-level motion planning, we integrate A* search and polynomial planning to define a safe trajectory for the quadcopter assuring collision avoidance, boundedness of the quadcopter rotor speeds and tracking error, and fast arrival to a target destination from an arbitrary initial location.

cs.RO↗

arXiv2022

A Finite-State Fixed-Corridor Model for UAS Traffic Management

This paper proposes a physics-inspired solution for low altitude Unmanned Aircraft System (UAS) Traffic Management (UTM) in urban areas. We decompose UTM into spatial and temporal planning problems. For the spatial planning problem, we use the principles of Eulerian continuum mechanics to safely and optimally allocate finite airspace to a UAS. To this end, the finite airspace is partitioned into planned and unplanned subspaces with unplanned subspace(s) or zone(s) enclosing buildings and restricted no-fly regions. The planned subspace is divided into navigable channels that safely wrap unplanned zone(s). We model the airspace planning problem as a Markov Decision Process (MDP) with states defined based on spatial and temporal airspace features and actions authorizing transitions between safe navigable channels. We apply the proposed traffic management solution to plan safe coordination of small UAS in the airspace above downtown Tucson, Arizona.

eess.SY↗

arXiv2022

A Concurrent Switching Model for Traffic Congestion Control

We introduce a new conservation-based approach for traffic coordination modeling and control in a network of interconnected roads (NOIR) with switching movement phase rotations at every NOIR junction. For modeling of traffic evolution, we first assume that the movement phase rotation is cyclic at every NOIR junction, but the duration of each movement phase can be arbitrarily commanded by traffic signals. Then, we propose a novel concurrent switching dynamics (CSD) with deterministic transitions among a finite number of states, representing the NOIR movement phases. We define the CSD control as a cyclic receding horizon optimization problem with periodic quadratic cost and constraints. More specifically, the cost is defined so that the traffic density is minimized and the boundary inflow is uniformly distributed over the boundary inlet roads, whereas the cost parameters are periodically changed with time. The constraints are linear and imposed by a trapezoidal fundamental diagram at every NOIR road so that traffic feasibility is assured and traffic over-saturation is avoided. The success of the proposed traffic boundary control is demonstrated by simulation of traffic congestion control in Downtown Phoenix.

eess.SY↗

arXiv2022

Quadrotor Formation Flying Resilient to Abrupt Vehicle Failures via a Fluid Flow Navigation Function

This paper develops and experimentally evaluates a navigation function for quadrotor formation flight that is resilient to abrupt quadrotor failures and other obstacles. The navigation function is based on modeling healthy quadrotors as particles in an ideal fluid flow. We provide three key contributions: (i) A Containment Exclusion Mode (CEM) safety theorem and proof which guarantees safety and formally specifies a minimum safe distance between quadrotors in formation, (ii) A real-time, computationally efficient CEM navigation algorithm, (iii) Simulation and experimental algorithm validation. Simulations were first performed with a team of six virtual quadrotors to demonstrate velocity tracking via dynamic slide speed, maintaining sufficient inter-agent distances, and operating in real-time. Flight tests with a team of two custom quadrotors were performed in an indoor motion capture flight facility, successfully validating that the navigation algorithm can handle non-trivial bounded tracking errors while guaranteeing safety.

cs.RO↗

arXiv2022

Real-Time Deployment of a Large-Scale Multi-Quadcopter System (MQS)

This paper presents a continuum mechanics-based approach for real-time deployment (RTD) of a multi-quadcopter system between moving initial and final configurations arbitrarily distributed in a 3-D motion space. The proposed RTD problem is decomposed into spatial planning, temporal planning and acquisition sub-problems. For the spatial planning, the RTD desired coordination is defined by integrating (i) rigid-body rotation, (ii) one-dimensional homogeneous deformation, and (ii) one-dimensional heterogeneous coordination such that necessary conditions for inter-agent collision avoidance between every two quadcopter UAVs are satisfied. By the RTD temporal planning, this paper suffices the inter-agent collision avoidance between every two individual quadcopters, and assures the boundedness of the rotor angular speeds for every individual quadcopter. For the RTD acquisition, each quadcopter modeled by a nonlinear dynamics applies a nonlinear control to stably and safely track the desired RTD trajectory such that the angular speeds of each quadcopter remain bounded and do not exceed a certain upper limit.

cs.RO↗

arXiv2022

Drones Practicing Mechanics

Mechanics of materials is a classic course of engineering presenting the fundamentals of strain and stress analysis to junior undergraduate students in several engineering majors. So far, material deformation and strain have been only analyzed using theoretical and numerical approaches, and they have been experimentally validated by expensive machines and tools. This paper presents a novel approach for strain and deformation analysis by using quadcopters. We propose to treat quadcopters as finite number of particles of a deformable body and apply the principles of continuum mechanics to illustrate the concept of axial and shear deformation by using quadcopter hardware in a $3$-D motion space. The outcome of this work can have significant impact on undergraduate education by filling the gap between in-class learning and hardware realization and experiments, where we introduce new roles for drones as "teachers" providing a great opportunity for practicing theoretical concepts of mechanics in a fruitful and understandable way.

physics.ed-ph↗

arXiv2021

A Continuous-Time Optimal Control Approach to Congestion Control

Traffic congestion has become a nightmare to modern life in metropolitan cities. On average, a driver spending X hours a year stuck in traffic is one of most common sentences we often read regarding traffic congestion. Our aim in this article is to provide a method to control this seemingly ever-growing problem of traffic congestion. We model traffic dynamics using a continuous-time mass-flow conservation law, and apply optimal control techniques to control traffic congestion. First, we apply the mass-flow conservation law to specify traffic feasibility and present continuous-time dynamics for modeling traffic as a network problem by defining a network of interconnected roads (NOIR). The traffic congestion control is formulated as a boundary control problem and we use the concept of statetransition matrix to help with the optimization of boundary flow by solving a constrained optimal control problem using quadratic programming. Finally, we show that the proposed algorithm is successful by simulating on a NOIR.

math.OC↗

arXiv2021

Collision-Free Continuum Deformation Coordination of a Multi-Quadcopter System Using Cooperative Localization

This paper integrates cooperative localization with continuum deformation coordination of a multi-quadcopter system {(MQS) to assure safety and optimality of the quadcopter team coordination in the presence of position uncertainty. We first consider the MQS as a finite number of particles of a deformable triangle in a $3$-D motion space and define their continuum deformation coordination as a leader-follower problem in which leader quadcopters} can estimate (know) their positions but follower quadcopters rely on relative position measurements to localize themselves and estimate the leaders' positions. We then propose a navigation strategy for the MQS to plan and acquire the desired continuum deformation coordination, in the presence of measurement noise, disturbance, and position uncertainties, such that collision is avoided and rotor angular speeds of all quadcopters remain bounded. We show the efficacy of the proposed strategy by simulating the continuum deformation coordination of an MQS with eight quadcopters.

math.DS↗

arXiv2021

A Physics-Based Data-Driven Approach for Finite Time Estimation of Pandemic Growth

COVID-19 is a global health crisis that has had unprecedented, widespread impact on households across the United States and has been declared a global pandemic on March 11, 2020 by World Health Organization (WHO) [1]. According to Centers for Disease Control and Prevention (CDC) [2], the spread of COVID-19 occurs through person-to-person transmission i.e. close contact with infected people through contaminated surfaces and respiratory fluids carrying infectious virus. This paper presents a data-driven physics-based approach to analyze and predict the rapid growth and spread dynamics of the pandemic. Temporal and Spatial conservation laws are used to model the evolution of the COVID-19 pandemic. We integrate quadratic programming and neural networks to learn the parameters and estimate the pandemic growth. The proposed prediction model is validated through finite time estimation of the pandemic growth using the total number of cases, deaths and recoveries in the United States recorded from March 12, 2020 until October 1, 2021 [3].

eess.SY↗

arXiv2021

A Physics-Based Safety Recovery Approach for Fault-Resilient Multi-Quadcopter Coordination

This paper develops a novel physics-based approach for fault-resilient multi-quadcopter coordination in the presence of abrupt quadcopter failure. Our approach consists of two main layers: (i) high-level physics-based guidance to safely plan the desired recovery trajectory for every healthy quadcopter and (ii) low-level trajectory control design by choosing an admissible control for every healthy quadcopter to safely recover from the anomalous situation, arisen from quadcopter failure, as quickly as possible. For the high-level trajectory planning, first, we consider healthy quadcopters as particles of an irrotational fluid flow sliding along streamline paths wrapping failed quadcopters in the shared motion space. We then obtain the desired recovery trajectories by maximizing the sliding speeds along the streamline paths such that the rotor angular speeds of healthy quadcopters do not exceed certain upper bounds at all times during the safety recovery. In the low level, a feedback linearization control is designed for every healthy quadcopter such that quadcopter rotor angular speeds remain bounded and satisfy the corresponding safety constraints. Simulation results are given to illustrate the efficacy of the proposed method.

math.DS↗