arXiv subjects

Majid Khonji

Publications and source records attributed to Majid Khonji.

29 records · Page 2Linked to original sources

arXiv2022

Autonomous Recharging and Flight Mission Planning for Battery-operated Autonomous Drones

Unmanned aerial vehicles (UAVs), commonly known as drones, are being increasingly deployed throughout the globe as a means to streamline monitoring, inspection, mapping, and logistic routines. When dispatched on autonomous missions, drones require an intelligent decision-making system for trajectory planning and tour optimization. Given the limited capacity of their onboard batteries, a key design challenge is to ensure the underlying algorithms can efficiently optimize the mission objectives along with recharging operations during long-haul flights. With this in view, the present work undertakes a comprehensive study on automated tour management systems for an energy-constrained drone: (1) We construct a machine learning model that estimates the energy expenditure of typical multi-rotor drones while accounting for real-world aspects and extrinsic meteorological factors. (2) Leveraging this model, the joint program of flight mission planning and recharging optimization is formulated as a multi-criteria Asymmetric Traveling Salesman Problem (ATSP), wherein a drone seeks for the time-optimal energy-feasible tour that visits all the target sites and refuels whenever necessary. (3) We devise an efficient approximation algorithm with provable worst-case performance guarantees and implement it in a drone management system, which supports real-time flight path tracking and re-computation in dynamic environments. (4) The effectiveness and practicality of the proposed approach are validated through extensive numerical simulations as well as real-world experiments.

cs.RO↗

arXiv2021

Online Algorithm for Demand Response with Inelastic Demands and Apparent Power Constraint

A classical problem in power systems is to allocate in-coming (elastic or inelastic) demands without violating the operating constraints of electric networks in an online fashion. Although online decision problems have been well-studied in the literature, a unique challenge arising in power systems is the presence of non-linear constraints, a departure from the traditional settings. A particular example is the capacity constraint of apparent power, which gives rise to a quadratic constraint, rather than typical linear constraints. In this paper, we present a competitive randomized online algorithm for deciding whether a sequence of inelastic demands can be allocated for the requested intervals, subject to the total satisfiable apparent power within a time-varying capacity constraint. We also consider an alternative setting with nodal voltage constraint, using a variant of the online algorithm. Finally, simulation studies are provided to evaluate the algorithms empirically.

cs.DS↗

arXiv2019

Combinatorial Optimization of AC Optimal Power Flow with Discrete Demands in Radial Networks

The AC Optimal power flow (OPF) problem is one of the most fundamental problems in power systems engineering. For the past decades, researchers have been relying on unproven heuristics to tackle OPF. The hardness of OPF stems from two issues: (1) non-convexity and (2) combinatoric constraints (e.g., discrete power extraction constraints). The recent advances in providing sufficient conditions on the exactness of convex relaxation of OPF can address the issue of non-convexity. To complete the understanding of OPF, this paper presents a polynomial-time approximation algorithm to solve the convex-relaxed OPF with discrete demands as combinatoric constraints, which has a provably small parameterized approximation ratio (also known as PTAS algorithm). Together with the sufficient conditions on the exactness of the convex relaxation, we provide an efficient approximation algorithm to solve OPF with discrete demands, when the underlying network is radial with a fixed size and one feeder. The running time of PTAS is $O(n^{4m/ε}T)$, where $T$ is the time required to solve a convex relaxation of the problem, and $m, ε$ are fixed constants. Based on prior hardness results of OPF, our PTAS is among the best achievable in theory. Simulations show our algorithm can produce close-to-optimal solutions in practice.

cs.DS↗

arXiv2019

Risk-Aware Reasoning for Autonomous Vehicles

A significant barrier to deploying autonomous vehicles (AVs) on a massive scale is safety assurance. Several technical challenges arise due to the uncertain environment in which AVs operate such as road and weather conditions, errors in perception and sensory data, and also model inaccuracy. In this paper, we propose a system architecture for risk-aware AVs capable of reasoning about uncertainty and deliberately bounding the risk of collision below a given threshold. We discuss key challenges in the area, highlight recent research developments, and propose future research directions in three subsystems. First, a perception subsystem that detects objects within a scene while quantifying the uncertainty that arises from different sensing and communication modalities. Second, an intention recognition subsystem that predicts the driving-style and the intention of agent vehicles (and pedestrians). Third, a planning subsystem that takes into account the uncertainty, from perception and intention recognition subsystems, and propagates all the way to control policies that explicitly bound the risk of collision. We believe that such a white-box approach is crucial for future adoption of AVs on a large scale.

cs.AI↗

arXiv2018

Complex-demand Scheduling Problem with Application in Smart Grid

We consider the problem of scheduling complex-valued demands over a discretized time horizon. Given a set of users, each user is associated with a set of demands representing different power consumption preferences. A demand is represented by a complex number, a time interval, and a utility value obtained if it is satisfied. At each time slot, the magnitude of the total selected demands should not exceed a given generation capacity. This naturally captures the supply constraints in alternating current (AC) electric systems. In this paper, we consider maximizing the aggregate user utility subject to power supply limits over a time horizon. We present approximation algorithms characterized by the maximum angle $ϕ$ between any two complex-valued demands. More precisely, a PTAS is presented for the case $ϕ\in [0,\tfracπ{2}]$, a bi-criteria FPTAS for $ϕ\in [0,π \mbox{-} \varepsilon]$ for any polynomially small $\varepsilon$, assuming the number of time slots in the discretized time horizon is a constant. Furthermore, if the number of time slots is part of the input, we present a reduction to the real-valued unsplittable flow problem on a path with only a constant approximation ratio. Finally, we present a practical greedy algorithm for the single time slot case with an approximation ratio of $\tfrac{1}{2}\cos \fracϕ{2}$ and a running time complexity of only ${O}(N\log N)$, $N$ standing for the aggregate number of user demands, which can be implemented efficiently in practice.

cs.DS↗

arXiv2018

Enabling Self-aware Smart Buildings by Augmented Reality

Conventional HVAC control systems are usually incognizant of the physical structures and materials of buildings. These systems merely follow pre-set HVAC control logic based on abstract building thermal response models, which are rough approximations to true physical models, ignoring dynamic spatial variations in built environments. To enable more accurate and responsive HVAC control, this paper introduces the notion of "self-aware" smart buildings, such that buildings are able to explicitly construct physical models of themselves (e.g., incorporating building structures and materials, and thermal flow dynamics). The question is how to enable self-aware buildings that automatically acquire dynamic knowledge of themselves. This paper presents a novel approach using "augmented reality". The extensive user-environment interactions in augmented reality not only can provide intuitive user interfaces for building systems, but also can capture the physical structures and possibly materials of buildings accurately to enable real-time building simulation and control. This paper presents a building system prototype incorporating augmented reality, and discusses its applications.

cs.HC↗

arXiv2016

Optimal Power Flow with Inelastic Demands for Demand Response in Radial Distribution Networks

The classical optimal power flow problem optimizes the power flow in a power network considering the associated flow and operating constraints. In this paper, we investigate optimal power flow in the context of utility-maximizing demand response management in distribution networks, in which customers' demands are satisfied subject to the operating constraints of voltage and transmission power capacity. The prior results concern only elastic demands that can be partially satisfied, whereas power demands in practice can be inelastic with binary control decisions, which gives rise to a mixed integer programming problem. We shed light on the hardness and approximability by polynomial-time algorithms for optimal power flow problem with inelastic demands. We show that this problem is inapproximable for general power network topology with upper and lower bounds of nodal voltage. Then, we propose an efficient algorithm for a relaxed problem in radial networks with bounded transmission power loss and upper bound of nodal voltage. We derive an approximation ratio between the proposed algorithm and the exact optimal solution. Simulations show that the proposed algorithm can produce close-to-optimal solutions in practice.

cs.DS↗

arXiv2016

Truthful Mechanisms for Combinatorial Allocation of Electric Power in Alternating Current Electric Systems for Smart Grid

Traditional studies of combinatorial auctions often only consider linear constraints. The rise of smart grid presents a new class of auctions, characterized by quadratic constraints. This paper studies the {\em complex-demand knapsack problem}, in which the demands are complex valued and the capacity of supplies is described by the magnitude of total complex-valued demand. This naturally captures the power constraints in alternating current (AC) electric systems. In this paper, we provide a more complete study and generalize the problem to the multi-minded version, beyond the previously known $\frac{1}{2}$-approximation algorithm for only a subclass of the problem. More precisely, we give a truthful PTAS for the case $ϕ\in[0,\fracπ{2}-δ]$, and a truthful FPTAS, which {\it fully} optimizes the objective function but violates the capacity constraint by at most $(1+ε)$, for the case $ϕ\in(\fracπ{2},π-δ]$, where $ϕ$ is the maximum argument of any complex-valued demand and $ε,δ>0$ are arbitrarily small constants. We complement these results by showing that, unless P=NP, neither a PTAS for the case $ϕ\in(\fracπ{2},π-δ]$ nor any bi-criteria approximation algorithm with polynomial guarantees for the case when $ϕ$ is arbitrarily close to $π$ (that is, when $δ$ is arbitrarily close to $0$) can exist.

cs.GT↗

arXiv2016

Efficient Algorithm for Scalable Event-based Demand Response Management in Microgrids

Demand response management has become one of the key enabling technologies for smart grids. Motivated by the increasing demand response incentives offered by service operators, more customers are subscribing to various demand response programs. However, with growing customer participation, the problem of determining the optimal loads to be curtailed in a microgrid during contingencies within a feasible time frame becomes computationally hard. This paper proposes an efficient approximation algorithm for event-based demand response management in microgrids. In event-based management, it is important to curtail loads as fast as possible to maintain the stability of a microgrid during the islanded mode in a scalable manner. A simple greedy approach is presented that can rapidly determine a close-to-optimal load curtailment scheme to maximize the aggregate customer utility in milliseconds for a large number of customers. This paper further derives a novel theoretical guarantee of the gap between the proposed efficient algorithm and the optimal solution (that may be computationally hard to obtain). The performance of algorithm is corroborated extensively by simulations with up to thousands of customers. For the sake of practicality, the proposed event-based demand response management algorithm is applied to a feeder from the Canadian benchmark distribution system. The simulation results demonstrate that the proposed approach efficiently optimizes microgrid operation during islanded mode while maintaining appropriate voltage levels and network constrains.

math.OC↗

arXiv2016

Online Algorithms for Information Aggregation from Distributed and Correlated Sources

There is a fundamental trade-off between the communication cost and latency in information aggregation. Aggregating multiple communication messages over time can alleviate overhead and improve energy efficiency on one hand, but inevitably incurs information delay on the other hand. In the presence of uncertain future inputs, this trade-off should be balanced in an online manner, which is studied by the classical dynamic TCP ACK problem for a single information source. In this paper, we extend dynamic TCP ACK problem to a general setting of collecting aggregate information from distributed and correlated information sources. In this model, distributed sources observe correlated events, whereas only a small number of reports are required from the sources. The sources make online decisions about their reporting operations in a distributed manner without prior knowledge of the local observations at others. Our problem captures a wide range of applications, such as in-situ sensing, anycast acknowledgement and distributed caching. We present simple threshold-based competitive distributed online algorithms under different settings of intercommunication. Our algorithms match the theoretical lower bounds in order of magnitude. We observe that our algorithms can produce satisfactory performance in simulations and practical testbed.

cs.NI↗

arXiv2014

Truthful Mechanisms for Combinatorial AC Electric Power Allocation

Traditional studies of combinatorial auctions often only consider linear constraints (by which the demands for certain goods are limited by the corresponding supplies). The rise of smart grid presents a new class of auctions, characterized by quadratic constraints. Yu and Chau [AAMAS 13'] introduced the complex-demand knapsack problem, in which the demands are complex-valued and the capacity of supplies is described by the magnitude of total complex-valued demand. This naturally captures the power constraints in AC electric systems. In this paper, we provide a more complete study and generalize the problem to the multi-minded version, beyond the previously known 1/2-approximation algorithm for only a subclass of the problem. More precisely, we give a truthful PTAS for the case phi in [0,pi/2-delta], and a truthful FPTAS, which fully optimizes the objective function but violates the capacity constraint by at most (1+epsilon), for the case phi in (pi/2,pi-delta], where phi is the maximum angle between any two complex-valued demands and epsilon,delta>0 are arbitrarily small constants.

cs.GT↗