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Kala R. Mission planning on preference-based expression trees using heuristics-assisted evolutionary computation. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
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2
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Lifted model checking for relational MDPs. Mach Learn 2022. [DOI: 10.1007/s10994-021-06102-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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3
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Appraising the Optimal Power Flow and Generation Capacity in Existing Power Grid Topology with Increase in Energy Demand. ENERGIES 2022. [DOI: 10.3390/en15072522] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Several socioeconomic factors such as industrialization, population growth, evolution of modern technologies, urbanization and other social activities do heavily influence the increase in energy demand. A thorough understanding of the effects of energy demand to power grid is highly essential for effective planning and operation of a power system network in terms of the available generation and transmission line capacities. This paper presents an optimal power flow (OPF) with the aim to determine the exact nodes through which the network capacities can be increased. The problem is formulated as a Direct Current (DC) OPF model, which is a linearized version of an Alternating Current (AC) OPF model. The DC-OPF model was solved as a single period OPF problem. The model was tested in several case studies using the topology of the IEEE test systems, and the computation speeds of the different cases were compared. The results suggested dual variables of the problem’s constraints as an extra tool for the network designer to see where to increase the network capacities.
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Multi-robot mission planning using evolutionary computation with incremental task addition. INTEL SERV ROBOT 2021. [DOI: 10.1007/s11370-021-00389-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Lacerda B, Faruq F, Parker D, Hawes N. Probabilistic planning with formal performance guarantees for mobile service robots. Int J Rob Res 2019. [DOI: 10.1177/0278364919856695] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
We present a framework for mobile service robot task planning and execution, based on the use of probabilistic verification techniques for the generation of optimal policies with attached formal performance guarantees. Our approach is based on a Markov decision process model of the robot in its environment, encompassing a topological map where nodes represent relevant locations in the environment, and a range of tasks that can be executed in different locations. The navigation in the topological map is modeled stochastically for a specific time of day. This is done by using spatio-temporal models that provide, for a given time of day, the probability of successfully navigating between two topological nodes, and the expected time to do so. We then present a methodology to generate cost optimal policies for tasks specified in co-safe linear temporal logic. Our key contribution is to address scenarios in which the task may not be achievable with probability one. We introduce a task progression function and present an approach to generate policies that are formally guaranteed to, in decreasing order of priority: maximize the probability of finishing the task; maximize progress towards completion, if this is not possible; and minimize the expected time or cost required. We illustrate and evaluate our approach with a scalability evaluation in a simulated scenario, and report on its implementation in a robot performing service tasks in an office environment for long periods of time.
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Affiliation(s)
| | - Fatma Faruq
- School of Computer Science, University of Birmingham, UK
| | - David Parker
- School of Computer Science, University of Birmingham, UK
| | - Nick Hawes
- Oxford Robotics Institute, University of Oxford, UK
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Abstract
SummaryMission planning is a complex motion planning problem specified by using Temporal Logic constituting of Boolean and temporal operators, typically solved by model verification algorithms with an exponential complexity. The paper proposes co-evolutionary optimization thus building an iterative solution to the problem. The language for mission specification is generic enough to represent everyday missions, while specific enough to design heuristics. The mission is broken into components which cooperate with each other. The experiments confirm that the robot is able to outperform the search, evolutionary and model verification techniques. The results are demonstrated by using a Pioneer LX robot.
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Malone N, Chiang HT, Lesser K, Oishi M, Tapia L. Hybrid Dynamic Moving Obstacle Avoidance Using a Stochastic Reachable Set-Based Potential Field. IEEE T ROBOT 2017. [DOI: 10.1109/tro.2017.2705034] [Citation(s) in RCA: 59] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Plaku E, Karaman S. Motion planning with temporal-logic specifications: Progress and challenges. AI COMMUN 2015. [DOI: 10.3233/aic-150682] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Erion Plaku
- Department of Electrical Engineering and Computer Science, Catholic University of America, Washington, DC, USA. E-mail:
| | - Sertac Karaman
- Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA. E-mail:
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Wang J, Ding X, Lahijanian M, Paschalidis IC, Belta CA. Temporal logic motion control using actor–critic methods. Int J Rob Res 2015. [DOI: 10.1177/0278364915581505] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This paper considers the problem of deploying a robot from a specification given as a temporal logic statement about some properties satisfied by the regions of a large, partitioned environment. We assume that the robot has noisy sensors and actuators and model its motion through the regions of the environment as a Markov decision process (MDP). The robot control problem becomes finding the control policy which maximizes the probability of satisfying the temporal logic task on the MDP. For a large environment, obtaining transition probabilities for each state–action pair, as well as solving the necessary optimization problem for the optimal policy, are computationally intensive. To address these issues, we propose an approximate dynamic programming framework based on a least-squares temporal difference learning method of the actor–critic type. This framework operates on sample paths of the robot and optimizes a randomized control policy with respect to a small set of parameters. The transition probabilities are obtained only when needed. Simulations confirm that convergence of the parameters translates to an approximately optimal policy.
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Affiliation(s)
- Jing Wang
- Division of System Engineering, Department of Mechanical Engineering, and Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - Xuchu Ding
- Embedded Systems and Networks Group, United Technologies Research Center, East Hartford, CT, USA
| | | | - Ioannis Ch. Paschalidis
- Division of System Engineering, Department of Mechanical Engineering, and Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - Calin A. Belta
- Division of System Engineering, Department of Mechanical Engineering, and Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
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Ulusoy A, Wongpiromsarn T, Belta C. Incremental controller synthesis in probabilistic environments with temporal logic constraints. Int J Rob Res 2014. [DOI: 10.1177/0278364913519000] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In this paper, we consider automatic computation of optimal control strategies for a robot interacting with a set of independent uncontrollable agents in a graph-like environment. The mission specification is given as a syntactically co-safe Linear Temporal Logic formula over some properties that hold at the vertices of the environment. The robot is assumed to be a deterministic transition system, while the agents are probabilistic Markov models. The goal is to control the robot such that the probability of satisfying the mission specification is maximized. We propose a computationally efficient incremental algorithm based on the fact that temporal logic verification is computationally cheaper than synthesis. We present several case studies where we compare our approach to the classical non-incremental approach in terms of computation time and memory usage.
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Affiliation(s)
- Alphan Ulusoy
- Division of Systems Engineering, Boston University, Boston, MA, USA
| | | | - Calin Belta
- Division of Systems Engineering, Boston University, Boston, MA, USA
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Cizelj I, Belta C. Control of noisy differential-drive vehicles from time-bounded temporal logic specifications. Int J Rob Res 2014. [DOI: 10.1177/0278364914522312] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
We address the problem of controlling a noisy differential drive mobile robot such that the probability of satisfying a specification given as a bounded linear temporal logic formula over a set of properties at the regions in the environment is maximized. We assume that the vehicle can determine its precise initial position in a known map of the environment. However, motivated by practical limitations, we assume that the vehicle is equipped with noisy actuators and, during its motion in the environment, it can only measure the angular velocity of its wheels using limited accuracy incremental encoders. Assuming the duration of the motion is finite, we map the measurements to a Markov decision process (MDP). We use recent results in statistical model checking to obtain an MDP control policy that maximizes the probability of satisfaction. We translate this policy to a vehicle feedback control strategy and show that the probability that the vehicle satisfies the specification in the environment is bounded from below by the probability of satisfying the specification on the MDP. We illustrate our method with simulations and experimental results.
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Affiliation(s)
- Igor Cizelj
- Division of Systems Engineering at Boston University, MA, USA
| | - Calin Belta
- Division of Systems Engineering at Boston University, MA, USA
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