Article
Robotics
Zhong Wang, Ying Shen, Lin Zhang, Shaoming Zhang, Yicong Zhou
Summary: This letter introduces a path planning and tracking method for carlike robots in coverage navigation, solving the challenges of stable path tracking and high coverage rate by introducing reference line constraints, heuristic initialization strategy, and cubic parametric path smoother. The proposed approach significantly reduces deviation off the reference line and achieves high success rate even in complex environments. The path tracker designed in this study ensures stable and high-precision tracking for multi-segment paths.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Automation & Control Systems
Zlatan Ajanovic, Enrico Regolin, Barys Shyrokau, Hana Catic, Martin Horn, Antonella Ferrara
Summary: To achieve optimal robot behavior in dynamic scenarios, a search-based method is proposed to effectively solve the complex dynamics problem in a highly dimensional state space. The method successfully explores the space of possible trajectories by sampling different combinations of motion primitives guided by the search and allows the use of multiple locally approximated models to simplify the problem without losing accuracy. The algorithm performance is evaluated in simulated driving on a mixed-track with segments of different curvatures (right and left).
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Civil
Sean Kragelund, Claire Walton, Isaac Kaminer, Vladimir Dobrokhodov
Summary: This article presents a computational framework for planning mine countermeasures (MCM) search missions by autonomous vehicles, utilizing generalized optimal control (GenOC) to optimize search performance, developing tunable sensor models, and incorporating sonar detection models for optimization.
IEEE JOURNAL OF OCEANIC ENGINEERING
(2021)
Article
Robotics
Minsu Cho, Yeongseok Lee, Kyung-Soo Kim
Summary: Nonlinear model predictive control (NMPC) is an efficient and proven method for optimization-based autonomous vehicle motion planning. By converting the inequality-constrained optimization problem into an unconstrained optimization problem, a single unified constraint is designed to simplify complex motion planning problems and reduce computational costs.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Robotics
Yao Qi, Binbing He, Rendong Wang, Le Wang, Youchun Xu
Summary: This letter introduces a hierarchical motion planner that can generate smooth and feasible trajectories for autonomous vehicles in unstructured environments. The framework enables real-time computation by progressively shrinking the solution space. It proposes a graph searcher and a time interval-based algorithm for finding coarse trajectories and obstacle detection respectively, as well as a continuous optimizer for smoothing the trajectory. The approach has been validated in simulations and real-world off-road environments, showing improved success rate and travel efficiency while avoiding obstacles.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Robotics
Shinkyu Park, Michal Cap, Javier Alonso-Mora, Carlo Ratti, Daniela Rus
Summary: The article proposes a trajectory planning algorithm that penalizes deviations of autonomous vessels from human-operated vessels' movements and finds trajectories resembling those of human-operated vessels using optimal control formulation, kernel density estimation, and Kullback-Leibler control cost. Through experiments, it is demonstrated that the trajectories generated by the algorithm resemble those of human-operated vessels, reducing head-on encounters between vessels and improving navigation safety.
IEEE TRANSACTIONS ON ROBOTICS
(2021)
Article
Robotics
Ruiqi Zhang, Jing Hou, Guang Chen, Zhijun Li, Jianxiao Chen, Alois Knoll
Summary: This research proposes an efficient residual policy learning method for motion planning in high-speed autonomous racing. The method utilizes real-time observations from LiDAR and IMU for obstacle avoidance and navigation. It combines a modified artificial potential field controller and a deep reinforcement learning algorithm to generate the optimal policy, resulting in fast convergence and reduced resource consumption.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Automation & Control Systems
Henglai Wei, Yang Shi
Summary: Autonomous marine vehicles (AMVs) have gained attention for their essential roles in marine applications. Recent advances in communication technologies, perception capability, computational power, and optimization algorithms have stimulated the development of AMVs. Model predictive control (MPC) is effective in handling constraints and optimizing control performance. This paper reviews the progress in motion planning and control for AMVs from the perspective of MPC and highlights future research trends.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2023)
Article
Computer Science, Interdisciplinary Applications
Luis Luttgens, Benjamin Jurgelucks, Heinrich Wernsing, Sylvain Roy, Christof Buskens, Kathrin Flasskamp
Summary: This article presents a novel approach to autonomous trajectory planning using precomputed and connectable trajectory segments, called motion primitives, and an A*-search algorithm. It achieves finding optimal trajectories even in terrains with obstacles. The method is especially significant for navigation scenarios such as ships in ports.
MATHEMATICAL AND COMPUTER MODELLING OF DYNAMICAL SYSTEMS
(2022)
Article
Automation & Control Systems
Daniel S. Campos, Joao B. R. do Val
Summary: The article presents an H-infinity-norm theory for stochastic systems in the CSVIU class. It introduces the concept of H-infinity control with infinite energy disturbance signals to accurately represent persistent perturbations in the environment. The article establishes a refined connection between stability and system power finiteness, and utilizes the relations between H-infinity optimization and differential games to analyze the worst-case stability of CSVIU systems.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Engineering, Marine
Raphael Zaccone
Summary: This study focuses on autonomous vessels and real-time path planning, proposing an optimal path-planning algorithm based on RRT* and discussing collision avoidance, compliance with regulations, path feasibility, and optimality in detail.Tests on a high-fidelity simu
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2021)
Article
Robotics
Yingbing Chen, Ren Xin, Jie Cheng, Qingwen Zhang, Xiaodong Mei, Ming Liu, Lujia Wang
Summary: This paper proposes a learning-based Interaction Point Model (IPM) to describe the interaction between agents in autonomous driving, and integrates the model into a planning framework, demonstrating its effectiveness and robustness through comprehensive simulations in highly dynamic environments.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Review
Engineering, Marine
Ulku Ozturk, Melih Akdag, Tarik Ayabakan
Summary: This review explores the path planning algorithms of autonomous maritime vehicles and highlights the need to address various traffic rules, as well as the calibratability of algorithms after regulation amendments.
Article
Engineering, Aerospace
Bowen Hou, Dayi Wang, Jiongqi Wang, Dongming Ge, Haiyin Zhou, Xuanying Zhou
Summary: The paper introduces a one-step optimal maneuver solution method, which considers various constraints based on an optimization problem and practical engineering requirements to improve the state estimation accuracy in relative navigation.
JOURNAL OF GUIDANCE CONTROL AND DYNAMICS
(2021)
Article
Engineering, Electrical & Electronic
Lantian Shangguan, J. Alex Thomasson, Swaminathan Gopalswamy
Summary: This paper presents a motion planning algorithm for autonomous grain carts that integrates Artificial Potential Field (APF) with Fuzzy Logic Control (FLC). Simulation tests confirmed the effectiveness, robustness, and efficiency of the proposed algorithm in harvest operations, while mobile robot tests demonstrated the practicality of the navigation solution.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Biology
Jiawei Yin, Agung Julius, John T. Wen, Meeko M. K. Oishi, Lee K. Brown
CHRONOBIOLOGY INTERNATIONAL
(2020)
Article
Automation & Control Systems
Abraham P. Vinod, Meeko M. K. Oishi
Summary: This study focuses on the stochastic reachability problem in constrained dynamical systems, providing insights on the geometric properties of stochastic reach sets and proposing a scalable algorithm for their computation using convex optimization. The efficacy and scalability of the approach is demonstrated through numerical examples, showing superior performance compared to existing software tools for linear systems verification.
Article
Automation & Control Systems
Joseph D. Gleason, Abraham P. Vinod, Meeko M. K. Oishi
Summary: The study tackles the problem of stochastic reachability for discrete-time, stochastic dynamical systems with bounded control authority, proposing grid-free algorithms to compute approximations of the stochastic reach set and synthesizing a feedback-based controller. The approach utilizes set-theoretic techniques for nonlinear systems with stochastic disturbances.
Article
Automation & Control Systems
Abraham P. Vinod, Meeko M. K. Oishi
Summary: The text discusses computational techniques based on Fourier transforms for characterizing the stochasticity of future states in Markov jump affine systems. Convex optimization is used to compute outer approximations of the probabilistic occupancy function. This method does not rely on gridding, recursion, or sampling, accommodates non-Gaussian perturbed dynamics, and provides outer-approximation guarantees.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Automation & Control Systems
Abraham P. Vinod, Adam J. Thorpe, Philip A. Olaniyi, Tyler H. Summers, Meeko M. K. Oishi
Summary: We propose a method for dynamics-driven, user-interface design for a human-automation system via sensor selection. The user interface is defined as the output of a multiple-input-multiple-output (MIMO) linear time-invariant (LTI) system, and the design problem involves selecting an output matrix from a given set of candidates. Constraints based on necessary situation awareness are added to the selection process, taking into account the level of trust the human has in the automation. The resulting combinatorial problem is addressed using tractable algorithms that consider monotonicity and submodularity. This method is applied to the IEEE 118-bus system to construct correct-by-design interfaces under various operating scenarios.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2022)
Article
Automation & Control Systems
Adam J. Thorpe, Kendric R. Ortiz, Meeko M. K. Oishi
Summary: In this paper, finite sample bounds are computed for data-driven approximations of the solution to stochastic reachability problems using a nonparametric technique called kernel distribution embeddings. This approach provides model-free probabilistic assurances of safety for stochastic systems and is responsive to non-uniformly sampled data.
Editorial Material
Computer Science, Interdisciplinary Applications
Mohammad Al Faruque, Meeko Mitsuko Oishi
Summary: The articles in this special section are based on selected papers presented at the 2021 ACM/IEEE International Conference on Cyber-Physical Systems, which focused on contributions related to various areas, including smart and connected cities, autonomous CPS, verification and control, security and privacy, and human health and biomedical CPS.
ACM TRANSACTIONS ON CYBER-PHYSICAL SYSTEMS
(2022)
Article
Multidisciplinary Sciences
Chukwuemeka O. Ike, John T. Wen, Meeko M. K. Oishi, Lee K. Brown, A. Agung Julius
Summary: In this paper, a linear state observer is proposed as an elegant solution for continuous phase estimation in circadian rhythms. By adjusting the gains of the observer using an evolutionary optimization algorithm, the target components can be extracted from individuals' data, providing accurate phase estimates under ambulatory conditions.
Proceedings Paper
Automation & Control Systems
Kendric R. Ortiz, Adam J. Thorpe, AnaMaria Perez, Maya Luster, Brandon J. Pitts, Meeko Oishi
Summary: Variability in human response poses challenges for modeling and controlling human-automation systems. It is essential to develop methods that can accommodate human variability to ensure efficiency, safety, and high levels of performance in the pervasive use of autonomy. This study proposes an easily computable modeling framework that utilizes a metric to assess variability in individual human response in a dynamic task.
Proceedings Paper
Automation & Control Systems
Adam J. Thorpe, Meeko M. K. Oishi
Summary: This paper presents SOCKS, a data-driven stochastic optimal control toolbox based in kernel methods. SOCKS is capable of computing approximate solutions for stochastic optimal control problems with arbitrary cost and constraint functions. By utilizing nonparametric techniques such as kernel methods, SOCKS is able to handle a wide range of systems without making prior assumptions on system dynamics or uncertainty structure.
HSCC 2022: PROCEEDINGS OF THE 25TH ACM INTERNATIONAL CONFERENCE ON HYBRID SYSTEMS: COMPUTATION AND CONTROL (PART OF CPS-IOT WEEK 2022)
(2022)
Proceedings Paper
Green & Sustainable Science & Technology
Yuliya Matlashova, Meeko Oishi, Ali Bidram
Summary: This study presents an optimal user interface for microgrid operators utilizing a combinatorial optimization strategy, taking into consideration trust level and assigned tasks. By decomposing the model into slow and fast dynamics, challenges of inverter-based microgrid modeling are addressed, resulting in an effective user interface design. The proposed approach is validated through Matlab modeling of an inverter-based microgrid.
2021 13TH ANNUAL IEEE GREEN TECHNOLOGIES CONFERENCE GREENTECH 2021
(2021)
Proceedings Paper
Automation & Control Systems
Shawn Priore, Abraham Vinod, Vignesh Sivaramakrishnan, Christopher Petersen, Meeko Oishi
Summary: In this research, a convex optimization approach is used to solve a multi-satellite maneuvering problem, efficiently constructing provably safe sub-optimal controllers by solving a collection of quadratic programs, avoiding collisions and reducing fuel consumption.
2021 AMERICAN CONTROL CONFERENCE (ACC)
(2021)
Proceedings Paper
Automation & Control Systems
Adam J. Thorpe, Vignesh Sivaramakrishnan, Meeko M. K. Oishi
Summary: The method presented utilizes conditional distribution embeddings to compute stochastic reachability safety probabilities for high-dimensional stochastic dynamical systems, addressing the modeling issue of stochastic kernels through a data-driven approach. By employing random Fourier features, the computational requirements for high-dimensional systems are alleviated successfully.
2021 AMERICAN CONTROL CONFERENCE (ACC)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Nathan Patrizi, Georgios Fragkos, Kendric Ortiz, Meeko Oishi, Eirini Eleni Tsiropoulou
2020 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)
(2020)
Article
Automation & Control Systems
Adam J. Thorpe, Meeko M. K. Oishi
IEEE CONTROL SYSTEMS LETTERS
(2020)