Article
Automation & Control Systems
Jiankun Wang, Max Q-H Meng
Summary: This article proposes a two-level planner to address the autonomous luggage trolley collection problem at the airport. The higher level planner tackles a decision-making problem, while the lower level planner introduces a novel real-time path planning algorithm.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Chemistry, Multidisciplinary
Mingrui Shu, Xiuyu Zheng, Fengguo Li, Kaiyong Wang, Qiang Li
Summary: This paper proposes a time-optimal path planning method for autonomous underwater vehicles (AUVs) based on a Markov decision process (MDP) algorithm, and demonstrates its importance and advantages in the marine environment through simulation experiments.
APPLIED SCIENCES-BASEL
(2022)
Article
Automation & Control Systems
Chaoqun Wang, Xiangyu Chen, Chenming Li, Rui Song, Yibin Li, Max Q-H Meng
Summary: This article presents a hierarchical trajectory planning approach for safe and smooth robot motion in dynamic environments. The approach includes global path generation, local chasing and tracking, adaptive model predictive control, and event-triggered mechanism. Through extensive experiments, the effectiveness of the approach is demonstrated.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Automation & Control Systems
Manuel Amersdorfer, Thomas Meurer
Summary: This article presents a equidistant tool path planning strategy on curved freeform surfaces, maintaining constant velocity while limiting maximum acceleration, suitable for robotic machining tasks.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Article
Chemistry, Analytical
Yi Ding, Hongyang Zhu
Summary: This paper presents a heuristic algorithm, named MHA, for time-optimized avoidance of USVs based on the Risk-Sensitive Markov decision process model. The proposed method can effectively generate motion paths that align with the actual time constraints and evaluate the trade-off between the probability of achieving the goal and the budget.
Article
Engineering, Mechanical
Yu-Ju Chen, Bing-Gang Jhong, Mei-Yung Chen
Summary: This paper proposes a real-time path planning algorithm based on the Markov decision process (MDP), which can be used to guide wheeled mobile robots to the goal in dynamic environments. The algorithm consists of a utility update phase and a policy update phase, where a series of policies are obtained by maximizing long-term total reward, forming a path towards the goal.
Article
Computer Science, Interdisciplinary Applications
Rishi K. Malhan, Aniruddha Shembekar, Ariyan M. Kabir, Prahar M. Bhatt, Brual Shah, Scott Zanio, Steven Nutt, Satyandra K. Gupta
Summary: Hand layup is a commonly used process for making composite structures, but it is laborious and limits throughput. We have developed a multi-robot cell to automate the layup process, using a hybrid-physics simulator and state space search to generate feasible trajectories. The system is computationally efficient and can handle a wide variety of complex parts.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2021)
Article
Robotics
Ariyan M. Kabir, Shantanu Thakar, Rishi K. Malhan, Aniruddha Shembekar, Brual C. Shah, Satyandra K. Gupta
Summary: This article presents an approach to generate path-constrained synchronous motion for a coupled ensemble of robots, addressing relative motion constraints among objects in the environment through non-linear optimization. The method formulates the problem as a discrete parameter optimization problem and solves it using successive constraint refinement techniques, adapting to different scenarios and reducing computation time. The effectiveness of the proposed method is demonstrated on challenging test cases with high-degree-of-freedom robotic systems in simulation and physical environments.
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
(2021)
Article
Automation & Control Systems
Mark Burkhardt, Andreas Gienger, Oliver Sawodny
Summary: This study aims to design an offline trajectory planning algorithm for autonomous tower cranes to move along straight connection lines and achieve smooth transitions at the intersection points. By solving an optimal control problem, the trajectory for transitions between waypoints is computed, and the effectiveness of the algorithm is validated through simulations and experiments.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2023)
Article
Engineering, Marine
Guanzhong Chen, Yue Shen, Nanzhu Qu, Bo He
Summary: This study proposes a path planning method based on behavioral decision-making for autonomous underwater vehicle to save energy during the diving process. The method utilizes an adaptive differential evolution algorithm and motion constraints to optimize energy consumption. Experimental results show that the proposed method can save at least 9% energy and obtain a reachable path with optimized energy consumption during diving process.
Article
Engineering, Civil
Alexander Botros, Stephen L. Smith
Summary: Lattice-based planning techniques simplify motion planning for autonomous vehicles by limiting available motions to a pre-computed set of primitives. This study formulates the problem for an arbitrary lattice as a mixed integer linear program and proposes an A*-based algorithm to solve the motion planning problem using these primitives. The study also introduces an algorithm that removes excessive oscillations from planned motions. The method is validated for autonomous driving in both parking lot and highway scenarios.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Automation & Control Systems
Omveer Sharma, N. C. Sahoo, N. B. Puhan
Summary: Autonomous vehicles are gaining attention in academic and industrial research due to their advantages such as safety improvement and reduced traffic congestion. Intelligent motion and behavior planning play crucial roles in decision making process, considering factors like safety, comfort, and traffic rules. Various techniques have been developed over the past few decades, but there is still a need for rigorous evaluation and improvement of existing approaches.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Engineering, Marine
Rohit Chowdhury, Atharva Navsalkar, Deepak Subramani
Summary: The importance of autonomous marine vehicles is increasing in various ocean science and engineering applications. Multi-objective optimization is crucial for planning optimal routes in dynamic ocean environments, and our developed path planner can compute optimal operating curves in a fraction of the time of existing solvers. This solution also serves as a benchmark for other approximate AI algorithms, improving their explainability.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Information Systems
Jee-Yong Park, Hoosang Lee, Changhyeon Kim, Jeha Ryu
Summary: This article proposes a novel data sampling strategy, Voronoi tessellation sampling, for learning Gaussian process-based robotic motion planning in dynamically changing environments. Experimental results show that this method can learn optimal policies using fewer data.
Article
Management
Akash Deep, Shiyu Zhou, Dharmaraj Veeramani, Yong Chen
Summary: The growing use of real-time condition monitoring in industrial equipment has led to interest in methods for optimal maintenance planning. Existing approaches for maintenance policy development consider degradation to be fully or partially observable. This paper addresses the issue of partial observability by modeling observed CM signals through a time-dependent piecewise linear linkage and utilizing a Partially Observed Markov Decision Process (POMDP) for determining the optimal maintenance strategy. The study shows the existence of a control-limit policy under certain conditions and presents a case study demonstrating the superiority of the proposed modeling procedure compared to other competing models.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)