scholarly journals Ship Motion Planning for MASS Based on a Multi-Objective Optimization HA* Algorithm in Complex Navigation Conditions

2021 ◽  
Vol 9 (10) ◽  
pp. 1126
Author(s):  
Meiyi Wu ◽  
Anmin Zhang ◽  
Miao Gao ◽  
Jiali Zhang
Keyword(s):  
Motion Planning ◽  
Autonomous Navigation ◽  
Optimal Path ◽  
Kinematic Model ◽  
Ship Motion ◽  
Planning Problem ◽  
Navigation Systems ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Three Degree Of Freedom

Ship motion planning constitutes the most critical part in the autonomous navigation systems of marine autonomous surface ships (MASS). Weather and ocean conditions can significantly affect their navigation, but there are relatively few studies on the influence of wind and current on motion planning. This study investigates the motion planning problem for USV, wherein the goal is to obtain an optimal path under the interference of the navigation environment (wind and current), and control the USV in order to avoid obstacles and arrive at its destination without collision. In this process, the influences of search efficiency, navigation safety and energy consumption on motion planning are taken into consideration. Firstly, the navigation environment is constructed by integrating information, including the electronic navigational chart, wind and current field. Based on the environmental interference factors, the three-degree-of-freedom kinematic model of USVs is created, and the multi-objective optimization and complex constraints are reasonably expressed to establish the corresponding optimization model. A multi-objective optimization algorithm based on HA* is proposed after considering the constraints of motion and dynamic and optimization objectives. Simulation verifies the effectiveness of the algorithm, where an efficient, safe and economical path is obtained and is more in line with the needs of practical application.

scholarly journals Local path planning for mobile robots based on intermediate objectives

Robotica ◽  
2014 ◽  
Vol 33 (4) ◽  
pp. 1017-1031 ◽  
Author(s):  
Yingchong Ma ◽  
Gang Zheng ◽  
Wilfrid Perruquetti ◽  
Zhaopeng Qiu
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Autonomous Navigation ◽  
Optimal Path ◽  
Kinematic Model ◽  
Planning Problem ◽  
Planning Algorithm ◽  
Local Path ◽  
Velocity Constraints ◽  
Horizon Planning

SUMMARYThis paper presents a path planning algorithm for autonomous navigation of non-holonomic mobile robots in complex environments. The irregular contour of obstacles is represented by segments. The goal of the robot is to move towards a known target while avoiding obstacles. The velocity constraints, robot kinematic model and non-holonomic constraint are considered in the problem. The optimal path planning problem is formulated as a constrained receding horizon planning problem and the trajectory is obtained by solving an optimal control problem with constraints. Local minima are avoided by choosing intermediate objectives based on the real-time environment.

Multi-Objective Optimization Model Development to Support Sizing Decisions for a Novel Reciprocating Steam Engine Technology

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
J. M. Hamel ◽  
Devin Allphin ◽  
Joshua Elroy
Keyword(s):  
Computational Model ◽  
Design Optimization ◽  
Kinematic Model ◽  
Operating Conditions ◽  
Steam Engine ◽  
System Model ◽  
System Level ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Crank Angle

A system-level computational model of a recently patented and prototyped novel steam engine technology was developed from first principles for the express purpose of performing design optimization studies for the engine's inventors. The developed system model consists of numerous submodels including a flow model of the intake process, a dynamic model of the intake valve response, a pressure model of the engine cylinder, a kinematic model of the engine piston, and an output model that determines engine performance parameters. A crank-angle discretization strategy was employed to capture the performance of engine throughout a full cycle of operation, thus requiring all engine design submodels to be evaluated at each crank angle of interest. To produce a system model with sufficient computational speed to be useful within optimization algorithms, which must exercise the system level model repeatedly, various simplifying assumptions and modeling approximations were utilized. The model was tested by performing a series of multi-objective design optimization case studies using the geometry and operating conditions of the prototype engine as a baseline. The results produced were determined to properly capture the fundamental behavior of the engine as observed in the operation of the prototype and demonstrated that the design of engine technology could be improved over the baseline using the developed computational model. Furthermore, the results of this study demonstrate the applicability of using a multi-objective optimization-driven approach to conduct conceptual design efforts for various engine system technologies.

scholarly journals RESILIENCE-COST TRADEOFF SUPPLY CHAIN PLANNING FOR THE PREFABRICATED CONSTRUCTION PROJECT

2021 ◽  
Vol 27 (1) ◽  
pp. 45-59
Author(s):  
Hong Zhang ◽  
Lu Yu
Keyword(s):  
Supply Chain ◽  
Optimization Model ◽  
Construction Project ◽  
Transportation Cost ◽  
Planning Problem ◽  
Multi Objective Optimization ◽  
Planning Stage ◽  
Multi Objective ◽  
Construction Schedule

Delivery of the prefabricated components may be disrupted by low productivity and various of traffic restrictions, thus delaying the prefabricated construction project. However, planning of the prefabricated component supply chain (PCSC) under disruptions has seldom been studied. This paper studies the construction schedule-dependent resilience for the PCSC plan by considering transportation costs and proposes a multi-objective optimization model. First, the PCSC planning problem regarding schedule-dependent resilience and resultant transportation cost is analyzed. Second, a quantification scheme of the schedule-dependent resilience of the PCSC plan is proposed. Third, formulation of the resilience-cost tradeoff optimization model for the PCSC planning is developed. Fourth, the multi-objective particle swarm optimization (MOPSO)-based method for solving the resilience-cost tradeoff model is presented. Finally, a case study is presented to demonstrate and justify the developed method. This study contributes to the knowledge and methodologies for PCSC management by addressing resilience at the planning stage.

Robust multi-objective optimization for solving the RFID network planning problem

2021 ◽  
Vol 8 (4) ◽  
pp. 616-626
Author(s):  
S. Ait Lhadj Lamin ◽  
◽  
A. Raghib ◽  
B. Abou El Majd ◽  
◽  
...  
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
New Technology ◽  
Network Planning ◽  
Environmental Parameters ◽  
Planning Problem ◽  
Optimization Approach ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Multi Objective

Radio-frequency identification (RFID) is a new technology used for identifying and tracking objects or people by radio-frequency waves to facilitate automated traceability and data collection. The RFID system consists of an electronic tag attached to an object, readers, and a middleware. In the latest real applications based on the RFID technology, the deployment of readers has become a central issue for RFID network planning by means of optimizing several objectives such as the coverage of tags, the number of readers, and the readers/tags interferences. In practice, the system is affected by uncertainty and uncontrollable environmental parameters. Therefore, the optimal solutions to the RFID network planning problem can be significantly reduced with uncontrollable variations in some parameters, such as the reader's transmitted power. In this work, we propose a robust multi-objective optimization approach to solve the deployment of RFID readers. In this way, we achieve robust optimal solutions that are insensitive to uncertainties in the optimization parameters.

Energy-optimal motion planning of a biped pole-climbing robot with kinodynamic constraints

2018 ◽  
Vol 45 (3) ◽  
pp. 343-353 ◽  
Author(s):  
Xuefeng Zhou ◽  
Li Jiang ◽  
Yisheng Guan ◽  
Haifei Zhu ◽  
Dan Huang ◽  
...  
Keyword(s):  
Motion Planning ◽  
Optimal Path ◽  
Planning Method ◽  
Outdoor Environment ◽  
Planning Problem ◽  
Climbing Robot ◽  
Optimal Motion ◽  
Climbing Robots ◽  
Content Type ◽  
Optimal Motion Planning

Purpose Applications of robotic systems in agriculture, forestry and high-altitude work will enter a new and huge stage in the near future. For these application fields, climbing robots have attracted much attention and have become one central topic in robotic research. The purpose of this paper is to propose an energy-optimal motion planning method for climbing robots that are applied in an outdoor environment. Design/methodology/approach First, a self-designed climbing robot named Climbot is briefly introduced. Then, an energy-optimal motion planning method is proposed for Climbot with simultaneous consideration of kinematic constraints and dynamic constraints. To decrease computing complexity, an acceleration continuous trajectory planner and a path planner based on spatial continuous curve are designed. Simulation and experimental results indicate that this method can search an energy-optimal path effectively. Findings Climbot can evidently reduce energy consumption when it moves along the energy-optimal path derived by the method used in this paper. Research limitations/implications Only one step climbing motion planning is considered in this method. Practical implications With the proposed motion planning method, climbing robots applied in an outdoor environment can commit more missions with limit power supply. In addition, it is also proved that this motion planning method is effective in a complicated obstacle environment with collision-free constraint. Originality/value The main contribution of this paper is that it establishes a two-planner system to solve the complex motion planning problem with kinodynamic constraints.

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