To solve the problem of poor masonry quality of traditional wall-building robots in an uncertain viscoelastic contact environment while reducing energy consumption, reducing contact forces with the environment, and im...
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To solve the problem of poor masonry quality of traditional wall-building robots in an uncertain viscoelastic contact environment while reducing energy consumption, reducing contact forces with the environment, and improving work efficiency and smoothness, a segmented multiobjective trajectory optimization method is proposed based on radial basis function (RBF) and nondominated sorting genetic algorithm II (NSGA-II). The method divides the motion trajectory into the free motion segment and the masonry segment. In the masonry segment, the compensation variable is introduced at the brick-stopping position, and the values of design variables are obtained by Latin hypercube sampling. The relationship between the objective functions and the design variables is established by using an RBF substitution model. The optimal design is carried out by the NSGA-II, and the compromise solution is obtained by using the technique for order preference by similarity to an ideal solution algorithm. On this basis, a multiobjective trajectory optimization method based on seven times nonuniform B-spline curves is proposed for the free motion segment. According to the performance indicators, such as operation efficiency, trajectory smoothness, and energy consumption, the compromise solution is again sought and obtained. Finally, the proposed trajectoryoptimization method is compared with the standard gate-shaped trajectory planning method. The results show that after trajectoryoptimization, the masonry efficiency of the wall-building robot is improved by 28.36%, and the energy consumption and trajectory smoothness are reduced by 28.68% and 93.81%, respectively. At the same time, the contact force with the environment is reduced by 12.26%, and the masonry error is reduced from 2.67 to 0.13 mm. These results can contribute to the construction of walls and improve the masonry quality of bricks while considering other performance indicators.
Mobile edge computing (MEC) is used to provide IT services environment and cloud computing capabilities at the edge of the network. As the technology of unmanned aerial vehicles (UAVs) matures, the growing attempts ha...
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Mobile edge computing (MEC) is used to provide IT services environment and cloud computing capabilities at the edge of the network. As the technology of unmanned aerial vehicles (UAVs) matures, the growing attempts have been made to use UAVs to replace fixed ground stations for MEC due to their flexibility. In this work, we study the multiobjective trajectory optimization for mobile edge computing system assisted by a single UAV, where the UAV is used to provide computing services for Internet of Things (IoT) devices located on the ground. A multiobjective trajectory optimization problem is formulated, which not only needs to minimize the energy consumption of the MEC system to provide computing services to all IoT devices, but also minimize the task urgency indicator by optimizing the UAV's flight trajectory. In this problem, the number and the locations of hover points (HPs) of UAVs have been taken into consideration. To solve this problem, a multiobjective trajectory optimization algorithm with a cutting and padding encoding strategy is proposed, where the cutting and padding encoding strategy is used to help optimize the population whose individuals may have different lengths. The verification experiments are carried out on a set of instances with up to 400 IoT devices and the experimental results demonstrate the promising performance of the proposed algorithm for trajectoryoptimization problems in a single-UAV-assisted MEC system.
In this paper, optimal trajectories of a spacecraft traveling from Earth to Moon using impulsive maneuvers (Delta V maneuvers) are investigated. The total flight time and the summation of impulsive maneuvers Delta V a...
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In this paper, optimal trajectories of a spacecraft traveling from Earth to Moon using impulsive maneuvers (Delta V maneuvers) are investigated. The total flight time and the summation of impulsive maneuvers Delta V are the objective functions to be minimized. The main celestial bodies influencing the motion of the spacecraft in this journey are Sun, Earth and Moon. Therefore, a three-dimensional restricted four-body problem (R4BP) model is utilized to represent the motion of the spacecraft in the gravitational field of these celestial bodies. The total Delta V of the maneuvers is minimized by eliminating the Delta V required for capturing the spacecraft by Moon. In this regard, only a mid-course impulsive maneuver is utilized for Moon ballistic capture. To achieve such trajectories, the optimization problem is parameterized with respect to the orbital elements of the ballistic capture orbits around Moon, the arrival date and a mid-course maneuver time. The equations of motion are solved backward in time with three impulsive maneuvers up to a specified low Earth parking orbit. The results show high potential and capability of this type of parameterization in finding several Pareto-optimal trajectories. Using the non-dominated sorting genetic algorithm with crowding distance sorting (NSGA-II) for the resulting multiobjectiveoptimization problem, several trajectories are discovered. The resulting trajectories of the presented scheme permit alternative trade-off studies by designers incorporating higher level information and mission priorities. (C) 2009 COSPAR. Published by Elsevier Ltd. All rights reserved.
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