NOVEL APPROACH FOR ROBOT PATH PLANNING BASED ON NUMERICAL ARTIFICIAL POTENTIAL FIELD AND GENETIC ALGORITHM

A novel approach for collision-free path planning of a multiple degree-of-freedom （DOF） articulated robot in a complex environment is proposed. Firstly, based on visual neighbor point （VNP）, a numerical artificial potential field is constructed in Cartesian space, which provides the heuristic information, effective distance to the goal and the motion direction for the motion of the robot joints. Secondly, a genetic algorithm, combined with the heuristic rules, is used in joint space to determine a series of contiguous configurations piecewise from initial configuration until the goal configuration is attained. A simulation shows that the method can not only handle issues on path planning of the articulated robots in environment with complex obstacles, but also improve the efficiency and quality of path planning.

Path planning for unmanned aerial vehicles in surveillance tasks under wind fields

The optimal path planning for fixed-wing unmanned aerial vehicles(UAVs) in multi-target surveillance tasks(MTST) in the presence of wind is concerned.To take into account the minimal turning radius of UAVs,the Dubins model is used to approximate the dynamics of UAVs.Based on the assumption,the path planning problem of UAVs in MTST can be formulated as a Dubins traveling salesman problem(DTSP).By considering its prohibitively high computational cost,the Dubins paths under terminal heading relaxation are introduced,which leads to significant reduction of the optimization scale and difficulty of the whole problem.Meanwhile,in view of the impact of wind on UAVs' paths,the notion of virtual target is proposed.The application of the idea successfully converts the Dubins path planning problem from an initial configuration to a target in wind into a problem of finding the minimal root of a transcendental equation.Then,the Dubins tour is derived by using differential evolution(DE) algorithm which employs random-key encoding technique to optimize the visiting sequence of waypoints.Finally,the effectiveness and efficiency of the proposed algorithm are demonstrated through computational experiments.Numerical results exhibit that the proposed algorithm can produce high quality solutions to the problem.

Machine learning inspired workflow to revise field development plan under uncertainty

We present an efficient and risk-informed closed-loop field development (CLFD) workflow for recurrently revising the field development plan (FDP) using the accrued information. To make the process practical, we integrated multiple concepts of machine learning, an intelligent selection process to discard the worst FDP options and a growing set of representative reservoir models. These concepts were combined and used with a cluster-based learning and evolution optimizer to efficiently explore the search space of decision variables. Unlike previous studies, we also added the execution time of the CLFD workflow and worked with more realistic timelines to confirm the utility of a CLFD workflow. To appreciate the importance of data assimilation and new well-logs in a CLFD workflow, we carried out researches at rigorous conditions without a reduction in uncertainty attributes. The proposed CLFD workflow was implemented on a benchmark analogous to a giant field with extensively time-consuming simulation models. The results underscore that an ensemble with as few as 100 scenarios was sufficient to gauge the geological uncertainty, despite working with a giant field with highly heterogeneous characteristics. It is demonstrated that the CLFD workflow can improve the efficiency by over 85% compared to the previously validated workflow. Finally, we present some acute insights and problems related to data assimilation for the practical application of a CLFD workflow.

Design and analysis of an advanced thermal management system for the solar close observations and proximity experiments spacecraft

In this paper,the mission and the thermal environment of the Solar Close Observations and Proximity Experiments(SCOPE)spacecraft are analyzed,and an advanced thermal management system(ATMS)is designed for it.The relationship and functions of the integrated database,the intelligent thermal control system and the efficient liquid cooling system in the ATMS are elaborated upon.For the complex thermal field regulation system and extreme space thermal environment,a modular simulation and thermal field planning method are proposed,and the feasibility of the planning algorithm is verified by numerical simulation.A solar array liquid cooling system is developed,and the system simulation results indicate that the temperatures of the solar arrays meet the requirements as the spacecraft flies by perihelion and aphelion.The advanced thermal management study supports the development of the SCOPE program and provides a reference for the thermal management in other deep-space exploration programs.

Risk Identification and Mitigation Strategies for Deepwater Oilfields Development

Deepwater oilfields will become main sources of the world's oil and gas production.It is characterized with high technology,huge investment,long duration,high risk and high profit.It is a huge system project,including exploration and appraising,field development plan(FDP)design,implementation,reservoir management and optimization.Actually,limited data,international environment and oil price will cause much uncertainty for FDP design and production management.Any unreasonable decision will cause huge loss.Thus,risk foreseeing and mitigation strategies become more important.This paper takes AKPO and EGINA as examples to analyze the main uncertainties,proposes mitigation strategies,and provides valuable experiences for the other deepwater oilfields development.

A collaborative assembly for low-voltage electrical apparatuses

Low-voltage electrical apparatuses(LVEAs)have many workpieces and intricate geometric structures,and the assembly process is rigid and labor-intensive,and has little balance.The assembly process cannot readily adapt to changes in assembly situations.To address these issues,a collaborative assembly is proposed.Based on the requirements of collaborative assembly,a colored Petri net(CPN)model is proposed to analyze the performance of the interaction and self-government of robots in collaborative assembly.Also,an artificial potential field based planning algorithm(AFPA)is presented to realize the assembly planning and dynamic interaction of robots in the collaborative assembly of LVEAs.Then an adaptive quantum genetic algorithm(AQGA)is developed to optimize the assembly process.Lastly,taking a two-pole circuit-breaker controller with leakage protection(TPCLP)as an assembly instance,comparative results show that the collaborative assembly is cost-effective and flexible in LVEA assembly.The distribution of resources can also be optimized in the assembly.The assembly robots can interact dynamically with each other to accommodate changes that may occur in the LVEA assembly.