An In-Orbit Stereo Navigation Camera Self-Calibration Method for Planetary Rovers with Multiple Constraints

In order to complete the high-precision calibration of the planetary rover navigation camera using limited initial data in-orbit, we proposed a joint adjustment model with additional multiple constraints. Specifically, a base model was first established based on the bundle adjustment model, second-order radial and tangential distortion parameters. Then, combining the constraints of collinearity, coplanarity, known distance and relative pose invariance, a joint adjustment model was constructed to realize the in orbit self-calibration of the navigation camera. Given the problem of directionality in line extraction of the solar panel due to large differences in the gradient amplitude, an adaptive brightness-weighted line extraction method was proposed. Lastly, the Levenberg-Marquardt algorithm for nonlinear least squares was used to obtain the optimal results. To verify the proposed method, field experiments and in-orbit experiments were carried out. The results suggested that the proposed method was more accurate than the self-calibration bundle adjustment method, CAHVOR method (a camera model used in machine vision for three-dimensional measurements), and vanishing points method. The average error for the flag of China and the optical solar reflector was only 1 mm and 0.7 mm, respectively. In addition, the proposed method has been implemented in China’s deep space exploration missions.

Transformational Change for Achieving Scale: Lessons for a Greener Recovery

Achieving transformational changes that can be then effectively scaled up requires ambition in design, a supportive policy environment, sound project design and implementation, partnerships, and multistakeholder participation. This chapter presents a framework that can be applied at the design stage to plan for change and scaling up and provides relevant lessons based on GEF interventions. Achieving change and scale can be an iterative and a continuous process until impacts are generated at the magnitude and scope of the targeted scale. Successful transformations typically adopt a systems approach and address multiple constraints to attain environmental and other socioeconomic impacts.

Feasibility-Based Design Model For Road Vertical Alignment

Road vertical alignment design is a multi-objective design problem that needs to consider multiple constraints. Intelligent design based on optimization algorithms cannot wholly solve problems, such as multi-objective, uncertainty, and constraint dynamics. The article proposes a model of dynamically transforming design constraints into feasible regions as the design develops, to provide decision information before design actions rather than performing constraint evaluation after the design that reduces the empirical estimation. The design actions are divided into new design actions and modifying design actions, and corresponding feasible regions derived from constraints of design specifications and control elevations are established, respectively. Geometrical equations and program algorithms of feasible regions are described in the graphic environment, which is applied to the vertical alignment design to improve the design efficiency and decision-making level.

Research on machining parameter optimization in finishing milling with multiple constraints

High efficiency and high precision milling, as the eternal goal of CNC machining, needs to balance many constraints for selecting the most reasonable processing parameters. This paper presents an efficient machining parameter optimization method for finishing milling operation with multiple constraints. Firstly, under the multiple constraints of parameter feasible region, milling force, milling stability, roughness, and machining contour accuracy, a multi-variable parameter optimization model with machining efficiency as the objective is established. A four level cycle optimization strategy has been detailly described for solving the optimization problem, in which the feed per tooth is optimized by using the golden section method, and with the aid of the random vector search method, the spindle speed, radial, and axial depth cuts are both numerically iterated. The optimal machining parameter combination of the tooth number, feed per tooth, spindle speed, radial, and axial depth of cuts are achieved at last. Finally, the experimental verification results show that the proposed method can greatly improve the machining efficiency under chatter free condition and achieve an efficient finishing milling with consideration of the multiple constraints.