Robots whose geometric structure does not meet the Pieper criterion are called general robots. For the inverse kinematic operation of general robots, the closed solution method cannot be solved, and the numerical solution calculation amount is too large and the singular position cannot be calculated. To solve this problem, this paper proposes an inverse kinematics calculation method based on improved particle swarm optimization (PSO) algorithm and applicable to general robots. In order to avoid the particle update rate not adapting to each stage of the optimization process, a nonlinear dynamic inertia weight adjustment method based on the concept of similarity is introduced, so that the search process is more robust; in addition, to overcome the problem of local optimal solution At the same time, multiple populations are introduced to perform optimization search at the same time, and the immigration operator is proposed to increase the diversity of the particle population in the iteration. This article uses Comau NJ-220 robot for test verification, compared with the original PSO and multi-subswarm algorithm, the results show that the proposed improved PSO has higher algorithm stability for general robot kinematics inverse solution problems, and can greatly improve the convergence accuracy and speed. This method provides a new solution to the field of robot inverse kinematics, and provides a more efficient and stable kinematics foundation for robot motion planning.

Iron and steel scrap could substitute ironstone as the raw material for steelmaking and continuous casting production, and effectively reduce emissions of waste gas, water, and residue. The iron and steel scrap steelmaking and continuous casting (ISSSC) process is a compact production process involving scrap steel as raw material, electric arc furnace (AF), refining (RH/CAS/KIP/LF) and continuous casting (CC). Traditional scheduling of ISSSC has emphasized production performance indexes without considering energy and environmental factors. This study presents a new mathematical programming model for the scheduling of ISSSC that considers energy consumption with time-of-use electricity price and associated carbon emissions in addition to production performance indexes. The complexity of the multi-objective mixed-integer programming problem increases if the interval of the time-of-use electricity price is enlarged. Furthermore, the main and accessorial equipment is considered separately, and the flexible processing time is controllable. Under the linearization augmented Lagrangian relaxation framework, a solution methodology, based on controllable direction and a step surrogate sub-gradient Lagrangian algorithm, is developed according to the charge-based sub-problem splitting policy. This solution could keep a smaller intersection angle between the gradient and the optimal direction, damping the zigzag, reducing the number of iterations, and ensuring the optimization efficiency for the scheduling of ISSSC. Numerical testing results show that the method generates high quality schedules in a timely fashion, while considering the time-of-use electricity price and associated carbon emissions.

Steelmaking-continuous casting (SCC) is the crucial step in steel production, and the process involves multiple production equipment (converters, refiners and continuous casting machine) and coordinated transport by multiple cranes. Scientific and reasonable scheduling of SCC with cranes can regulate the production rhythm and improve the efficiency of steel production. However, production equipment scheduling and crane scheduling are often solved separately in existing studies of SCC, which ignores the coupling relationship between them. Therefore, this letter proposes an integrated mixed integer linear programming (MILP) model for the SCC scheduling problem with cranes. Firstly, a MILP model with minimum residence time and completion time as objectives is established. Secondly, a disjunctive graph is established to describe the relationship between the processing tasks of the production equipment and the transport tasks of the crane. Thirdly, A new MILP model is proposed by simplifying the crane transport constraints based on the disjunctive graph. Finally, the validity and the superior computational performance of the new model are verified through 35 instances.