Optimization method and experimental research on attitude adjustment scheme of attitude adaptive rescue robot

Abstract

To improve the space attitude adjustment efficiency of the robot designed in this study, the average water level height variation of each ballast tank during the rescue process and the ballast water filling mass before the rescue process are taken as optimization variables, the minimal ballasting time during rescue process as the optimisation objective, and the heel and trim inclination angle, and stability in the rescue process as the constraint conditions. For the first time, an optimization method of a rescue robot space attitude adjustment scheme based on a dynamic programming algorithm is proposed. Relevant experiments and data collection were carried out with a model robot with a physical ratio of 1:2. MATLAB simulation and model robot experimental results show that compared with an empirical scheme, the total deployment time and ballast water total allocation mass are reduced by 11.07% and 30.79%, respectively, and the heel and trim angle variation stability is increased by 4.18% and 8.67%, respectively. The optimization model and algorithm are beneficial to improve the space attitude adjustment efficiency and stability of the rescue robot in this paper, and it is also easier to transfer to other fields of ballast water allocation, which has strong practical engineering significance.

Point-to-Point-Based Optimization Method of Ballast Water Allocation for Revolving Floating Cranes with Experimental Verification

“…Topalov et al [5] developed an information system for the real-time acquisition of the operational parameters of the ballast system, and the research results confirmed the effectiveness of this information system for realizing the automatic allocation of ballast water. Wang et al [6] proposed an optimization method based on the dynamic programming algorithm for the ballast process of underwater rescue robots, established and solved the optimization model with the minimum ballast time as the optimization objective, and verified the feasibility of the optimization results by using the simulation results. Adi et al [7] divided the lifting processes into several stages and employed a multi-objective evolutionary algorithm to determine the optimal ballast-allocation scheme for each stage, with numerical simulation results finding that the obtained allocation scheme can significantly improve the ballast efficiency compared with traditional approaches.…”