Dynamic Path Planning of Mobile Robot Based on Improved Sparrow Search Algorithm

Abstract

Aiming at the shortcomings of the traditional sparrow search algorithm (SSA) in path planning, such as its high time-consumption, long path length, it being easy to collide with static obstacles and its inability to avoid dynamic obstacles, this paper proposes a new improved SSA based on multi-strategies. Firstly, Cauchy reverse learning was used to initialize the sparrow population to avoid a premature convergence of the algorithm. Secondly, the sine–cosine algorithm was used to update the producers’ position of the sparrow population and balance the global search and local exploration capabilities of the algorithm. Then, a Lévy flight strategy was used to update the scroungers’ position to avoid the algorithm falling into the local optimum. Finally, the improved SSA and dynamic window approach (DWA) were combined to enhance the local obstacle avoidance ability of the algorithm. The proposed novel algorithm is named ISSA-DWA. Compared with the traditional SSA, the path length, path turning times and execution time planned by the ISSA-DWA are reduced by 13.42%, 63.02% and 51.35%, respectively, and the path smoothness is improved by 62.29%. The experimental results show that the ISSA-DWA proposed in this paper can not only solve the shortcomings of the SSA but can also plan a highly smooth path safely and efficiently in the complex dynamic obstacle environment.

Application of the Improved Rapidly Exploring Random Tree Algorithm to an Insect-like Mobile Robot in a Narrow Environment

“…The experimental test [29] environment was the sixth floor of the Yif It can be observed from the experimental path planning diagram a data that the improved RRT algorithm presented in this paper exhibits cle over the RRT algorithm, RRT-connect algorithm, and RRT* algorithm in planning time, the number of extended sampling nodes, and path cost. By improved RRT algorithm for pruning and smoothing the path, the resul remains smooth and unaffected by the presence of obstacles.…”

“…The experimental test [ 29 ] environment was the sixth floor of the Yifu Science and Technology Building at China Jiliang University. The environmental map, which is shown in the form of portable gray map depicted in Figure 17 , illustrates the elevator entrance at the starting point, the corridor along the path, and the elevator entrance at the target point, respectively.…”

Enhancing sparrow search algorithm with OCSSA: Integrating osprey optimization and Cauchy mutation for improved convergence and precision

“…Zhang et al enhanced SSA's exploration and diversity using sine chaotic mapping and adaptive weight factors, improving convergence speed [16]. Liu et al improved SSA for path planning and drone routing by introducing Cauchy reverse learning and Cauchy-Gaussian mechanisms to prevent premature convergence and enhance search capabilities [17]. Liu et al balanced the search and exploitation capabilities of SSA using adaptive weight factors and enhanced SSA's ability to overcome stagnation with the Cauchy-Gaussian mechanism [18].…”

“…Liu et al. improved SSA for path planning and drone routing by introducing Cauchy reverse learning and Cauchy–Gaussian mechanisms to prevent premature convergence and enhance search capabilities [17]. Liu et al.…”

Perceiving like a Bat: Hierarchical 3D Geometric–Semantic Scene Understanding Inspired by a Biomimetic Mechanism

“…In recent years, some studies have proposed partial solutions to these problems, such as simultaneous localization and mapping (SLAM) [ 1 , 2 , 3 , 4 , 5 , 6 ], autonomous path replanning [ 7 , 8 , 9 , 10 ], pattern recognition [ 11 , 12 , 13 , 14 ], and iterative reconstruction [ 15 , 16 , 17 ]. Nevertheless, research in these fields has traditionally proceeded in isolation, and there is currently no complete full-stack solution for scene parsing.…”