Abstract
Assembly path planning of complex products in virtual assembly is a necessary and complicated step, which will become long and inefficient if the assembly path of each part is completely planned in the assembly space. The coincidence or partial coincidence of the assembly paths of some parts provides an opportunity to solve this problem. A path planning algorithm based on prior path reuse (PPR algorithm) is proposed in this paper, which realizes rapid planning of an assembly path by reusing the planned paths. The core of the PPR algorithm is a dual-tree fusion strategy for path reuse, which is implemented by improving the rapidly exploring random tree star (RRT *) algorithm. The dual-tree fusion strategy is used to find the nearest prior node, the prior connection node, the nearest exploring node, and the exploring connection node and to connect the exploring tree to the prior tree after the exploring tree is extended to the prior space. Then, the optimal path selection strategy is used to calculate the costs of all planned paths and select the one with the minimum cost as the optimal path. The PPR algorithm is compared with the RRT * algorithm in path planning for one start node and multiple start nodes. The results show that the total time and the number of sampling points for assembly path planning of batch parts using the PPR algorithm are far less than those using the RRT * algorithm.
Highlights
Digital assembly is one of the key technologies in the design and manufacturing of complex products [1,2,3,4]
The PPR algorithm proposed in this paper improves the rapidly exploring random tree (RRT) * algorithm by developing some prior path reuse strategies to rapidly generate an asymptotically optimal assembly path by reusing the prior paths after the path exploring tree is expanded to the prior path space
The above-mentioned path planning results presented in Table 2 show that the minimum number of sampling points and the calculation time required by the PPR algorithm are much smaller than those of the RRT * algorithm, which verifies the effectiveness of the Minimum Number of
Summary
Digital assembly is one of the key technologies in the design and manufacturing of complex products [1,2,3,4]. Planning (AP) is one of the most important bases and premises of the assembly process and has some main subproblems: assembly sequence planning (ASP), assembly motion planning (AMP), assembly resource planning (ARP), assembly path planning (APP), etc These problems have proven to be either non-deterministic polynomial hard (NP-hard) or non-deterministic polynomial complete (NP-complete), and many studies have been conducted to solve them efficiently [14,15,16,17,18,19]. For most of the APP methods mentioned above, it takes a large amount of repeated computation and leads to low planning efficiency and high costs if generating the assembly path through all parts. In these studies, rapidly exploring random tree (RRT) and probabilistic roadmap planners (PRM) are two typical random sampling algorithms in path planning. The proposed method in this paper performs well with a small sampling size which reduces repeated calculations by reusing planned assembly paths and improves the efficiency of new path planning
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