Micro-Assembly Sequence and Path Planning Using Subassemblies

2018 ◽  
Vol 10 (6) ◽  
Author(s):  
Vinoth Venkatesan ◽  
Joseph Seymour ◽  
David J. Cappelleri
Keyword(s):  
Path Planning ◽  
Search Algorithm ◽  
Assembly Sequence Planning ◽  
Assembly Sequence ◽  
Micro Assembly ◽  
Final Assembly ◽  
Custom Made ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Assembly Constraints

This paper presents a novel assembly sequence planning (ASP) procedure utilizing a subassembly based search algorithm (SABLS) for micro-assembly applications involving geometric and other assembly constraints. The breakout local search (BLS) algorithm is adapted to provide sequencing solutions in assemblies with no coherent solutions by converting the final assembly into subassemblies which can be assembled together. This is implemented using custom-made microparts which fit together only in a predefined fashion. Once the ASP is done, the parts are manipulated from a cluttered space to their final positions in the subassemblies using a path-planning algorithm based on rapidly exploring random tree (RRT*), a random-sampling based execution, and micromanipulation motion primitives. The entire system is demonstrated by assembling LEGO® inspired microparts into various configurations which involve subassemblies, showing the versatility of the system.

Dynamic Path Planning Algorithm Based on Chaos Genetic Vehicle Navigation

2011 ◽  
Vol 142 ◽  
pp. 12-15
Author(s):  
Ping Feng
Keyword(s):  
Path Planning ◽  
Search Algorithm ◽  
Traffic Condition ◽  
Planning Time ◽  
Dynamic Path Planning ◽  
Short Period ◽  
Planning Algorithm ◽  
Dynamic Path ◽  
Path Planning Algorithm ◽  
Chaos Genetic Algorithm

The paper puts forward the dynamic path planning algorithm based on improving chaos genetic algorithm by using genetic algorithms and chaos search algorithm. In the practice of navigation, the algorithm can compute at the best path to meet the needs of the navigation in such a short period of planning time. Furthermore,this algorithm can replan a optimum path of the rest paths after the traffic condition in the sudden.

scholarly journals Hierarchical Path-Planning for Mobile Robots Using a Skeletonization-Informed Rapidly Exploring Random Tree*

2020 ◽  
Vol 10 (21) ◽  
pp. 7846
Author(s):  
Hyejeong Ryu
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Search Algorithm ◽  
Random Tree ◽  
Graph Search ◽  
Complex Environments ◽  
Sampling Process ◽  
Planning Algorithm ◽  
Local Grid ◽  
Path Planning Algorithm

An efficient, hierarchical, two-dimensional (2D) path-planning method for large complex environments is presented in this paper. For mobile robots moving in 2D environments, conventional path-planning algorithms employ single-layered maps; the proposed approach engages in hierarchical inter- and intra-regional searches. A navigable graph of an environment is constructed using segmented local grid maps and safe junction nodes. An inter-regional path is obtained using the navigable graph and a graph-search algorithm. A skeletonization-informed rapidly exploring random tree* (SIRRT*) efficiently computes converged intra-regional paths for each map segment. The sampling process of the proposed hierarchical path-planning algorithm is locally conducted only in the start and goal regions, whereas the conventional path-planning should process the sampling over the entire environment. The entire path from the start position to the goal position can be achieved more quickly and more robustly using the hierarchical approach than the conventional single-layered method. The performance of the hierarchical path-planning is analyzed using a publicly available benchmark environment.

An Algorithm of Non-Iso Parameters Cutting Path Planning Using Toroid Cutter

2013 ◽  
Vol 373-375 ◽  
pp. 2088-2091
Author(s):  
Quan Liang ◽  
Dong Hai Su ◽  
Jie Wang ◽  
Ye Mu Wang
Keyword(s):  
Path Planning ◽  
Trajectory Planning ◽  
Tool Path ◽  
Search Algorithm ◽  
Free Form ◽  
Processing Efficiency ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Five Axis ◽  
Cutting Path

For the problem of poor processing efficiency of iso-parameter tool path planning algorithm, this paper proposed a non iso-parameter trajectory planning algorithm. First established a mathematical model of five-axis machining toroid cutter, then analyzed the toroid cutter and machining surface partial differential geometric properties, proposed one kind of iso-scallop path search algorithm. Finally, using the above algorithm developed an application of trajectory planning for free-form surface and generated tool paths for such surface. The trajectories generated verified the algorithm is practicable.

scholarly journals Path Planning and Control of a Quadrotor UAV Based on an Improved APF Using Parallel Search

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Tianpeng Huang ◽  
Deqing Huang ◽  
Na Qin ◽  
Yanan Li
Keyword(s):  
Path Planning ◽  
Local Minimum ◽  
Search Algorithm ◽  
External Disturbance ◽  
Target Point ◽  
Observation Error ◽  
Parallel Search ◽  
Planning And Control ◽  
Planning Algorithm ◽  
Path Planning Algorithm

Control and path planning are two essential and challenging issues in quadrotor unmanned aerial vehicle (UAV). In this paper, an approach for moving around the nearest obstacle is integrated into an artificial potential field (APF) to avoid the trap of local minimum of APF. The advantage of this approach is that it can help the UAV successfully escape from the local minimum without collision with any obstacles. Moreover, the UAV may encounter the problem of unreachable target when there are too many obstacles near its target. To address the problem, a parallel search algorithm is proposed, which requires UAV to simultaneously detect obstacles between current point and target point when it moves around the nearest obstacle to approach the target. Then, to achieve tracking of the planned path, the desired attitude states are calculated. Considering the external disturbance acting on the quadrotor, a nonlinear disturbance observer (NDO) is developed to guarantee observation error to exponentially converge to zero. Furthermore, a backstepping controller synthesized with the NDO is designed to eliminate tracking errors of attitude. Finally, comparative simulations are carried out to illustrate the effectiveness of the proposed path planning algorithm and controller.

scholarly journals Path planning for the Platonic solids on prescribed grids by edge-rolling

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252613
Author(s):  
Ngoc Tam Lam ◽  
Ian Howard ◽  
Lei Cui
Keyword(s):  
Path Planning ◽  
Search Algorithm ◽  
Penrose Tiling ◽  
Platonic Solid ◽  
Breadth First Search ◽  
Platonic Solids ◽  
Science And Art ◽  
Planning Algorithm ◽  
Position And Orientation ◽  
Path Planning Algorithm

The five Platonic solids—tetrahedron, cube, octahedron, dodecahedron, and icosahedron—have found many applications in mathematics, science, and art. Path planning for the Platonic solids had been suggested, but not validated, except for solving the rolling-cube puzzles for a cubic dice. We developed a path-planning algorithm based on the breadth-first-search algorithm that generates a shortest path for each Platonic solid to reach a desired pose, including position and orientation, from an initial one on prescribed grids by edge-rolling. While it is straightforward to generate triangular and square grids, various methods exist for regular-pentagon tiling. We chose the Penrose tiling because it has five-fold symmetry. We discovered that a tetrahedron could achieve only one orientation for a particular position.

scholarly journals Optimal Path Planning using Informed Probabilistic Road Map Algorithm

2021 ◽  
Author(s):  
Muhammad Aria ◽  
Keyword(s):  
Path Planning ◽  
Local Search ◽  
Search Algorithm ◽  
Optimal Path ◽  
Local Search Algorithm ◽  
Optimal Path Planning ◽  
Road Map ◽  
Planning Algorithm ◽  
Sample Points ◽  
Path Planning Algorithm

This study aims to propose a new path planning algorithm that can guarantee the optimal path solution. The method used is to hybridize the Probabilistic Road Map (PRM) algorithm with the Information Search Algorithm. This hybridization algorithm is called the Informed-PRM algorithm. There are two informed search methods used. The first method is the informed sampling through an ellipsoid subset whose eccentricity is dependent on the length of the shortest current solution that is successfully planned in that iteration. The second method is to use a local search algorithm. The basic PRM algorithm will be run in the first iteration. Since the second iteration, the generation of sample points in the PRM algorithm will be carried out based on information. The informed sampling method will be used to generate 50% of the sampling points. Meanwhile, the remaining number of sample points will be generated using a local search algorithm. Using several benchmark cases, we compared the performance of the Informed-PRM algorithm with the Rapidly Exploring Random Tree* (RRT*) and informed RRT* algorithm. The test results show that the Informed-PRM algorithm successfully constructs the nearly optimal path for all given cases. In producing the path, the time and path cost of the Informed-PRM algorithm is better than the RRT* and Informed RRT* algorithm. The Friedman test was then performed to check for the significant difference in performance between Informed-PRM with RRT* and Informed RRT*. Thus, the Informed-PRM algorithm can be implemented in various systems that require an optimal path planning algorithm, such as in the case of medical robotic surgery or autonomous vehicle systems.

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