In Robot Cells, the robot trajectory planning is a major issue to be observed, because the accurate trajectory optimize the application cycle time. In this context, this work presents a study and simulation of robot t...
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ISBN:
(纸本)9781665490481
In Robot Cells, the robot trajectory planning is a major issue to be observed, because the accurate trajectory optimize the application cycle time. In this context, this work presents a study and simulation of robot trajectory planning using the Probabilistic Roadmap Method (prm) algorithm. A Selective Compliance Articulated Robot Arm (SCARA) robot (TS60, Staubli) and the RoboDK post processor software are employed. In order to analyze the trajectory defined by the prm algorithm, two scenarios are developed in RoboDK and the robot cell cycle time is measured utilizing a Python script.
To ensure the safe production of mines, the intelligent trend of underground mining operations is gradually advancing. However, the operational environment of subterranean mining is intricate, making the conventional ...
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To ensure the safe production of mines, the intelligent trend of underground mining operations is gradually advancing. However, the operational environment of subterranean mining is intricate, making the conventional path-planning algorithm used by mining inspection robots frequently inadequate for real requirements. To safeguard the mining inspection robot, targeting the problem of low search efficiency and path redundancy in the path planning of the existing rapidly exploring random tree (RRT) algorithm in the narrow and complex unstructured environment, a path-planning algorithm combining improved RRT and a probabilistic road map (prm) is proposed. Initially, the target area is efficiently searched according to the fan-shaped goal orientation strategy and the adaptive step size expansion strategy. Subsequently, the prm algorithm and the improved RRT algorithm are combined to reduce the redundant points of the planning path. Ultimately, considering the kinematics of the vehicle, the path is optimized by the third-order Bessel curve. The experimental simulation results show that the proposed path-planning algorithm has a higher success rate, smoother path, and shorter path length than other algorithms in complex underground mining environments, which proves the effectiveness of the proposed algorithm.
The selection of random sampling points is crucial for the path quality generated by probabilistic roadmap (prm) algorithm. Increasing the number of sampling points can enhance path quality. However, it may also lead ...
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The selection of random sampling points is crucial for the path quality generated by probabilistic roadmap (prm) algorithm. Increasing the number of sampling points can enhance path quality. However, it may also lead to extended convergence time and reduced computational efficiency. Therefore, an improved probabilistic roadmap algorithm (TL-prm) is proposed based on topological discrimination and lazy collision. TL-prm algorithm first generates a circular grid area among start and goal points. Then, it constructs topological nodes. Subsequently, elliptical sampling areas are created between each pair of adjacent topological nodes. Random sampling points are generated within these areas. These sampling points are interconnected using a layer connection strategy. An initial path is generated using a delayed collision strategy. The path is then adjusted by modifying the nodes on the convex outer edges to avoid obstacles. Finally, a reconnection strategy is employed to optimize the path. This reduces the number of path waypoints. In dynamic environments, TL-prm algorithm employs pose adjustment strategies for semi-static and dynamic obstacles. It can use either the same or opposite pose adjustments to avoid dynamic obstacles. Experimental results indicate that TL-prm algorithm reduces the average number of generated sampling points by 70.9% and average computation time by 62.1% compared with prm* and prm-Astar algorithms. In winding and narrow passage maps, TL-prm algorithm significantly decreases the number of sampling points and shortens convergence time. In dynamic environments, the algorithm can adjust its pose orientation in real time. This allows it to safely reach the goal point. TL-prm algorithm provides an effective solution for reducing the generation of sampling points in prm algorithm.
Plug and produce demonstrators handles multiple processes in the industry, appropriate path planning is essential and at the same time there is an increasing emphasis on more sustainable processes. To ensure the susta...
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ISBN:
(纸本)9781728129891
Plug and produce demonstrators handles multiple processes in the industry, appropriate path planning is essential and at the same time there is an increasing emphasis on more sustainable processes. To ensure the sustainability and automate these processes optimized path planning is required. We present an implementation of a path planning algorithm, which creates a smooth collision free path and considers energy use. In the paper, we demonstrated the implementation of prm (Probabilistic Road Map) path planning and Dijkstra based optimization algorithm in a simulation environment and thereafter test in a real plug and produce demonstrator. To validate the simulated results the real energy was measured through the signal analyzer online. The measured results outlined in this paper includes;computational time, move along path time, and energy use with different loads. From the experiments and results we conclude that the combination of the two algorithms, prm with Dijkstra, can be used to generate a collision free optimized path. Here we have considered the distance as the cost function for Dijkstra optimization algorithm and measured the energy of the collision free optimized path. The practical implication of this research is as an enabler for any kind of application where there are large variations of orders e.g., kitting techniques in assembly operations for manufacturing industry.
Robots are mechanical devices programmed to perform certain repetitive functions. They can also be programmed to perform many tasks that can be complex or dangerous for humans. In order for robots to be used more effe...
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The research on motion planning of a virtual agent is very important in computer animation, where path planning is the most representative. In this paper we do an in-depth study about the problem of static and global ...
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ISBN:
(纸本)9783642226939
The research on motion planning of a virtual agent is very important in computer animation, where path planning is the most representative. In this paper we do an in-depth study about the problem of static and global path planning for three-dimensional virtual animals in the marine environment, constructed the path planning strategy of a fast randomized algorithm. First of all, we carry out a pre-processing on the environment, proposed the new partial random sampling strategy and the sequential connecting strategy, which to generate "roadmap" of three-dimensional free space. And then, we put forward a driven step by step inquiring strategy, analyzed two feature-parameters and time complexity of the inquiring strategy. Finally, the simulation experiments show the effectiveness of algorithm in solving the issue of static and global path planning in three-dimensional.
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