Abstract
At present, designing and planning of robots are mainly based on path planning. This mode cannot meet requirements of real-time and precise planning for robots, especially under complex working conditions. Therefore, a parallel collaborative planning strategy is proposed in this paper, which parallel collaborates optimal task allocation planning and optimal local path planning. That is, according to real-time dynamic working environment of robots, the dynamic optimal task allocation planning strategy for coupled system of robot in low coupling state is adopted, to improve real-time working efficiency of underground heavy-load robot. Meanwhile, the parallel elite particle swarm optimization algorithm is adopted to improve accuracy of path tracking and controlling. Finally, the two planning strategies are collaborated parallel to realize intelligent and efficient planning of whole complex coupled system for underground heavy-load robot. The simulation and experiment results show that the parallel collaborative planning algorithm proposed in this paper has perfect controlling effects: Total flow of overall system is saved by 11.03 L, execution time saved by 16.8 s and implementation efficiency has been improved by 10 times. Therefore, the parallel collaborative planning strategy proposed in this paper can not only meet requirements of high efficiency and precision of intelligent robot under complex working conditions, but also greatly improve real-time working effectiveness and robustness of robots, so as to provide a reference for dynamic planning of complex intelligent engineering machinery, and also supply design basis for development of multi-robot collaborative system.
Highlights
China is the largest coal producer and consumer in the world
Under the premise of minimum cost schemes and that all the constraints are satisfied, based on task decomposition, this paper proposes an optimal task allocation planning strategy by coordinating parallelly the complex MDO system and dynamic environment, and plans the optimal task allocation for the robot
To achieve the optimal planning and design of the whole MDO system, this paper proposes a double-layer parallel collaborative planning strategy based on optimal task allocation and Parallel Elite Particle Swarm Optimization (PEPSO) path planning
Summary
China is the largest coal producer and consumer in the world. According to the statistics of Ministry of Energy in 2018, the output of raw coal in China reached 3.55 billion tons, and more than 90% of them still rely on underground mining.[1]. Based on the above researches and complex coupled system of the underground heavy-load robot itself, this paper proposes a double-layer dynamic parallel collaborative planning strategy. The double layer dynamic parallel collaborate planning strategy proposed in this paper can provide a basis for realization of precise intelligent navigation system of underground mining robots in complex tunnel environment. R1(k P41)g).[13] We can conclude that the mechanical body of the underground heavy-load robot adopts complex spatial multi-DOF structure This serial structure can meet the flexible action requirements of robots in confined space, it increases the difficulty of precise control and implementation for overall system. Under the condition that all the constraints are satisfied, the dynamic spatial environment and changes of operation are coordinately considered, and the executive tasks of each actuator mechanism (D1, D2, and D3) are reasonably distributed according to eqPuation (8).
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