Energy Optimal Path Planning for Mobile Robots Based on Improved AD* Algorithm
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    Abstract:

    Path planning with distance and time constraints is often required for mobile robots. Meanwhile, reducing energy consumption is much more important in order to make robot perform more tasks and more efficiently with limit energy supply. Hence, it is important to minimize the energy consumption of mobile robots deployed in real world missions. One of the ways that can be accomplished is to choose the robot’s motion to minimize the mechanical and electrical energy usage required by the robot’s motion. However, this method will cause the robot accelerate or decelerate frequently. Taking into account the demand for reducing energy consumption and improving the efficiency of path planning, an energy optimal path planning method was proposed based on AD* algorithm. The energy consumption was reduced with the proposed energy optimal path planning method by taking into account the distance and time constraints. Firstly, the energy consumption of path was calculated by using the dynamic model and power model of the robot. The samplebased model predictive optimization algorithm was used to generate the optimal trajectory cluster based on the kinematics model of robot. Then, an energy optimal path planning method was proposed based on AD* search algorithm by integrating the energy consumption into the node’s evaluation function. Energy optimal path planning was carried out online to find the optimal energy consumption path, according to the connection relationship of nodes and environment map. Finally, the effectiveness of the proposed energy optimal path plannign method was confirmed by comparing the simulation results with distance optimal path planning method. The proposed energy optimal path planning method can be deployed on the mobile robot platform which served in outdoor terrain environments for decreasing energy consumption.

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History
  • Received:May 06,2018
  • Revised:
  • Adopted:
  • Online: September 10,2018
  • Published: September 10,2018
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