A novel energy-efficient optimization A^(*)algorithm is proposed to address the performance requirements of energy consumption reduction and global optimality in the context of mobile navigation robots for the visuall...A novel energy-efficient optimization A^(*)algorithm is proposed to address the performance requirements of energy consumption reduction and global optimality in the context of mobile navigation robots for the visually impaired.The energy cost function of mobile navigation robots is incorporated into a heuristic algorithm based on the A^(*)algorithm to plan energy-optimal paths quickly.The optimized A^(*)algorithm was compared with the original A^(*)algorithm and the Q-learning algorithm through simulation experiments on a grid map using the MATLAB platform.The experimental results show that compared with the standard A^(*)algorithm,the optimized A^(*)algorithm reduces the path-planning time by 37.2%and the energy consumption by 29.5%.In addition,compared with the Q-learning algorithm,this algorithm’s overall search efficiency is improved by 76.8%.Simulation results show that the optimized A^(*)algorithm is more efficient in searching and has a shorter seek time,fully saving energy.展开更多
文摘A novel energy-efficient optimization A^(*)algorithm is proposed to address the performance requirements of energy consumption reduction and global optimality in the context of mobile navigation robots for the visually impaired.The energy cost function of mobile navigation robots is incorporated into a heuristic algorithm based on the A^(*)algorithm to plan energy-optimal paths quickly.The optimized A^(*)algorithm was compared with the original A^(*)algorithm and the Q-learning algorithm through simulation experiments on a grid map using the MATLAB platform.The experimental results show that compared with the standard A^(*)algorithm,the optimized A^(*)algorithm reduces the path-planning time by 37.2%and the energy consumption by 29.5%.In addition,compared with the Q-learning algorithm,this algorithm’s overall search efficiency is improved by 76.8%.Simulation results show that the optimized A^(*)algorithm is more efficient in searching and has a shorter seek time,fully saving energy.
