Research On Full Coverage Optimization Algorithm Of Sweeping Robot In Closed Unknown Environment

With the arrival of the smart home era,the development of sweeping robot has attracted people's attention.Since the sweeping robot often works in a closed and unknown complex environment,and the movement of indoor tables and chairs may cause changes in the environment.It is difficult to automatically achieve the full coverage of the environmental area,which restricts the development of the sweeping robot.For this reason,the full coverage path planning technology of sweeping robot in the closed unknown environment is deeply researched in this thesis.The environmental modeling problem of the sweeping robot is firstly analyzed in this thesis.By comparing the advantages and disadvantages of several common environmental modeling methods,the grid method which is more suitable for the research content of this thesis is selected for environmental modeling.Then,the grid method is improved for the characteristics of the working environment in this thesis.Due to the complexity of the environment,the strategy of sub-regional coverage is adopted in this thesis.For this reason,a new local area coverage algorithm based on Starting Point Direction First(SPDF)is proposed to achieve coverage in local sub-region.Then,the SPDF algorithm and BCD algorithm are simulated and compared,the experimental results show that the SPDF algorithm has higher feasibility and significant advantages.After the sweeping robot completes the coverage of the current local sub-region,it needs to switch to the uncovered sub-regions to continue working.For this reason,a backtracking mechanism for recording uncovered sub-regions and selecting target points for the regional cohesion path is firstly established.Then,the regional cohesion path planning algorithm is designed.Since there may be unknown parts in the working environment of this algorithm,theta* algorithm is firstly improved and used in global path planning.When the local unknown environment is encountered,the rolling window algorithm is used to plan the local path.Finally,the Bezier curve is used to fit and optimize the planned path.In order to verify the performance of the algorithm in this thesis,the simulation experiment is compared with the D* algorithm,and the results show that the algorithm in this thesis has the advantages of short and smooth planning path.Based on the above design,the full coverage path planning algorithm in this thesis is designed and optimized.Firstly,the whole process of the full coverage path planning algorithm is developed.Then,the optimization strategy of the algorithm in this thesis is designed,including the global optimization strategy for the overall optimization of the full coverage path and the local optimization strategy that can make real-time adjustments when the obstacles are moved.Finally,the algorithm in this thesis is simulated and compared with the BD* algorithm.The experimental results show that the algorithm in this thesis can be well applied to the closed unknown complex environment,with fewer local sub-regions,short and smooth paths,good adaptability and real-time optimization characteristics.Finally,the main research content of this thesis is summarized,and the future direction of the algorithm in this thesis is prospected.