Research On Autonomous Navigation Method For Indoor Robots Based On Multisensor Fusion

Autonomous navigation is the core issue of indoor robot's self-determination behavior planning.Because there are so many problems in complex environment,the method which based on multi-sensor fusion has become a new trend.On the background of engineering application,and with the hope of introducing the deep learning into the field of artificial intelligence,we carried out some research on the using of related robot technologies in autonomous navigation.We developed an integrated robot system framework,including mechanical platform,embedded hardware,software,SLAM algorithms and so on.Combining with multi-sensor fusion environment information,we not only provided a solution to the common problems in the navigation tasks,but also promoted the development of semantic mapping.The main contents of this paper as follows:First,we developed an integrated framework of robot system,including a mechanical platform,an embedded hardware architecture and a robot software system.The mechanical platform consists of active perception,voice input,motion control and other modules.The embedded hardware architecture includes some hardware elements,such as 10 drive,mathematical operation library,kinematics library and dynamic model.The robot software system refers to the bootbot system,provided mobile terminal,intermediate connection,mission algorithms and so on.This integrated framework we designed can meet the conditions of autonomous navigation.Second,we investigated a SLAM algorithm based on multi-sensors for ROS environment.Firstly,we created the grid map based on Gmapping method,by mixing the data from ultrasound,RGBD camera and laser.Then,we used both the adaptive monte carlo location algorithm and A*algorithm for global exploration.Furthermore,the dynamic window method was applied to avoid local obstacles.The experiment results showed that the map had a resolution of 2 cm and can guide the robot for indoor navigation.Third,we proposed a real-time correction algorithm which fused the information of IMU.We used the Extend Kalman filtering and complementary filtering to acquire the position of robot,and by constantly comparing odometer displacement and quadratic integral accelerate data to prevent the movement and suspension of robot.The results indicated that the fusion data was stable and the accuracy was high for calibrating the odometer loss.Fourth,we proposed a kind of directional A*algorithm.Firstly,the "line of sight" solutions was used to smooth the path for getting rid of the zigzag effect and collisions.Secondly,the "arc-line-arc" turning methods was applied to avoid the width of the robot in path-finding.At last,some basic optimizations based on the binary heap were carried out to speed up the directional A*algorithm.Simulation and comparison results between the improved A*algorithm and traditional one showed that the method we proposed was more efficient.At the same time,the accelerating algorithm based on the binary heap made the path-finding 4-7 times faster.Moreover,a path planning and tracking test was carried out in lab environment,the results verified that the tracking precision can keep in a small range and the robot can run without collision when the navigation path was given by the proposed algorithm.Finally,we proposed the semantic mapping algorithm on a robot without environment-specific training.Firstly,the two-dimensional grid map was constructed to plan the recognition path of scene classes with depth information.Secondly,a state-of-the-art convolutional network was applied to recognition semantic classes without environment-specific training in real-time.At last,a Bayesian estimation framework,which incorporating prior domain knowledge,were carried out to smooth out spurious results in semantic map.Simulation and experiment results,which was used to evaluate the classification system on a robot in different places of our center,showed that the method we proposed was efficient.At the same time,the probability model based on Bayesian framework can fix error classification.Moreover,a path planning and navigation test was carried out in lab environment.The results verified that the robot can modulate its behavior with semantic information.