An active perception methodology is proposed to locally predict the observability condition in a reasonable horizon and suggest an observability-constrained motion direction for the next step to ensure an accurate and...An active perception methodology is proposed to locally predict the observability condition in a reasonable horizon and suggest an observability-constrained motion direction for the next step to ensure an accurate and consistent state estimation performance of vision-based navigation systems. The methodology leverages an efficient EOG-based observability analysis and a motion primitive-based path sampling technique to realize the local observability prediction with a real-time performance. The observability conditions of potential motion trajectories are evaluated,and an informed motion direction is selected to ensure the observability efficiency for the state estimation system. The proposed approach is specialized to a representative optimizationbased monocular vision-based state estimation formulation and demonstrated through simulation and experiments to evaluate the ability of estimation degradation prediction and efficacy of motion direction suggestion.展开更多
Miniature jumping robots(MJRs)have difficulty executing autonomous movements in unstructured environments with obstacles because of their limited perception and computing resources.This study investigates the obstacle...Miniature jumping robots(MJRs)have difficulty executing autonomous movements in unstructured environments with obstacles because of their limited perception and computing resources.This study investigates the obstacle detection and autonomous stair climbing methods for MJRs.We propose an obstacle detection method based on a combination of attitude and distance detections,as well as MJRs’motion.A MEMS inertial sensor collects the yaw angle of the robot,and a ranging sensor senses the distance between the robot and the obstacle to estimate the size of the obstacle.We also propose an autonomous stair climbing algorithm based on the obstacle detection method.The robot can detect the height and width of stairs and its position relative to the stairs and then repeatedly jump to climb them step by step.Moreover,the height,width,and position are sent to a control terminal through a wireless sensor network to update the information regarding the MJR and stairs in a control interface.Furthermore,we conduct stair detection,modeling,and stair climbing experiments on the MJR and obtain acceptable precisions for autonomous obstacle negotiation.Thus,the proposed obstacle detection and stair climbing methods can enhance the locomotion capability of the MJR in environmental monitoring,search and rescue,etc.展开更多
文摘An active perception methodology is proposed to locally predict the observability condition in a reasonable horizon and suggest an observability-constrained motion direction for the next step to ensure an accurate and consistent state estimation performance of vision-based navigation systems. The methodology leverages an efficient EOG-based observability analysis and a motion primitive-based path sampling technique to realize the local observability prediction with a real-time performance. The observability conditions of potential motion trajectories are evaluated,and an informed motion direction is selected to ensure the observability efficiency for the state estimation system. The proposed approach is specialized to a representative optimizationbased monocular vision-based state estimation formulation and demonstrated through simulation and experiments to evaluate the ability of estimation degradation prediction and efficacy of motion direction suggestion.
基金supported in part by the National Natural Science Foundation of China(61873066 and 62173090)the Zhi Shan Scholars Program of Southeast University,China(2242020R40096).
文摘Miniature jumping robots(MJRs)have difficulty executing autonomous movements in unstructured environments with obstacles because of their limited perception and computing resources.This study investigates the obstacle detection and autonomous stair climbing methods for MJRs.We propose an obstacle detection method based on a combination of attitude and distance detections,as well as MJRs’motion.A MEMS inertial sensor collects the yaw angle of the robot,and a ranging sensor senses the distance between the robot and the obstacle to estimate the size of the obstacle.We also propose an autonomous stair climbing algorithm based on the obstacle detection method.The robot can detect the height and width of stairs and its position relative to the stairs and then repeatedly jump to climb them step by step.Moreover,the height,width,and position are sent to a control terminal through a wireless sensor network to update the information regarding the MJR and stairs in a control interface.Furthermore,we conduct stair detection,modeling,and stair climbing experiments on the MJR and obtain acceptable precisions for autonomous obstacle negotiation.Thus,the proposed obstacle detection and stair climbing methods can enhance the locomotion capability of the MJR in environmental monitoring,search and rescue,etc.
