Observability-based Local Path Planning and Collision Avoidance Using Bearing-only Measurements
نویسندگان
چکیده
In this paper we present an observability-based local path planning and collision avoidance technique. Bearing-only measurements are utilized to estimate the time-to-collision (TTC) and bearing to obstacles using an extended Kalman Filter (EKF). To ensure the error covariance matrix computed by an EKF is bounded, the system should be observable. We perform a nonlinear observability analysis to obtain the necessary conditions for complete observability. We use these conditions to design a path planning algorithm that enhances observability while avoiding collisions with obstacles. We analyze the behavior of the path planning algorithm and specially define the environments where the path planning algorithm will guarantee collision-free paths that lead to a goal configuration. Numerical results show that the planning algorithm successfully solves the single and multiple obstacle avoidance Email addresses: [email protected] (Huili Yu), [email protected] (Rajnikant Sharma), [email protected] (Randal W. Beard), [email protected] (Clark N. Taylor) Preprint submitted to Elsevier January 20, 2012 Report Documentation Page Form Approved OMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. 1. REPORT DATE 20 JAN 2012 2. REPORT TYPE 3. DATES COVERED 00-00-2012 to 00-00-2012 4. TITLE AND SUBTITLE Observability-based Local Path Planning and Collision Avoidance Using Bearing-only Measurements 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Brigham Young University,Department of Electrical and Computer Engineering,Provo,UT,84602 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR’S ACRONYM(S) 11. SPONSOR/MONITOR’S REPORT NUMBER(S) 12. DISTRIBUTION/AVAILABILITY STATEMENT Approved for public release; distribution unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT In this paper we present an observability-based local path planning and collision avoidance technique. Bearing-only measurements are utilized to estimate the time-to-collision (TTC) and bearing to obstacles using an extended Kalman Filter (EKF). To ensure the error covariance matrix computed by an EKF is bounded, the system should be observable. We perform a nonlinear observability analysis to obtain the necessary conditions for complete observability. We use these conditions to design a path planning algorithm that enhances observability while avoiding collisions with obstacles. We analyze the behavior of the path planning algorithm and specially define the environments where the path planning algorithm will guarantee collision-free paths that lead to a goal configuration. Numerical results show that the planning algorithm successfully solves the single and multiple obstacle avoidance problems while improving the estimation accuracy. 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT Same as Report (SAR) 18. NUMBER OF PAGES 33 19a. NAME OF RESPONSIBLE PERSON a. REPORT unclassified b. ABSTRACT unclassified c. THIS PAGE unclassified Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 problems while improving the estimation accuracy.
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