[7] KUFFNER J JLAVALLE S M.RRTconnect:an efficient approach to singlequery path planning［C］//Proceedings 2000 ICRA.Millennium Conference.IEEE International Conference on Robotics and Automation.Symposia Proceedings (Cat.No.00CH37065).San Francisco:IEEE 2000.doi:10.1109/ROBOT.2000.844730.

[8] LAVALLE S MJR KUFFNER J J.Randomized kinodynamic planning［J］.The International Journal of Robotics Research200120(5):378400.

[9] KAVRAKI L ESVESTKA PLATOMBE J Cet al.Probabilistic roadmaps for path planning in highdimensional configuration spaces［J］.IEEE Transactions on Robotics and Automation199612(4):566580.

[10] RATLIFF NZUCKER MBAGNELL J Aet al.CHOMP:gradient optimization techniques for efficient motion planning［C］//2009 IEEE International Conference on Robotics and Automation.Kobe:IEEE2009:489494.

[11] HE K MMARTIN EZUCKER M.Multigrid CHOMP with local smoothing［C］//2013 13th IEEERAS International Conference on Humanoid Robots (Humanoids).Atlanta:IEEE2013:315322.

[12] ZUCKER MRATLIFF NDRAGAN A Det al.CHOMP:covariant hamiltonian optimization for motion planning［J］.The International Journal of Robotics Research201332(910):11641193.

[13] KALAKRISHNAN MCHITTA STHEODOROU Eet al.STOMP:stochastic trajectory optimization for motion planning［C］//2011 IEEE International Conference on Robotics and Automation.Shanghai:IEEE2011:45694574.

[14] SCHULMAN JHO JLEE Aet al.Finding locally optimalcollisionfree trajectories with sequential convex optimization［C］//Robotics:Science and Systems(RSS).Berlin:RSS2013.doi:10.15607/RSS.2013.IX.031.

[15] SCHULMAN JDUAN YHO Jet al.Motion planning with sequential convex optimization and convex collision checking［J］.The International Journal of Robotics Research2014 33(9):12511270.

[16] MUKADAM MYAN X YBOOTS B.Gaussian process motion planning［C］//2016 IEEE International Conference on Robotics and Automation (ICRA).Stockholm:IEEE2016:915.

[17] DONG JMUKADAM MDELLAERT Fet al.Motion planning as probabilistic inference using Gaussian processes and factor graphs［C］//Robotics:Science and Systems(RSS).Ann Arbor:RSS2016.doi:10.15607/RSS.2016.XII.001.

[18] TOUSSAINT M.Robot trajectory optimization using approximate inference［C］//Proceedings of the 26th Annual International Conference on Machine Learning.Montreal:ICML2009:10491056.

[19] TOUSSAINT MGOERICK C.A Bayesian view on motor control and planning［M］//SIGAUD OPETERS J.From motor learning to interaction learning in robots.Berlin:Springer Heidelberg2010:227252.

[20] ZHANG J LZHANG X S.A SQP method for inequality constrained optimization［J］.Acta Mathematicae Applicatae Sinica200218(1):7784.

[22] POWELL M J D.The convergence of variable metric methods for nonlinearly constrained optimization calculations［M］//MANGASARIAN O LMEYER R RROBINSON S M.Nonlinear programming 3.Pittsburgh:Academic Press 1978:2763.

[23] POWELL M J D.Variable metric methods for constrained optimization［M］//BACHEM AKORTE BGRTSCHEL M.Mathematical programming the state of the art.Berlin:Springer Heidelberg1983.

[24] WILSON D B.Generating random spanning trees more quickly than the cover time［C］//Proceedings of the 28th Annual ACM Symposium on Theory of Computing.Philadelphia:STOC1996:296303.

[25] DONG JBOOTS BDELLAERT F.Sparse Gaussian processes for continuoustime trajectory estimation on matrix lie groups［R］.Los Alamos:arXiv Preprint 2017:arXiv:1705.06020.

[26] DELLAERT F.Factor graphs and GTSAM:a handson introduction［R］.Atlanta:Georgia Institute of Technology2012.