无人车运动规划的52篇文章
本来想凑出一百篇的，后来发现有些工作虽然写得也比较完备，但是开创性并不大，只是一个良好的应用型文章，后来就只列出了这51篇。当然，为了搞懂这些文章，阅读一些阐述详细的应用型文章是少不了的。
[1] E. W. Dijkstra, 「A note on two problems in connexion with graphs,」 Numerische Mathematik, vol. 1, no. 1, pp. 269–271, Dec. 1959.
远古大神E.W. Dijkstra开发的Dijkstra演算法，在拓扑图，栅格图，lattice图中搜索都有不错的效果。
[2] P. E. Hart, N. J. Nilsson, and B. Raphael, 「A Formal Basis for the Heuristic Determination of Minimum Cost Paths,」 IEEE Transactions on Systems Science and Cybernetics, vol. 4, no. 2, pp. 100–107, Jul. 1968.
A* 演算法，在Dijkstra的代价函数里增加了启发函数，速度快，保证最优，但很多时候因为启发值的问题并不能适用很多问题。
[3] Bellman R. Dynamic programming[J]. Science, 1966, 153(3731): 34-37.
Bellman的动态规划，在拓扑图和最优控制中应用很广。
[4] A. Stentz, 「Optimal and efficient path planning for partially-known environments,」 in Robotics and Automation, 1994. Proceedings., 1994 IEEE International Conference on, 1994, pp. 3310–3317.
[5] A. Stentz, 「The Focussed D* Algorithm for Real-Time Replanning,」 p. 8.
D*演算法。
[6] D. Ferguson and A. Stentz, 「Using interpolation to improve path planning: The Field D* algorithm,」 J. Field Robotics, vol. 23, no. 2, pp. 79–101, Feb. 2006.
Field D*演算法。
[7] S. Koenig, M. Likhachev, and D. Furcy, 「Lifelong Planning A?,」 Artificial Intelligence, vol. 155, no. 1–2, pp. 93–146, May 2004.
LPA*演算法。
[8] S. Koenig and M. Likhachev, 「Improved fast replanning for robot navigation in unknown terrain,」 in Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), 2002, vol. 1, pp. 968–975 vol.1.
[9] S. Koenig and M. Likhachev, 「Fast replanning for navigation in unknown terrain,」 IEEE Trans. Robot., vol. 21, no. 3, pp. 354–363, Jun. 2005.
D* Lite演算法。
[10] A. Kelly and B. Nagy, 「Reactive Nonholonomic Trajectory Generation via Parametric Optimal Control,」 The International Journal of Robotics Research, vol. 22, no. 7–8, pp. 583–601, Jul. 2003.
这篇文章非常经典，用spiral连接两个状态。下面是我写的一些想法。
小邬：模型预测轨迹生成与lattice
[11] T. M. Howard and A. Kelly, 「Optimal Rough Terrain Trajectory Generation for Wheeled Mobile Robots,」 The International Journal of Robotics Research, vol. 26, no. 2, pp. 141–166, Feb. 2007.
[12] T. M. Howard, C. J. Green, A. Kelly, and D. Ferguson, 「State space sampling of feasible motions for high-performance mobile robot navigation in complex environments,」 Journal of Field Robotics, vol. 25, no. 6–7, pp. 325–345, Jun. 2008.
这两篇文章叫做模型预测轨迹生成，也是受到spiral的启发。
[13] D. Dolgov, S. Thrun, M. Montemerlo, and J. Diebel, 「Practical Search Techniques in Path Planning for Autonomous Driving,」 p. 6.
[14] D. Dolgov, S. Thrun, M. Montemerlo, and J. Diebel, 「Path Planning for Autonomous Vehicles in Unknown Semi-structured Environments,」 The International Journal of Robotics Research, vol. 29, no. 5, pp. 485–501, Apr. 2010.
[15] M. Montemerlo et al., 「Junior: The Stanford entry in the Urban Challenge,」 Journal of Field Robotics, vol. 25, no. 9, pp. 569–597, Sep. 2008.
斯坦福在DARPPA挑战赛期间开发的演算法。Hybrid A*及其优化。
[16] C. Urmson et al., 「Autonomous driving in urban environments: Boss and the Urban Challenge,」 Journal of Field Robotics, vol. 25, no. 8, pp. 425–466, Aug. 2008.
[17] D. Ferguson, T. M. Howard, and M. Likhachev, 「Motion planning in urban environments,」 Journal of Field Robotics, vol. 25, no. 11–12, pp. 939–960, Nov. 2008.
[18] J. Leonard et al., 「A perception-driven autonomous urban vehicle,」 Journal of Field Robotics, vol. 25, no. 10, pp. 727–774, Oct. 2008.
MIT 在DARPPA期间用的演算法。 RRT。
[19] D. Fox, W. Burgard, and S. Thrun, 「The dynamic window approach to collision avoidance,」 IEEE Robotics & Automation Magazine, vol. 4, no. 1, pp. 23–33, Mar. 1997.
动态窗口法，是控制空间采样比较代表性的演算法，也有许多人分为反应式运动规划演算法一类。
[20] L. E. Kavraki, P. Svestka, J.-C. Latombe, and M. H. Overmars, 「Probabilistic roadmaps for path planning in high-dimensional configuration spaces,」 IEEE Transactions on Robotics and Automation, vol. 12, no. 4, pp. 566–580, Aug. 1996.
状态空间随机采样PRM演算法。
[21] LaValle S M. Rapidly-exploring random trees: A new tool for path planning[J]. 1998.
状态空间随机采样RRT演算法。
[22] Y. Kuwata, J. Teo, G. Fiore, S. Karaman, E. Frazzoli, and J. P. How, 「Real-Time Motion Planning With Applications to Autonomous Urban Driving,」 IEEE Transactions on Control Systems Technology, vol. 17, no. 5, pp. 1105–1118, Sep. 2009.
[23] S. Karaman and E. Frazzoli, 「Optimal kinodynamic motion planning using incremental sampling-based methods,」 in 49th IEEE Conference on Decision and Control (CDC), Atlanta, GA, USA, 2010, pp. 7681–7687.
状态空间随机采样RRT*演算法。
[24] O. Khatib, 「Real-time obstacle avoidance for manipulators and mobile robots,」 in Robotics and Automation. Proceedings. 1985 IEEE International Conference on, 1985, vol. 2, pp. 500–505.
人工势场法。
CMU
卡耐基梅隆大学的人才源源不断，规划领域的许多常见演算法都来自CMU，可见CMU的机器人学有多强悍。一些博士论文更是经典。
[25] M. McNaughton, C. Urmson, J. M. Dolan, and J.-W. Lee, 「Motion planning for autonomous driving with a conformal spatiotemporal lattice,」 in 2011 IEEE International Conference on Robotics and Automation, Shanghai, China, 2011, pp. 4889–4895.
[26] W. Xu, J. Wei, J. M. Dolan, H. Zhao, and H. Zha, 「A real-time motion planner with trajectory optimization for autonomous vehicles,」 in 2012 IEEE International Conference on Robotics and Automation, 2012, pp. 2061–2067.
降维，采样，优化
[27] T. Gu, J. Snider, J. M. Dolan, and J. Lee, 「Focused Trajectory Planning for autonomous on-road driving,」 in 2013 IEEE Intelligent Vehicles Symposium (IV), Gold Coast City, Australia, 2013, pp. 547–552.
降维，搜索，再次搜索
德国KIT开发的演算法
[28] S. Kammel et al., 「Team AnnieWAY』s autonomous system for the 2007 DARPA Urban Challenge,」 J. Field Robotics, vol. 25, no. 9, pp. 615–639, Sep. 2008.
[29] J. Ziegler and C. Stiller, 「Spatiotemporal state lattices for fast trajectory planning in dynamic on-road driving scenarios,」 in 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, MO, USA, 2009, pp. 1879–1884.
[30] J. Ziegler and C. Stiller, 「Fast collision checking for intelligent vehicle motion planning,」 in 2010 IEEE Intelligent Vehicles Symposium, 2010, pp. 518–522.
[31] M. Werling, J. Ziegler, S. Kammel, and S. Thrun, 「Optimal trajectory generation for dynamic street scenarios in a Frenét Frame,」 in 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, 2010, pp. 987–993.
这一篇经典，一个三维规划问题变成两个二维采样问题。
[32] M. Werling, S. Kammel, J. Ziegler, and L. Gr?ll, 「Optimal trajectories for time-critical street scenarios using discretized terminal manifolds,」 The International Journal of Robotics Research, vol. 31, no. 3, pp. 346–359, Mar. 2012.
[33] J. Ziegler, P. Bender, T. Dang, and C. Stiller, 「Trajectory planning for Bertha — A local, continuous method,」 in 2014 IEEE Intelligent Vehicles Symposium Proceedings, 2014, pp. 450–457.
这一篇非常经典，我们怎么处理非凸障碍物，作者引入了行驶通道和lanelet，然后时间序列化，然后SQP优化[x,y].这种处理激发了很多后来的工作。
[34] J. Ziegler et al., 「Making Bertha Drive—An Autonomous Journey on a Historic Route,」 IEEE Intelligent Transportation Systems Magazine, vol. 6, no. 2, pp. 8–20, Summer 2014.
[35] M. Kelly, 「An Introduction to Trajectory Optimization: How to Do Your Own Direct Collocation,」 SIAM Rev., vol. 59, no. 4, pp. 849–904, Jan. 2017.
[36] N. Ratliff, M. Zucker, J. A. Bagnell, and S. Srinivasa, 「CHOMP: Gradient optimization techniques for efficient motion planning,」 in 2009 IEEE International Conference on Robotics and Automation, 2009, pp. 489–494.
[37] M. Zucker et al., 「CHOMP: Covariant Hamiltonian optimization for motion planning,」 The International Journal of Robotics Research, vol. 32, no. 9–10, pp. 1164–1193, Aug. 2013.
[38] P. Bender, J. Ziegler, and C. Stiller, 「Lanelets: Efficient map representation for autonomous driving,」 in 2014 IEEE Intelligent Vehicles Symposium Proceedings, 2014, pp. 420–425.
韩国汉阳大学的运动规划
[39] K. Chu, M. Lee, and M. Sunwoo, 「Local Path Planning for Off-Road Autonomous Driving With Avoidance of Static Obstacles,」 IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 4, pp. 1599–1616, Dec. 2012.
[40] J. Kim, K. Jo, W. Lim, M. Lee, and M. Sunwoo, 「Curvilinear-Coordinate-Based Object and Situation Assessment for Highly Automated Vehicles,」 IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 3, pp. 1559–1575, Jun. 2015.
这两篇比较有新意的是怎么处理参考线问题，写得很详细。怎么分段，怎么弧长参数化，可以参考[49][50][51], 也可以去OPENDRIVE官网去看看他们怎么处理参考线问题的。
[41] J. Kim and D. Kum, 「Collision Risk Assessment Algorithm via Lane-Based Probabilistic Motion Prediction of Surrounding Vehicles,」 IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 9, pp. 2965–2976, Sep. 2018.
[42] W. Lim, S. Lee, M. Sunwoo, and K. Jo, 「Hierarchical Trajectory Planning of an Autonomous Car Based on the Integration of a Sampling and an Optimization Method,」 IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 2, pp. 613–626, Feb. 2018.
这一篇也是解耦，先搜索再优化。
伯克利的运动规划
[43] C. Liu, C. Lin, Y. Wang, and M. Tomizuka, 「Convex feasible set algorithm for constrained trajectory smoothing,」 in 2017 American Control Conference (ACC), 2017, pp. 4177–4182.
[44] C. Liu, W. Zhan, and M. Tomizuka, 「Speed profile planning in dynamic environments via temporal optimization,」 in 2017 IEEE Intelligent Vehicles Symposium (IV), 2017, pp. 154–159.
[45] C. Liu, C.-Y. Lin, and M. Tomizuka, 「The Convex Feasible Set Algorithm for Real Time Optimization in Motion Planning,」 SIAM Journal on Control and Optimization, vol. 56, no. 4, pp. 2712–2733, Jan. 2018.
对于一个三维规划问题，建模为一个小的凸可行域优化问题。
[46] J. Chen, C. Liu, and M. Tomizuka, 「FOAD: Fast Optimization-based Autonomous Driving Motion Planner,」 in 2018 Annual American Control Conference (ACC), 2018, pp. 4725–4732.
[47] J. Chen, W. Zhan, and M. Tomizuka, 「Autonomous Driving Motion Planning with Constrained Iterative LQR,」 IEEE Transactions on Intelligent Vehicles, pp. 1–1, 2019.
百度Apollo
[48] Fan H. et al., 「Baidu Apollo EM Motion Planner,」 Jul. 2018.
这一篇将一个三维问题解耦为两个二维问题，然后分别做搜索和优化。和xuwenda2012年的这一篇工作思路较像。
FRENET frame
[49] Wang H, Kearney J, Atkinson K. Robust and efficient computation of the closest point on a spline curve[C]//Proceedings of the 5th International Conference on Curves and Surfaces. 2002: 397-406.
[50] Wang H, Kearney J, Atkinson K. Arc-length parameterized spline curves for real-time simulation[C]//Proc. 5th International Conference on Curves and Surfaces. 2002, 387396.
[51] Hu X, Chen L, Tang B, et al. Dynamic path planning for autonomous driving on various roads with avoidance of static and moving obstacles[J]. Mechanical Systems and Signal Processing, 2018, 100: 482-500.
增加
[52] Likhachev M, Ferguson D. Planning long dynamically feasible maneuvers for autonomous vehicles[J]. The International Journal of Robotics Research, 2009, 28(8): 933-945.
小邬：学习运动规划该看什么书
小邬：运动规划综述文章
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