Principles of Robot Motion: Theory, Algorithms, and Implementations [Book Review]

By Howie Choset, Kevin M. Lynch, Seth Hutchinson, George Kantor, W. Burgard, Lydia E. Kavraki, Sebastian Thrun. IEEE robotics & automation magazine, 2005

1,327 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/13dcb5efadfc4575abb8ee5ef6f52d565e08de1d

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In Computer Vision and Pattern Recognition

Citations in the corpus

1-Point RANSAC for extended Kalman filtering: Application to real-time structure from motion and visual odometry (2010, methodology): “The collision avoidance is based on the Tangent Bug algorithm with a finite range sensor (Choset et al., 2005).”
Temporal-Logic-Based Reactive Mission and Motion Planning (2009, background): “concentrate on creating control inputs or closed-loop controllers that steer a robot from one configuration to another [1], [2] while taking into account different dynamics and motion constraints.”
Planning human-aware motions using a sampling-based costmap planner (2011, methodology): “Sampling-based path planning methods [ 2 ], [13], are able to handle complex problems in high-dimensional spaces.”
Temporal logic motion planning for dynamic robots (2009, methodology): “For that purpose, we can use many efficient cell decomposition methods for polygonal environments [4,24].”
Cooperative air and ground surveillance (2006, background): “In contrast to much of the literature that addresses the very difficult planning problems for coverage and search (see [ 1 ], for example) and for localization (see [2], for example), our interests are in reactive behaviors that 1) are easily implemented; 2) are independent of the number or the spec”
The Stochastic Motion Roadmap: A Sampling Framework for Planning with Markov Motion Uncertainty (2007, background): “including SLAM and POMDP’s to represent uncertainty in the current state [33, 13].”
Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation (2019, methodology): “Classical planning methods have been successful in solving long-horizon tasks (LaValle, 2006; Choset et al., 2005), but make restrictive assumptions about the state space and reachability between states, limiting their applicability to complex visual manipulation tasks.”
Optimal Multirobot Path Planning on Graphs: Complete Algorithms and Effective Heuristics (2015, background): “nipulation Ding et al. (2001); Grifﬁth and Akella (2005), object transportation Mataric´ et al. (1995); Rus et al. (1995), search and rescue Jennings et al. (1997), and so on. We refer the readers to Choset et al. (2005); Latombe (1991); LaValle (2006) and the references therein for a more comprehen”
Learning Relational Navigation Policies (2006, background): “Whereas such approaches yield highly efficient paths [1], [2], they typically do not take into account solutions to similar problems.”
Sampling-based algorithms for optimal motion planning (2011, background): “The robotic motion planning problem has received a considerable amount of attention, especially over the last decade, as robots started becoming a vital part of modern industry as well as our daily life (Latombe 1991; Choset et al. 2005; LaValle 2006).”
Sampling-Based Path Planning on Configuration-Space Costmaps (2010, background): “SAMPLING-BASED path planning has proven to be an effective framework that is suitable for a large class of problems in domains, such as robotics, manufacturing, computer animation, and computational biology (see [1] and [2] for a survey).”
Fast Approximate kNN Graph Construction for High Dimensional Data via Recursive Lanczos Bisection (2009, background): “, 2000], robot motion planning [Choset et al., 2005], and computer graphics [Sankaranarayanan et al.”
Optimal path planning of mobile robots: A review (2012, background): “path for digital artists in computer graphics and in computational biology to understand probable protein folding paths (Choset et al., 2005).”
Search on the Replay Buffer: Bridging Planning and Reinforcement Learning (2019, background): “k on image-based tasks, but we observed little difference in whether we took the maximum predicted distance or the average predicted distance. 4 Related Work Planning Algorithms: Planning algorithms (Choset et al., 2005; LaValle, 2006) efﬁciently solve longhorizon tasks, including those that stymie ”
Survey of Robot 3D Path Planning Algorithms (2016, methodology): “Choset [9] summarized a lot of his own and others’ works; however he mainly concentrates on 2D environment and therefore algorithms like bioinspired algorithms were paid no attention.”
Why the Monte Carlo method is so important today (2014, background): “Finally, Monte Carlo algorithms are eminently parallelizabe, in particular when various parts can be run independently.”
Watch this: Scalable cost-function learning for path planning in urban environments (2016, background): “I. INTRODUCTION The majority of state-of-the-art motion planning systems for autonomous driving applications rely on manually designed cost-functions [1], with recent successful examples given by the competing teams in the DARPA Grand [2] and Urban Challenges [3], [4].”