Principles of Robot Motion: Theory, Algorithms, and Implementations

By Howie Choset, Jean-Claude Latombe. 2005

688 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/eb0a4062fbd9b35c4b91f2c874c1ac6e741c11b1

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Citations in the corpus

Theta*: Any-Angle Path Planning on Grids (2007, background): “Many methods for discretizing continuous terrain have been investigated in computer science (Choset et al. 2005), all of which attempt to balance the inherent tradeoff between two conflicting criteria, namely the path planning runtime and the length of the resulting path:”
Path Planning and Trajectory Planning Algorithms: A General Overview (2015, unknown): “An overview of many techniques cited in this work can be found also in the classic book [23] or in the recent book [48].”
On the Probabilistic Foundations of Probabilistic Roadmap Planning (2006, background): “Probabilistic roadmap (PRM) planners (Kavraki et al. 1996; Choset et al. 2005) solve seemingly difficult motion planning problems, such as the one shown in Figure 1, where the robot’s configuration spaceC is six-dimensional and the environment is described by tens of thousands of triangles.”
Local Path Planning for Off-Road Autonomous Driving With Avoidance of Static Obstacles (2012, background): “RRT-based planning algorithms for real-time implementations require efficient guiding heuristics for the sampling configuration [9], [13]–[16].”
Process Control: Modeling, Design and Simulation (2003, background): “…protection of cultural heritage structures [1] » harvesting energy from structural vibration [2] » morphing aircraft [3] » lightweight actuators for aerospace applications such as adaptive optics [4] » noise and vibration cancellation [5] » precision positioning for micro-and nanosystems [6].”
Path Planning for Autonomous Underwater Vehicles (2007, background): “The sampling scheme may be probabilistic (see [14] for a survey on probabilistic path planning) or may be deterministic (see [15] for a study of the relationship between the deterministic and the probabilistic approaches).”
Vision-based autonomous mapping and exploration using a quadrotor MAV (2012, methodology): “As a remedy, we implement the Bug algorithm [7] for autonomous wall-following which could be optionally selected as the substitute exploration algorithm in cases where the frontierbased exploration under-performs.”
Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems (2011, unknown): “Our approach is based on a recent methodology under active development in the robotics community known as sampling-based motion planning which includes the rapidly-exploring random tree (RRT) [38] and the probabilistic roadmap (PRM) [9].”