Example of a workspace. Configuration space of a point-sized robot. Configuration space for a rectangular translating robot pictured red. Example of a valid path.
Kim, and Dinesh Manocha We introduce a novel proximity query, connection collision query CCQand use it for efficient and exact local planning in sampling-based motion planners. Given two collision-free configurations, CCQ checks whether these configurations can be connected by a given continuous path that either lies completely in the free space or penetrates any obstacle by at most a given threshold.
Our approach is general, robust, and can handle different continuous path formulations. We have integrated the CCQ algorithm with sampling-based motion planners and can perform reliable local planning queries with little performance degradation, as compared to prior methods.
Moreover, the CCQ-based exact local planner is about an order of magnitude faster than prior exact local planning algorithms. Our approach uses thread and data parallelism to achieve high performance for all components of sample-based algorithms, including random sampling, nearest neighbor computation, local planning, collision queries, and graph search.
The overall approach can efficiently solve both the multi-query and single-query versions of the problem and obtain considerable speedups over prior CPU-based algorithms.
This is the first algorithm that can perform real-time motion planning and global navigation using commodity hardware. Lin, and Dinesh Manocha We present a goal-directed motion synthesis technique that integrates sample-based planning methods with constraint-based dynamics simulation using a finite element formulation to generate collision-free paths for a deformable model.
Our method allows the user to quickly specify various constraints, including a desired trajectory as a sparse sequence of waypoints, and automatically computes a physically plausible path that satisfies geometric and physical constraints.
Kim, and Dinesh Manocha We present an approach to computing a collision-free path for part disassembly simulation and virtual prototyping of part removal. Our algorithm is based on sample-based motion planning that connects collision-free samples in the configuration space using local planning.
We describe techniques to generate samples in narrow passages and efficient local planning algorithms to connect them with collision-free paths. Retraction-based RRT Planner Liangjun Zhang and Dinesh Manocha We present a optimization-based retraction algorithm to improve the performance of sample-based planners in narrow passages for three-dimensional rigid robots.
The retraction step is formulated as an optimization problem using a distance metric in the configuration space.
We use local contact analysis to compute samples near the boundary of C-obstacle and use those samples to improve the performance of rapidly-exploring random tree RRT planners in narrow passages. Kim, Shankar Krishnan, and Dinesh Manocha We present efficient and practical algorithms for complete motion planning.
A complete motion planner either computes a collision-free path from the initial configuration to the final configuration or concludes that no such path exists. PMP relies upon motion equations and robot control in order to find a path.
The resulting path obeys kinematic and dynamics constraints while also finding a goal; something that configuration-space randomized planners cannot accomplish.
Furthermore, motion equations need not be restricted to the robot. The paths or roadmaps themselves can also be controlled in a physically plausible manner to ensure a collision-free path in a dynamic environment.
Lin We present a local planning method in contact-space which uses continuous collision detection CCD. We use the precise contact information provided by a CCD algorithm to enhance a randomized planner by efficiently sampling the contact-space, as well as by constraining the sampling when the roadmap is expanded.
Lin, and Dinesh Manocha We present a algorithm for motion planning of a deformable robot in a static environment. Our algorithm computes an approximate path using the probabilistic roadmap method PRM. We use constraint-based planning to simulate robot deformation and make appropriate path adjustments and corrections to compute a collision-free path.
Our PRM algorithm takes into account geometric constraints, like non-penetration, and physical constraints, like volume preservation. Lin, and Dinesh Manocha We present an algorithm for path planning for a flexible robot in complex environments. Our algorithm computes a collision-free path by taking into account geometric and physical constraints, including obstacle avoidance, non-penetration, volume preservation, surface tension, and energy minimization.
Lin, and Dinesh Manocha We have developed a system for navigation of massive environments using path planning.
Our system provides the user with a local driving mode that keeps an avatar constrained to walkable surfaces in the model and a global path planner. Arbitrary configurations are specified by the user and a preprocessed graph is searched for a path between the configurations.A fundamental aspect of autonomous vehicle guidance is planning trajectories.
Historically, two fields have contributed to trajectory or motion planning methods: robotics and dynamics and control. The former typically have a stronger focus on computational issues and real-time robot control, while.
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Motion Planning. Motion planning that is also known as the piano movers problem or the navigation problem is a robotics term that describes the process by which a movement task is broken into discrete motions.
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Make something really special with the range of designer paper available at Officeworks. Buy now using our fast and easy ordering service. Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.