Roadmap Graph Research Articles

Topology observing 3D device reconstruction from continuous-sweep limited angle fluoroscopy.

Minimally invasive procedures usually require navigating a microcatheter and guidewire through endoluminal structures such as blood vessels and airways to sites of the disease. For numerous clinical applications, two-dimensional (2D) fluoroscopy is the primary modality used for real-time image guidance during navigation. However, 2D imaging can pose challenges for navigation in complex structures. Real-time 3D visualization of devices within the anatomic context could provide considerable benefits for these procedures. Continuous-sweep limited angle (CLA) fluoroscopy has recently been proposed to provide a compromise between conventional rotational 3D acquisitions and real-timefluoroscopy. The purpose of this work was to develop and evaluate a noniterative 3D device reconstruction approach for CLA fluoroscopy acquisitions, which takes into account endoluminal topology to avoid impossible paths between disconnectedbranches. The algorithm relies on a static 3D roadmap (RM) of vessels or airways, which may be generated from conventional cone beam CT (CBCT) acquisitions prior to navigation. The RM is converted to a graph representation describing its topology. During catheter navigation, the device is segmented from the live 2D projection images using a deep learning approach from which the centerlines are extracted. Rays from the focal spot to detector pixels representing 2D device points are identified and intersections with the RM are computed. Based on the RM graph, a subset of line segments is selected as candidates to exclude device paths through disconnected branches of the RM. Depth localization for each point along the device is then performed by finding the point closest to the previous 3D reconstruction along the candidate segments. This process is repeated as the projection angle changes for each CLA image frame. The approach was evaluated in a phantom study in which a catheter and guidewire were navigated along five pathways within a complex vessel phantom. The result was compared to static cCBCT acquisitions of the device in the finalposition. The average root mean squared 3D distance between CLA reconstruction and reference centerline was mm. The Euclidean distance at the device tip was mm. The correct pathway was identified during reconstruction in of frames ( ). The percentage of 3D device points reconstructed inside the 3D roadmap was with an average distance of mm between the device points outside the roadmap and the nearest point within theroadmap. This study demonstrates the feasibility of reconstructing curvilinear devices such as catheters and guidewires during endoluminal procedures including intravascular and transbronchial interventions using a noniterative reconstruction approach for CLA fluoroscopy. This approach could improve device navigation in cases where the structure of vessels or airways is complex and includes overlappingbranches.

Vehicle specific robust traversability indices using roadmaps on 3D pointclouds

Roads undergo rapid deterioration due to various economic, social and natural reasons. The uncertainty and unavailability of road safety information has become a major issue in reliably transporting goods, movement of heavy machinery, transport of people and materials to/from disaster areas and in even giving plain guarantees for safe traversability of a road that is good on paper. In this paper, we propose a novel approach towards reducing this uncertainty. Instead of asking qualitative questions about whether a road network is generally traversable for a general class of vehicles, we propose a solution that gives precise quantitative answers as a distance modulated roadmap and confirms whether a particular segment of the road is traversable for a particular type of vehicle. In this paper, we extend on our previous work Khan et al. (2016) of traversability analysis by empirically proving that our framework is robust to shape and size of the roadmap graph. We show that obstacle size, count, and location can have varying effects on traversability of different vehicles. We also propose a new Road Safety Index (RSI) which is an extension of our previously proposed index, Road Traversability Index (RTI). RSI takes into account multiple disjoint narrow passages that belong to the same connected component of the graph. Lastly, we present application scenarios where our roadmap based traversability can be used to assess the safety of the road for a particular vehicle.

Maximizing the Coverage of Roadmap Graph for Optimal Motion Planning

Automated motion-planning technologies for industrial robots are critical for their application to Industry 4.0. Various sampling-based methods have been studied to generate the collision-free motion of articulated industrial robots. Such sampling-based methods provide efficient solutions to complex planning problems, but their limitations hinder the attainment of optimal results. This paper considers a method to obtain the optimal results in the roadmap algorithm that is representative of the sampling-based method. We define the coverage of a graph as a performance index of its optimality as constructed by a sampling-based algorithm and propose an optimization algorithm that can maximize graph coverage in the configuration space. The proposed method was applied to the model of an industrial robot, and the results of the simulation confirm that the roadmap graph obtained by the proposed algorithm can generate results of satisfactory quality in path-finding tests under various conditions.

Lazy Receding Horizon A* for Efficient Path Planning in Graphs with Expensive-to-Evaluate Edges

Motion-planning problems, such as manipulation in cluttered environments, often require a collision-free shortest path to be computed quickly given a roadmap graph. Typically, the computational cost of evaluating whether an edge of the roadmap graph is collision-free dominates the running time of search algorithms. Algorithms such as Lazy Weighted A* (LWA*) and LazySP have been proposed to reduce the number of edge evaluations by employing a lazy lookahead (one-step lookahead and infinite-step lookahead, respectively). However, this comes at the expense of additional graph operations: the larger the lookahead, the more the graph operations that are typically required. We propose Lazy Receding-Horizon A* (LRA*) to minimize the total planning time by balancing edge evaluations and graph operations. Endowed with a lazy lookahead, LRA* represents a family of lazy shortest-path graph-search algorithms that generalizes LWA* and LazySP. We analyze the theoretic properties of LRA* and demonstrate empirically that, in many cases, to minimize the total planning time, the algorithm requires an intermediate lazy lookahead. Namely, using an intermediate lazy lookahead, our algorithm outperforms both LWA* and LazySP. These experiments span simulated random worlds in R^2 and R^4, and manipulation problems using a 7-DOF manipulator.

Adaptation Algorithm of Geometric Graphs for Robot Motion Planning in Dynamic Environments

This study proposes an adaptive graph algorithm for collision-free motion planning of articulated robots in dynamic environments. For this purpose, deformations of the configuration space were analyzed according to the changes of the workspace using various simulations. Subsequently, we adopted the principles of gas motion dynamics in our adaptation algorithm to address the issue of the deformation of the configuration space. The proposed algorithm has an adaptation mechanism based on expansive repulsion and sensory repulsion, and it can be performed to provide the entire adaptation using distributed processing. The simulation results confirmed that the proposed method allows the adaptation of the roadmap graph to changes of the configuration space.

Probabilistic Multi Robot Path Planning in Dynamic Environments: A Comparison between A* and DFS

paper, a probabilistic roadmap planner algorithm with the multi robot path planning problem have been proposed by using the A* search algorithm in a dynamic environment. The whole process consists of two phases. In the first phase: Preprocessing phase, the work space is converted into the configuration space, constructing a probabilistic roadmap graph in the free space, and finding the optimal path for each robot using a global planner that avoids the collision with the static obstacles. The second phase: Moving phase, moves each robot in a prioritized manner from its starting point to its ending point through a near optimal path with avoiding collision with the moving obstacles and the other robots. A comparison has been done with the depth first algorithm to see the difference. The simulation results shows that choosing A* search algorithm affect positively the speed of the two phases together in comparison to the depth first search algorithm.

Using probabilistic roadmaps in changing environments

ABSTRACTIn this paper, we examine how a path planning problem can be solved in changing environments using probabilistic roadmap planners. A probabilistic roadmap is built in static environment where all obstacles are known in advance, but we show that a roadmap can be built in such a way that it works well even when new obstacles are added to the workspace. However, our experiments show that the roadmap graph must be built carefully. We compare three different methods that are used to decide which edges are added to the roadmap graph to connect the nodes. One of these is a distance‐based method, which we present in this paper. In the tests, we built a roadmap by using only the static obstacles. Then, we added additional obstacles to the environment and tested how well the roadmap still worked. The tests showed that our distance‐based method worked quickly and that it produced roadmaps, which could be used to find a path amid additional obstacles with a high success rate.Copyright © 2013 John Wiley & Sons, Ltd.

Camera movement for chasing a subject with unknown behavior based on real-time viewpoint goodness evaluation

We propose a method that automatically generates a smooth chase camera movement to follow a subject, a user-controlled character or a character with unknown behaviors, in a 3D environment freely in real time. We consider three objectives in generating the smooth-camera movement: to avoid collisions with obstacles, to avoid subject occlusions, and to choose a good viewpoint for looking at the subject. We evaluate the goodness of viewpoints by using a viewpoint entropy map and choose the best viewpoint as the goal position of the camera in real time. Afterwards, we move the camera toward the goal position by following the shortest path, found by the A* algorithm, on a roadmap graph. The resulting camera movement has a high degree of freedom and fulfills the three objectives above. Our method is effective for third-person-view 3D applications in tracking the real-time movement of user-controlled characters in exploring a 3D environment.