Voronoi-based trajectory optimization for UGV path planning

© 2017 IEEE. Optimal path planning in dynamic environments for an unmanned vehicle is a complex task of mobile robotics that requires an integrated approach. This paper describes a path planning algorithm, which allows to build a preliminary motion trajectory using global information about environment, and then dynamically adjust the path in real-time by varying objective function weights. We introduce a set of key parameters for path optimization and the algorithm implementation in MATLAB. The developed algorithm is suitable for fast and robust trajectory tuning to a dynamically changing environment and is capable to provide efficient planning for mobile robots

On Randomized Path Coverage of Configuration Spaces

We present a sampling-based algorithm that generates a set of locally-optimal paths that differ in visibility

Планирование маршрута для беспилотного наземного робота с учетом множества критериев оптимизации

Поиск оптимального маршрута движения для беспилотного транспортного средства является сложной задачей робототехники, требующей комплексного подхода. Основными критериями оценки качества алгоритма построения маршрута являются скорость выполнения поиска и оптимальность полученного для робота пути относительно различных, задаваемых пользователем, параметров оптимизации. В данной статье описан подход к планированию маршрута, позволяющий построить предварительный маршрут движения, а затем его динамически корректировать в режиме реального времени путем изменения весовых функций для различных параметров оптимизации. Мы представляем набор ключевых параметров оптимизации маршрута и результаты работы разработанного алгоритма в среде MATLAB

An Autonomous Path Planning Method for Unmanned Aerial Vehicle based on A Tangent Intersection and Target Guidance Strategy

Unmanned aerial vehicle (UAV) path planning enables UAVs to avoid obstacles and reach the target efficiently. To generate high-quality paths without obstacle collision for UAVs, this paper proposes a novel autonomous path planning algorithm based on a tangent intersection and target guidance strategy (APPATT). Guided by a target, the elliptic tangent graph method is used to generate two sub-paths, one of which is selected based on heuristic rules when confronting an obstacle. The UAV flies along the selected sub-path and repeatedly adjusts its flight path to avoid obstacles through this way until the collision-free path extends to the target. Considering the UAV kinematic constraints, the cubic B-spline curve is employed to smooth the waypoints for obtaining a feasible path. Compared with A*, PRM, RRT and VFH, the experimental results show that APPATT can generate the shortest collision-free path within 0.05 seconds for each instance under static environments. Moreover, compared with VFH and RRTRW, APPATT can generate satisfactory collision-free paths under uncertain environments in a nearly real-time manner. It is worth noting that APPATT has the capability of escaping from simple traps within a reasonable time

Disk-Graph Probabilistic Roadmap: Biased Distance Sampling for Path Planning in a Partially Unknown Environment

International audienceIn this paper, we propose a new sampling-based path planning approach, focusing on the challenges linked to autonomous exploration. Our method relies on the definition of a disk graph of free-space bubbles, from which we derive a biased sampling function that expands the graph towards known free space for maximal navigability and frontiers discovery. The proposed method demonstrates an exploratory behavior similar to Rapidly-exploring Random Trees, while retaining the connectivity and flexibility of a graph-based planner. We demonstrate the interest of our method by first comparing its path planning capabilities against state-of-theart approaches, before discussing exploration-specific aspects, namely replanning capabilities and incremental construction of the graph. A simple frontiers-driven exploration controller derived from our planning method is also demonstrated using the Pioneer platform

Improved Connected-Component Expansion Strategies for Sampling-Based Motion Planning

Motion planning is the problem of computing valid paths through an environment. Since computing exact solutions is intractable, sampling-based algorithms, such as Probabilistic RoadMaps (PRMs), have gained popularity. PRMs compute an approximate mapping of the planning space by sacrificing completeness in favor of efficiency. However, these algorithms have certain bottlenecks that hinder performance, causing difficulty mapping narrow or crowded regions, with the asymptotic bottleneck of these algorithms being the nearest-neighbor queries required to connect the roadmap. Thus, roadmaps may fail to efficiently capture the connectivity of the planning space. In this thesis, we present a set of connected component (CC) expansion algorithms, each with different biases (random expand, expand to the nearest CC, expand away from the host CC, and expand along the medial-axis) and expansion node selection policies (random, farthest from CC's centroid, and difficult nodes), that create a linked-chain of configurations designed to enable efficient connection of CCs. Given an a priori roadmap quality requirement in terms of roadmap connectivity, we show that when our expansion methods augment PRMs, we reach the required roadmap connectivity in less time

Automated Scenario Generation Using Halton Sequences for the Verification of Autonomous Vehicle Behavior in Simulation

As autonomous vehicles continue to develop, verifying their safety remains a large hurdle to mass adoption. One component of this is testing, however it has been shown that it is impractical to statistically prove an autonomous vehicle’s safety using real-world testing alone. Therefore, simulation tools and other virtual testing methods are being employed to assist with the verification process. Testing in simulation still faces some of the challenges of the real world, such as the difficulty in exhaustively testing the system in all scenarios it will encounter. Manual scenario creation is time consuming and does not guarantee scenario coverage. Pseudo-random scenario generation is a faster option, but still does not ensure coverage of the state space. Therefore, this study proposes the use of Halton sequences to automatically generate scenarios for autonomous vehicle testing in simulation. It compares these scenarios against a set of pseudo-randomly generated scenarios and assesses the performance of each method to cover the simulation state space and provide an accurate depiction of the capabilities of the system-under-test. These tests are carried out in the CARLA simulation environment on an open source, published driving model called “Learning by Cheating” which takes place as the system-under-test. This study concludes that the scenario set generated by the Halton sequence is better at providing an accurate representation of the capabilities of the system-under-test than the pseudo-random scenario generation method