Hybrid PSO-cubic spline for autonomous robots optimal trajectory planning

This paper presents a new version of the Particle Swarm Optimization algorithm where the particles are replaced by spline functions. The developed algorithm generates smooth motion trajectories with two times continuously differentiable curvature avoiding obstacles placed in the workspace. It can be used for autonomous robot path planning or transport problems. The spline based trajectory generation gives us continuous, smooth and optimized path trajectories. Simulation and experimental results demonstrate the effectiveness of the proposed method.info:eu-repo/semantics/publishedVersio

A comprehensive review on different path planning methods for autonomous vehicles

Autonomous vehicle is an active field where researches are going on to improve the vehicle's capability to travel autonomously from one place to another. Vehicle has to progress through different levels of control structure to navigate through different environments. Among those path planning plays a major role in autonomous vehicles navigation as different planning methods need to be used for planning the path at different intersections for the vehicle. However, AVs still face some challenges in urban intersections such as roundabouts, obstacle avoidance, which need to be resolved for completely automated path planning in AVs. So, this paper presents an overview on different path planning methods implemented in autonomous navigation. A description on different path planning methods and implementation of these methods by different authors is presented

Path planning for autonomous navigation in roundabouts using an improved triangular based polynomial estimation technique for bezier curve generation with aa and aa* algorithms

Autonomous vehicle (AVs) development is a rapidly growing research field. The AVs need to inspect the environment and detect the obstacle to create a path according to the environment. Path planning is a critical stage in the operation of vehicles, wherein the path is generated based on the surrounding environment to ensure safe and efficient navigation. The path is created for the vehicle to travel through different intersections. Among that roundabout is a type of intersection where path planning is challenging due to their complex shape and different traffic rules. To overcome this, different curve fitting methods were used to create path. However, the commonly used method is Bezier curve based curve fitting method which depends on the position of the control points. Many researchers have established different methods to position the control points but the change in the shape of roundabout cause the path to be inaccurate as the position of the control points are fixed. Therefore, the objective of the research is to introduce an enhanced method for calculating the control points for Bezier curve generation according to the shape of the roundabout. A triangular-based polynomial estimation technique is introduced, which helps calculate the Bezier curve's control points based on the points selected in the path. An equation is introduced which helps in calculating the control points based on the points selected and the respective segmentation factor

Risk-based autonomous vehicle motion control with considering human driver’s behaviour

The selected motions of autonomous vehicles (AVs) are subject to the constraints from the surrounding traffic environment, infrastructure and the vehicle’s dynamic capabilities. Normally, the motion control of the vehicle is composed of trajectory planning and trajectory following according to the surrounding risk factors, the vehicles’ capabilities as well as tyre/road interaction situations. However, pure trajectory following with a unique path may make the motion control of the vehicle be too careful and cautious with a large amount of steering effort. To follow a planned trajectory, the AVs with the traditional path-following control algorithms will correct their states even if the vehicles have only a slight deviation from the desired path or the vehicle detects static infrastructure like roadside trees. In this case, the safety of the AVs can be guaranteed to some degree, but the comfort and sense of hazards for the drivers are ignored, and sometimes the AVs have unusual motion behaviours which may not be acceptable to other road users. To solve this problem, this study aims to develop a safety corridor-based vehicle motion control approach by investigating human-driven vehicle behaviour and the vehicle’s dynamic capabilities. The safety corridor is derived by the manoeuvring action feedback of actual drivers as collected in a driving simulator when presented with surrounding risk elements and enables the AVs to have safe trajectories within it. A corridor-based Nonlinear Model Predictive Control (NMPC) has been developed which controls the vehicle state to achieve a smooth and comfortable trajectory while applying trajectory constraints using the safety corridor. The safety corridor and motion controller are assessed using four typical scenarios to show that the vehicle has a human-like or human-oriented behaviour which is expected to be more acceptable for both drivers and other road users

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

A method for real-time dynamic fleet mission planning for autonomous mining

This paper introduces a method for dynamic fleet mission planning for autonomous mining (in loop-free maps), in which a dynamic fleet mission is defined as a sequence of static fleet missions, each generated using a modified genetic algorithm. For the case of static fleet mission planning (where each vehicle completes just one mission), the proposed method is able to reliably generate, within a short optimization time, feasible fleet missions with short total duration and as few stops as possible. For the dynamic case, in simulations involving a realistic mine map, the proposed method is able to generate efficient dynamic plans such that the number of completed missions per vehicle is only slightly reduced as the number of vehicles is increased, demonstrating the favorable scaling properties of the method as well as its applicability in real-world cases

Planning smooth and obstacle-avoiding b-spline paths for autonomous mining vehicles

We study the problem of automatic generation of smooth and obstacle-avoiding planar paths for efficient guidance of autonomous mining vehicles. Fast traversal of a path is of special interest. We consider four-wheel four-gear articulated vehicles and assume that we have an a priori knowledge of the mine wall environment in the form of polygonal chains. Computing quartic uniform B-spline curves, minimizing curvature variation, staying at least at a proposed safety margin distance from the mine walls, we plan high speed paths.Validerad; 2010; Bibliografisk uppgift: Paper id:: T-ASE-2008-162; 20080612 (tb)</p

Path planning and map monitoring for self-driving vehicles based on HD maps

Este trabajo ha sido realizado dentro del contexto del proyecto Techs4AgeCar en el grupo de investigación Robesafe, cuyo objetivo es el desarrollo de un vehículo de conducción autónoma. Forma parte de dos líneas distintas del proyecto, la de mapeado y la de planificación, ya que ambas están directamente relacionadas. Se ha desarrollado un planificador de rutas global basado en mapas de alta defición (HD Maps) offline previamente generados. Por otro lado, también se ha cubierto toda la parte de generación de mapas que posteriormente son utilizados por el planificador. Además, se ha desarrollado un módulo capaz de aprovechar la información proporcionado por el mapa, de forma que se monitorizan los elementos relevantes y cercanos al coche que afectan a la ruta, como son carriles, intersecciones y elementos regulatoriosThis work has been done within the context of the Techs4AgeCar project in the Robesafe research group, whose project focuses on the development of an autonomous driving vehicle. This work is part of two different layers of the project, mapping and planning layers, since both are directly related. A global route planner has been developed based on previously generated offline HD Maps. Therefore, the entire part of generating maps that are later used by the planner has also been covered. In addition, a module capable of taking advantage of the information provided by the map has been developed, so that the relevant elements close to the vehicle that affect the route such as lanes, intersections and regulatory elements are monitored.Máster Universitario en Ingeniería Industrial (M 141