Vector Field Histogram* with look-ahead tree extension dependent on time variable environment

Abstract

The article deals with a novel approach to reactive navigation. A proposed reactive navigation is based on the Vector Field Histogram (VFH) method, which is easily modifiable. The biggest advantage of the proposed method is that it allows the robot to avoid static as well as moving obstacles in an unknown environment in a more effective way and without the need of switching any algorithm or the robot’s behaviour. Moreover, the proposed extension allows the avoidance of several moving obstacles in real time. The article contains three proposed improvements of the original VFH* method: representation of moving objects, look-ahead tree improvements and approach to criterion function. The method is verified by several experiments in various scenarios, which include few static and moving obstacles.

A New Path Evaluation Method for Path Planning With Localizability

“…The potential field approach, VFF (Virtual Force Field) and VFH (Vector Field Histogram) approaches are three widely known local path planning algorithms that help the robot move along the gradient of a potential field or gaps between obstacles. Their most recent developments can be found in [12], [13], where time variant environments and trap problems have been considered. Ulises Orozco-Rosas proposed a membrane evolutionary artificial field path planning method where a Genetic Algorithm and APF is blended through a membrane structure, to provide feasible and efficient paths in both static and dynamic environments [14].…”

“…Artificial intelligence methods like fuzzy logical control and neural network are also employed in local path planning. Some of their most recent developments can be found in [16] and [13]. Ashanie Gunathillake et al proposed a navigation algorithm for source seeking in a sensor network environment where the robot is localized in a topology coordinates system based on a packet reception probability function and a packet reception binary matrix [17].…”

“…So, according to the prior impact analysis, a sigmoid function is employed to evaluate the impact of δ f_D of pose S. Our evaluation function E( δ f_D ) represents the evaluation of δ f_D as formula (16) while the smaller the value of E( δ f_D ) is the better. Here r u is the lowest limitation defined by formula (17), which comes from the relationship defined in formulas (12) and (13). Parameter k 1 defines the raising ratio of the evaluation value.…”

An advanced potential field method proposed for mobile robot path planning

“…Autonomous mobile robot path planning represents a significant area of research in robotics. It aims to find a safe collision-free path from the start position to the goal while avoiding obstacles (Babinec et al, 2018; Hachour, 2008; Hong et al, 2011; Jiang et al, 2015). Path planning can be divided into global path planning based on a known environment and local path planning based on sensor information (Azzabi and Nouri, 2017; Azzabi et al, 2014).…”

Optimal Path-Planning of Nonholonomic Terrain Robots for Dynamic Obstacle Avoidance Using Single-Time Velocity Estimator and Reinforcement Learning Approach

“…Local obstacle avoidance techniques are employed online in the absence of global information, typically via popular methods such as artificial potential fields (APFs) [11], vector field histograms (VFH) [12], and the dynamic window approach (DWA) [13], which extends local avoidance approaches on account of kinematics constraints. Literature is abundant with various strategies proposed to address the problem of obstacle avoidance of automated robots [14]- [23].…”