scholarly journals Convex Decomposition for a Coverage Path Planning for Autonomous Vehicles: Interior Extension of Edges

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4165 ◽  
Author(s):  
Lasse Damtoft Nielsen ◽  
Inkyung Sung ◽  
Peter Nielsen
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Decomposition Methods ◽  
Target Area ◽  
Solution Approach ◽  
Coverage Path Planning ◽  
Convex Decomposition ◽  
Common Solution ◽  
Area Of Interest

To cover an area of interest by an autonomous vehicle, such as an Unmanned Aerial Vehicle (UAV), planning a coverage path which guides the unit to cover the area is an essential process. However, coverage path planning is often problematic, especially when the boundary of the area is complicated and the area contains several obstacles. A common solution for this situation is to decompose the area into disjoint convex sub-polygons and to obtain coverage paths for each sub-polygon using a simple back-and-forth pattern. Aligned with the solution approach, we propose a new convex decomposition method which is simple and applicable to any shape of target area. The proposed method is designed based on the idea that, given an area of interest represented as a polygon, a convex decomposition of the polygon mainly occurs at the points where an interior angle between two edges of the polygon is greater than 180 degrees. The performance of the proposed method is demonstrated by comparison with existing convex decomposition methods using illustrative examples.

scholarly journals Complete Coverage Path Planning of an Unmanned Surface Vehicle Based on a Complete Coverage Neural Network Algorithm

2021 ◽  
Vol 9 (11) ◽  
pp. 1163
Author(s):  
Peng-Fei Xu ◽  
Yan-Xu Ding ◽  
Jia-Cheng Luo
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Target Area ◽  
Complete Coverage ◽  
A Algorithm ◽  
Unmanned Surface Vehicle ◽  
Calculation Time ◽  
Coverage Path Planning ◽  
Practical Applications ◽  
Area Of Interest

In practical applications, an unmanned surface vehicle (USV) generally employs a task of complete coverage path planning for exploration in a target area of interest. The biological inspired neural network (BINN) algorithm has been extensively employed in path planning of mobile robots, recently. In this paper, a complete coverage neural network (CCNN) algorithm for the path planning of a USV is proposed for the first time. By simplifying the calculation process of the neural activity, the CCNN algorithm can significantly reduce calculation time. To improve coverage efficiency and make the path more regular, the optimal next position decision formula combined with the covering direction term is established. The CCNN algorithm has increased moving directions of the path in grid maps, which in turn has further reduced turning-angles and makes the path smoother. Besides, an improved A* algorithm that can effectively decrease path turns is presented to escape the deadlock. Simulations are carried out in different environments in this work. The results show that the coverage path generated by the CCNN algorithm has less turning-angle accumulation, deadlocks, and calculation time. In addition, the CCNN algorithm is capable to maintain the covering direction and adapt to complex environments, while effectively escapes deadlocks. It is applicable for USVs to perform multiple engineering missions.

scholarly journals Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2244
Author(s):  
S. M. Yang ◽  
Y. A. Lin
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Random Trees ◽  
Average Deviation ◽  
Lane Changes ◽  
Path Smoothing ◽  
Rapidly Exploring Random Trees

Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a “near”-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional–integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.

scholarly journals Auction-Based Consensus of Autonomous Vehicles for Multi-Target Dynamic Task Allocation and Path Planning in an Unknown Obstacle Environment

2021 ◽  
Vol 11 (11) ◽  
pp. 5057
Author(s):  
Wan-Yu Yu ◽  
Xiao-Qiang Huang ◽  
Hung-Yi Luo ◽  
Von-Wun Soo ◽  
Yung-Lung Lee
Keyword(s):  
Path Planning ◽  
Task Allocation ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Dynamic Task ◽  
The Core ◽  
Dynamic Task Allocation ◽  
Vehicle Technology ◽  
Future Work ◽  
Complicated Task

The autonomous vehicle technology has recently been developed rapidly in a wide variety of applications. However, coordinating a team of autonomous vehicles to complete missions in an unknown and changing environment has been a challenging and complicated task. We modify the consensus-based auction algorithm (CBAA) so that it can dynamically reallocate tasks among autonomous vehicles that can flexibly find a path to reach multiple dynamic targets while avoiding unexpected obstacles and staying close as a group as possible simultaneously. We propose the core algorithms and simulate with many scenarios empirically to illustrate how the proposed framework works. Specifically, we show that how autonomous vehicles could reallocate the tasks among each other in finding dynamically changing paths while certain targets may appear and disappear during the movement mission. We also discuss some challenging problems as a future work.

Multi-robot coverage path planning using hexagonal segmentation for geophysical surveys

Robotica ◽  
2018 ◽  
Vol 36 (8) ◽  
pp. 1144-1166 ◽  
Author(s):  
Héctor Azpúrua ◽  
Gustavo M. Freitas ◽  
Douglas G. Macharet ◽  
Mario F. M. Campos
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Autonomous Robots ◽  
Mineral Exploration ◽  
Statistical Investigation ◽  
Coverage Path Planning ◽  
Simulated Environment ◽  
Outdoor Setting ◽  
Geophysical Surveys ◽  
Multi Robot

SUMMARYThe field of robotics has received significant attention in our society due to the extensive use of robotic manipulators; however, recent advances in the research on autonomous vehicles have demonstrated a broader range of applications, such as exploration, surveillance, and environmental monitoring. In this sense, the problem of efficiently building a model of the environment using cooperative mobile robots is critical. Finding routes that are either length or time-optimized is essential for real-world applications of small autonomous robots. This paper addresses the problem of multi-robot area coverage path planning for geophysical surveys. Such surveys have many applications in mineral exploration, geology, archeology, and oceanography, among other fields. We propose a methodology that segments the environment into hexagonal cells and allocates groups of robots to different clusters of non-obstructed cells to acquire data. Cells can be covered by lawnmower, square or centroid patterns with specific configurations to address the constraints of magneto-metric surveys. Several trials were executed in a simulated environment, and a statistical investigation of the results is provided. We also report the results of experiments that were performed with real Unmanned Aerial Vehicles in an outdoor setting.

scholarly journals Review on path planning algorithm for unmanned aerial vehicles

2021 ◽  
Vol 24 (2) ◽  
pp. 1017
Author(s):  
Nurul Saliha Amani Ibrahim ◽  
Faiz Asraf Saparudin
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Computation Time ◽  
Planning Problem ◽  
Aerial Vehicles ◽  
Planning Algorithm ◽  
Path Planning Algorithm

The path planning problem has been a crucial topic to be solved in autonomous vehicles. Path planning consists operations to find the route that passes through all of the points of interest in a given area. Several algorithms have been proposed and outlined in the various literature for the path planning of autonomous vehicle especially for unmanned aerial vehicles (UAV). The algorithms are not guaranteed to give full performance in each path planning cases but each one of them has their own specification which makes them suitable in sophisticated situation. This review paper evaluates several possible different path planning approaches of UAVs in terms optimal path, probabilistic completeness and computation time along with their application in specific problems.

scholarly journals Multi UAV Coverage Path Planning in Urban Environments

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7365
Author(s):  
Javier Muñoz ◽  
Blanca López ◽  
Fernando Quevedo ◽  
Concepción A. Monje ◽  
Santiago Garrido ◽  
...  
Keyword(s):  
Path Planning ◽  
Collision Avoidance ◽  
Urban Environments ◽  
Fast Marching ◽  
Flight Pattern ◽  
Cluttered Environments ◽  
Coverage Path Planning ◽  
Area Of Interest ◽  
Smooth Path ◽  
Multi Uav

Coverage path planning (CPP) is a field of study which objective is to find a path that covers every point of a certain area of interest. Recently, the use of Unmanned Aerial Vehicles (UAVs) has become more proficient in various applications such as surveillance, terrain coverage, mapping, natural disaster tracking, transport, and others. The aim of this paper is to design efficient coverage path planning collision-avoidance capable algorithms for single or multi UAV systems in cluttered urban environments. Two algorithms are developed and explored: one of them plans paths to cover a target zone delimited by a given perimeter with predefined coverage height and bandwidth, using a boustrophedon flight pattern, while the other proposed algorithm follows a set of predefined viewpoints , calculating a smooth path that ensures that the UAVs pass over the objectives. Both algorithms have been developed for a scalable number of UAVs, which fly in a triangular deformable leader-follower formation with the leader at its front. In the case of an even number of UAVs, there is no leader at the front of the formation and a virtual leader is used to plan the paths of the followers. The presented algorithms also have collision avoidance capabilities, powered by the Fast Marching Square algorithm. These algorithms are tested in various simulated urban and cluttered environments, and they prove capable of providing safe and smooth paths for the UAV formation in urban environments.

MPC-Based Control of Autonomous Vehicles With Localized Path Planning for Obstacle Avoidance Under Uncertainties

2019 ◽  
Author(s):  
Sai Rajeev Devaragudi ◽  
Bo Chen
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Obstacle Detection ◽  
Sensor Data ◽  
Constant Time ◽  
Longitudinal Motion ◽  
Driver Assistance Systems

Abstract This paper presents a Model Predictive Control (MPC) approach for longitudinal and lateral control of autonomous vehicles with a real-time local path planning algorithm. A heuristic graph search method (A* algorithm) combined with piecewise Bezier curve generation is implemented for obstacle avoidance in autonomous driving applications. Constant time headway control is implemented for a longitudinal motion to track lead vehicles and maintain a constant time gap. MPC is used to control the steering angle and the tractive force of the autonomous vehicle. Furthermore, a new method of developing Advanced Driver Assistance Systems (ADAS) algorithms and vehicle controllers using Model-In-the-Loop (MIL) testing is explored with the use of PreScan®. With PreScan®, various traffic scenarios are modeled and the sensor data are simulated by using physics-based sensor models, which are fed to the controller for data processing and motion planning. Obstacle detection and collision avoidance are demonstrated using the presented MPC controller.

A Connected Autonomous Vehicle Emulator (CAVE) for Testing Multi-agent, Conventional–Autonomous Mixed Vehicle Traffic Scenarios

2021 ◽  
pp. 339-358
Author(s):  
Dan Negrut ◽  
Asher Elmquist ◽  
Radu Serban ◽  
Dylan Hatch ◽  
Parmesh Ramanathan
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Computer Programs ◽  
Software Infrastructure ◽  
Vehicle Traffic ◽  
Traffic Conditions ◽  
Multi Agent

We discuss a software infrastructure that provides a virtual proving ground for designing, training, and auditing the computer programs used to pilot connected autonomous vehicles (CAVs). This effort does not concentrate on developing the piloting computer programs (PCPs) responsible for path planning in autonomous vehicles (AVs). Instead, we have established a first version of an emulation platform that changes the PCP design/test/improve process, which is often times carried out covertly [46], or in actual traffic conditions with potentially fatal consequences [45, 47].

scholarly journals Survey on Coverage Path Planning with Unmanned Aerial Vehicles

Drones ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 4 ◽  
Author(s):  
Tauã Cabreira ◽  
Lisane Brisolara ◽  
Paulo R. Ferreira Jr.
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Performance Metrics ◽  
Partial Information ◽  
Convex Polygons ◽  
Cellular Decomposition ◽  
Coverage Path Planning ◽  
Area Of Interest ◽  
Aerial Vehicles ◽  
Smart Farming

Coverage path planning consists of finding the route which covers every point of a certain area of interest. In recent times, Unmanned Aerial Vehicles (UAVs) have been employed in several application domains involving terrain coverage, such as surveillance, smart farming, photogrammetry, disaster management, civil security, and wildfire tracking, among others. This paper aims to explore and analyze the existing studies in the literature related to the different approaches employed in coverage path planning problems, especially those using UAVs. We address simple geometric flight patterns and more complex grid-based solutions considering full and partial information about the area of interest. The surveyed coverage approaches are classified according to a classical taxonomy, such as no decomposition, exact cellular decomposition, and approximate cellular decomposition. This review also contemplates different shapes of the area of interest, such as rectangular, concave and convex polygons. The performance metrics usually applied to evaluate the success of the coverage missions are also presented.

scholarly journals A System of Shared Autonomous Vehicles Combined with Park-And-Ride in Residential Areas

2019 ◽  
Vol 11 (11) ◽  
pp. 3113 ◽  
Author(s):  
Yefang Zhou ◽  
Yanyan Li ◽  
Mingyang Hao ◽  
Toshiyuki Yamamoto
Keyword(s):  
Autonomous Vehicles ◽  
Population Aging ◽  
Autonomous Vehicle ◽  
Wait Time ◽  
Outer Edge ◽  
Metropolitan Region ◽  
Target Area ◽  
Residential Areas ◽  
Personal Mobility ◽  
Park And Ride

As suburbanization and unprecedented population aging are converging, enhanced personal mobility for suburban residents is required. In this study, a collaborative scheme involving park-and-ride services associated with public transport and a shared autonomous vehicle system are proposed. Two residential areas in the Nagoya metropolitan region, Japan, are considered: a residential area at the outer edge of a subway line and a commuter town with a nearby railway station. Three user groups are assumed: park-and-ride commuters who park shared autonomous vehicles at the station and take the train to their workplaces; inbound commuters who disembark from trains at the station and use the vehicles to reach their workplaces within the target area; and elderly and disabled residents, who use shared autonomous vehicles for trips within the target area. The system performance is investigated through agent-based simulation. The results suggest that, in the edge case, approximately 400 shared autonomous vehicles can facilitate more than 10,000 trips at an appropriate level of service. For the commuter town, fewer than 400 vehicles can provide rapid responses with a wait time of approximately 5 min for more than 5000 trips per day. Thus, the proposed system can feasibly provide a quick response service.

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