scholarly journals Path planning and collision avoidance for autonomous surface vehicles II: a comparative study of algorithms

2021 ◽  
Author(s):  
Anete Vagale ◽  
Robin T. Bye ◽  
Rachid Oucheikh ◽  
Ottar L. Osen ◽  
Thor I. Fossen
Keyword(s):  
Path Planning ◽  
Comparative Study ◽  
Collision Avoidance ◽  
State Of The Art ◽  
Potential Fields ◽  
Large Set ◽  
Enabling Technology ◽  
Fast Marching ◽  
Autonomous Surface Vehicles ◽  
Current State

AbstractArtificial intelligence is an enabling technology for autonomous surface vehicles, with methods such as evolutionary algorithms, artificial potential fields, fast marching methods, and many others becoming increasingly popular for solving problems such as path planning and collision avoidance. However, there currently is no unified way to evaluate the performance of different algorithms, for example with regard to safety or risk. This paper is a step in that direction and offers a comparative study of current state-of-the art path planning and collision avoidance algorithms for autonomous surface vehicles. Across 45 selected papers, we compare important performance properties of the proposed algorithms related to the vessel and the environment it is operating in. We also analyse how safety is incorporated, and what components constitute the objective function in these algorithms. Finally, we focus on comparing advantages and limitations of the 45 analysed papers. A key finding is the need for a unified platform for evaluating and comparing the performance of algorithms under a large set of possible real-world scenarios.

scholarly journals LEO: Liquid Exploration Online

2020 ◽  
Vol 2 (1) ◽  
pp. 58-80
Author(s):  
Frank Hoeller
Keyword(s):  
Path Planning ◽  
State Of The Art ◽  
Simulation Experiments ◽  
Coverage Path Planning ◽  
Current State ◽  
Extensive Evaluation ◽  
Novel Approach ◽  
Local Navigation ◽  
Local Decision ◽  
Real Robot

This article introduces a novel approach to the online complete- coverage path planning (CCPP) problem that is specically tailored to the needs of skid-steer tracked robots. In contrast to most of the current state-of-the-art algorithms for this task, the proposed algorithm reduces the number of turning maneuvers, which are responsible for a large part of the robot's energy consumption. Nevertheless, the approach still keeps the total distance traveled at a competitive level. The algorithm operates on a grid-based environment representation and uses a 3x3 prioritization matrix for local navigation decisions. This matrix prioritizes cardinal di- rections leading to a preference for straight motions. In case no progress can be achieved based on a local decision, global path planning is used to choose a path to the closest known unvisited cell, thereby guaranteeing completeness of the approach. In an extensive evaluation using simulation experiments, we show that the new algorithm indeed generates competi- tively short paths with largely reduced turning costs, compared to other state-of-the-art CCPP algorithms. We also illustrate its performance on a real robot.

Single Trajectory Learning: Exploration Versus Exploitation

2018 ◽  
Vol 32 (06) ◽  
pp. 1859009 ◽  
Author(s):  
Qiming Fu ◽  
Quan Liu ◽  
Shan Zhong ◽  
Heng Luo ◽  
Hongjie Wu ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Polynomial Function ◽  
State Of The Art ◽  
Large Set ◽  
Crucial Issue ◽  
Reward Function ◽  
Current State ◽  
Exploration Exploitation ◽  
Exploration Versus Exploitation ◽  
Trajectory Learning

In reinforcement learning (RL), the exploration/exploitation (E/E) dilemma is a very crucial issue, which can be described as searching between the exploration of the environment to find more profitable actions, and the exploitation of the best empirical actions for the current state. We focus on the single trajectory RL problem where an agent is interacting with a partially unknown MDP over single trajectories, and try to deal with the E/E in this setting. Given the reward function, we try to find a good E/E strategy to address the MDPs under some MDP distribution. This is achieved by selecting the best strategy in mean over a potential MDP distribution from a large set of candidate strategies, which is done by exploiting single trajectories drawn from plenty of MDPs. In this paper, we mainly make the following contributions: (1) We discuss the strategy-selector algorithm based on formula set and polynomial function. (2) We provide the theoretical and experimental regret analysis of the learned strategy under an given MDP distribution. (3) We compare these methods with the “state-of-the-art” Bayesian RL method experimentally.

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.

Consideration of obstacle danger level in path planning using and Fast-Marching optimisation: comparative study

2003 ◽  
Vol 83 (11) ◽  
pp. 2387-2396 ◽  
Author(s):  
P. Melchior ◽  
B. Orsoni ◽  
O. Lavialle ◽  
A. Poty ◽  
A. Oustaloup
Keyword(s):  
Path Planning ◽  
Comparative Study ◽  
Fast Marching ◽  
Danger Level

scholarly journals Adapting Behaviors via Reactive Synthesis

2021 ◽  
pp. 870-893
Author(s):  
Gal Amram ◽  
Suguman Bansal ◽  
Dror Fried ◽  
Lucas Martinelli Tabajara ◽  
Moshe Y. Vardi ◽  
...  
Keyword(s):  
Time Complexity ◽  
Design Pattern ◽  
State Of The Art ◽  
Test Cases ◽  
Large Set ◽  
Synthesis Algorithm ◽  
Reactive Synthesis ◽  
Current State ◽  
Scalable Synthesis ◽  
Hardware Platforms

AbstractIn the Adapter Design Pattern, a programmer implements a Target interface by constructing an Adapter that accesses an existing Adaptee code. In this work, we present a reactive synthesis interpretation to the adapter design pattern, wherein an algorithm takes an Adaptee and a Target transducers, and the aim is to synthesize an Adapter transducer that, when composed with the Adaptee, generates a behavior that is equivalent to the behavior of the Target. One use of such an algorithm is to synthesize controllers that achieve similar goals on different hardware platforms. While this problem can be solved with existing synthesis algorithms, current state-of-the-art tools fail to scale. To cope with the computational complexity of the problem, we introduce a special form of specification format, called Separated GR(k), which can be solved with a scalable synthesis algorithm but still allows for a large set of realistic specifications. We solve the realizability and the synthesis problems for Separated GR(k), and show how to exploit the separated nature of our specification to construct better algorithms, in terms of time complexity, than known algorithms for GR(k) synthesis. We then describe a tool, called SGR(k), that we have implemented based on the above approach and show, by experimental evaluation, how our tool outperforms current state-of-the-art tools on various benchmarks and test-cases.

scholarly journals A Comparative Study of Minimally Supervised Morphological Segmentation

2016 ◽  
Vol 42 (1) ◽  
pp. 91-120 ◽  
Author(s):  
Teemu Ruokolainen ◽  
Oskar Kohonen ◽  
Kairit Sirts ◽  
Stig-Arne Grönroos ◽  
Mikko Kurimo ◽  
...  
Keyword(s):  
Comparative Study ◽  
Empirical Evaluation ◽  
Boundary Detection ◽  
Literature Survey ◽  
Large Set ◽  
Current State ◽  
Morphological Segmentation ◽  
Word Forms ◽  
Morphology Learning ◽  
Minimally Supervised

This article presents a comparative study of a subfield of morphology learning referred to as minimally supervised morphological segmentation. In morphological segmentation, word forms are segmented into morphs, the surface forms of morphemes. In the minimally supervised data-driven learning setting, segmentation models are learned from a small number of manually annotated word forms and a large set of unannotated word forms. In addition to providing a literature survey on published methods, we present an in-depth empirical comparison on three diverse model families, including a detailed error analysis. Based on the literature survey, we conclude that the existing methodology contains substantial work on generative morph lexicon-based approaches and methods based on discriminative boundary detection. As for which approach has been more successful, both the previous work and the empirical evaluation presented here strongly imply that the current state of the art is yielded by the discriminative boundary detection methodology.

scholarly journals Path planning and collision avoidance for autonomous surface vehicles I: a review

2021 ◽  
Author(s):  
Anete Vagale ◽  
Rachid Oucheikh ◽  
Robin T. Bye ◽  
Ottar L. Osen ◽  
Thor I. Fossen
Keyword(s):  
Path Planning ◽  
Collision Avoidance ◽  
Regulatory Framework ◽  
Autonomous Surface Vehicles ◽  
Navigation And Control ◽  
Potential Benefits ◽  
Planning Algorithms ◽  
And Control

AbstractAutonomous surface vehicles are gaining increasing attention worldwide due to the potential benefits of improving safety and efficiency. This has raised the interest in developing methods for path planning that can reduce the risk of collisions, groundings, and stranding accidents at sea, as well as costs and time expenditure. In this paper, we review guidance, and more specifically, path planning algorithms of autonomous surface vehicles and their classification. In particular, we highlight vessel autonomy, regulatory framework, guidance, navigation and control components, advances in the industry, and previous reviews in the field. In addition, we analyse the terminology used in the literature and attempt to clarify ambiguities in commonly used terms related to path planning. Finally, we summarise and discuss our findings and highlight the potential need for new regulations for autonomous surface vehicles.

Sign in / Sign up

Export Citation Format

Share Document