scholarly journals An Improved Genetic Algorithm for Path-Planning of Unmanned Surface Vehicle

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2640 ◽  
Author(s):  
Junfeng Xin ◽  
Jiabao Zhong ◽  
Fengru Yang ◽  
Ying Cui ◽  
Jinlu Sheng
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Autonomous Navigation ◽  
Travelling Salesman Problem ◽  
Optimal Path ◽  
Convergence Speed ◽  
Planning Problem ◽  
Unmanned Surface Vehicle ◽  
Domain Inversion ◽  
And Control

The genetic algorithm (GA) is an effective method to solve the path-planning problem and help realize the autonomous navigation for and control of unmanned surface vehicles. In order to overcome the inherent shortcomings of conventional GA such as population premature and slow convergence speed, this paper proposes the strategy of increasing the number of offsprings by using the multi-domain inversion. Meanwhile, a second fitness evaluation was conducted to eliminate undesirable offsprings and reserve the most advantageous individuals. The improvement could help enhance the capability of local search effectively and increase the probability of generating excellent individuals. Monte-Carlo simulations for five examples from the library for the travelling salesman problem were first conducted to assess the effectiveness of algorithms. Furthermore, the improved algorithms were applied to the navigation, guidance, and control system of an unmanned surface vehicle in a real maritime environment. Comparative study reveals that the algorithm with multi-domain inversion is superior with a desirable balance between the path length and time-cost, and has a shorter optimal path, a faster convergence speed, and better robustness than the others.

scholarly journals COLREG-Compliant Optimal Path Planning for Real-Time Guidance and Control of Autonomous Ships

2021 ◽  
Vol 9 (4) ◽  
pp. 405
Author(s):  
Raphael Zaccone
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Collision Avoidance ◽  
Autonomous Navigation ◽  
Optimal Path ◽  
Open Water ◽  
Optimal Path Planning ◽  
Guidance And Control ◽  
Real Time Applications ◽  
And Control

While collisions and groundings still represent the most important source of accidents involving ships, autonomous vessels are a central topic in current research. When dealing with autonomous ships, collision avoidance and compliance with COLREG regulations are major vital points. However, most state-of-the-art literature focuses on offline path optimisation while neglecting many crucial aspects of dealing with real-time applications on vessels. In the framework of the proposed motion-planning, navigation and control architecture, this paper mainly focused on optimal path planning for marine vessels in the perspective of real-time applications. An RRT*-based optimal path-planning algorithm was proposed, and collision avoidance, compliance with COLREG regulations, path feasibility and optimality were discussed in detail. The proposed approach was then implemented and integrated with a guidance and control system. Tests on a high-fidelity simulation platform were carried out to assess the potential benefits brought to autonomous navigation. The tests featured real-time simulation, restricted and open-water navigation and dynamic scenarios with both moving and fixed obstacles.

Path planning of a mobile robot using genetic heuristics

Robotica ◽  
1998 ◽  
Vol 16 (5) ◽  
pp. 575-588 ◽  
Author(s):  
Andreas C. Nearchou
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Mobile Robot ◽  
Optimal Path ◽  
Hill Climbing ◽  
Planning Problem ◽  
Well Performance ◽  
Simulation Results ◽  
Task Oriented ◽  
Path Planning Problem

A genetic algorithm for the path planning problem of a mobile robot which is moving and picking up loads on its way is presented. Assuming a findpath problem in a graph, the proposed algorithm determines a near-optimal path solution using a bit-string encoding of selected graph vertices. Several simulation results of specific task-oriented variants of the basic path planning problem using the proposed genetic algorithm are provided. The results obtained are compared with ones yielded by hill-climbing and simulated annealing techniques, showing a higher or at least equally well performance for the genetic algorithm.

scholarly journals Local path planning for mobile robots based on intermediate objectives

Robotica ◽  
2014 ◽  
Vol 33 (4) ◽  
pp. 1017-1031 ◽  
Author(s):  
Yingchong Ma ◽  
Gang Zheng ◽  
Wilfrid Perruquetti ◽  
Zhaopeng Qiu
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Autonomous Navigation ◽  
Optimal Path ◽  
Kinematic Model ◽  
Planning Problem ◽  
Planning Algorithm ◽  
Local Path ◽  
Velocity Constraints ◽  
Horizon Planning

SUMMARYThis paper presents a path planning algorithm for autonomous navigation of non-holonomic mobile robots in complex environments. The irregular contour of obstacles is represented by segments. The goal of the robot is to move towards a known target while avoiding obstacles. The velocity constraints, robot kinematic model and non-holonomic constraint are considered in the problem. The optimal path planning problem is formulated as a constrained receding horizon planning problem and the trajectory is obtained by solving an optimal control problem with constraints. Local minima are avoided by choosing intermediate objectives based on the real-time environment.

scholarly journals Efficient Path Planning of Secondary Additive Manufacturing Operations

2018 ◽  
Vol 249 ◽  
pp. 03011
Author(s):  
Keimargeo McQueen ◽  
Sara Darensbourg ◽  
Carl Moore ◽  
Tarik Dickens ◽  
Clement Allen
Keyword(s):  
Additive Manufacturing ◽  
Path Planning ◽  
Travelling Salesman Problem ◽  
Optimal Path ◽  
Planning Problem ◽  
Robotic Arms ◽  
Optimal Path Planning ◽  
Manufacturing Operations ◽  
3D Printed ◽  
Path Planner

We have designed a path planner for an additive manufacturing (AM) prototype that consists of two robotic arms which collaborate on a single part. Theoretically, with two nozzle equipped arms, a part can be 3D printed twice as fast. Moreover, equipping the second robot with a machining tool enables the completion of secondary operations like hole reaming or surface milling before the printing is finished. With two arms in the part space care must be taken to ensure that the arms collaborate intelligently; in particular, tasks must be planned so that the robots do not collide. This paper discusses the development of a robot path planner to efficiently print segments with two arms, while maintaining a safe distance between them. A solution to the travelling salesman problem, an optimal path planning problem, was used to successfully determine the robots path plans while a simple nozzle-to-nozzle distance calculation was added to represent avoiding robot-to-robot collisions. As a result, in simulation, the average part completion time was reduced by 45% over the single nozzle case. Importantly, the algorithm can theoretically be run on n-robots, so time reduction possibilities are large.

scholarly journals Proposed Smooth-STC Algorithm for Enhanced Coverage Path Planning Performance in Mobile Robot Applications

Robotics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 44 ◽  
Author(s):  
Hai Van Pham ◽  
Philip Moore ◽  
Dinh Xuan Truong
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Forest Monitoring ◽  
Planning Problem ◽  
Operational Environment ◽  
Uncertain Environments ◽  
Coverage Path Planning ◽  
Agricultural Robots ◽  
Cleaning Robots ◽  
Path Planning Problem

Robotic path planning is a field of research which is gaining traction given the broad domains of interest to which path planning is an important systemic requirement. The aim of path planning is to optimise the efficacy of robotic movement in a defined operational environment. For example, robots have been employed in many domains including: Cleaning robots (such as vacuum cleaners), automated paint spraying robots, window cleaning robots, forest monitoring robots, and agricultural robots (often driven using satellite and geostationary positional satellite data). Additionally, mobile robotic systems have been utilised in disaster areas and locations hazardous to humans (such as war zones in mine clearance). The coverage path planning problem describes an approach which is designed to determine the path that traverses all points in a defined operational environment while avoiding static and dynamic (moving) obstacles. In this paper we present our proposed Smooth-STC model, the aim of the model being to identify an optimal path, avoid all obstacles, prevent (or at least minimise) backtracking, and maximise the coverage in any defined operational environment. The experimental results in a simulation show that, in uncertain environments, our proposed smooth STC method achieves an almost absolute coverage rate and demonstrates improvement when measured against alternative conventional algorithms.

An Integrated Algorithm for Autonomous Navigation of a Mobile Robot in an Unknown Environment

2008 ◽  
Vol 12 (4) ◽  
pp. 328-335 ◽  
Author(s):  
Lee Gim Hee ◽  
◽  
Marcelo H. Ang Jr. ◽  
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Hybrid Method ◽  
Autonomous Navigation ◽  
Prior Information ◽  
Optimal Path ◽  
Unknown Environment ◽  
Global Path Planning ◽  
Local Navigation ◽  
Navigation Algorithms

Global path planning algorithms are good in planning an optimal path in a known environment, but would fail in an unknown environment and when reacting to dynamic and unforeseen obstacles. Conversely, local navigation algorithms perform well in reacting to dynamic and unforeseen obstacles but are susceptible to local minima failures. A hybrid integration of both the global path planning and local navigation algorithms would allow a mobile robot to find an optimal path and react to any dynamic and unforeseen obstacles during an operation. However, the hybrid method requires the robot to possess full or partial prior information of the environment for path planning and would fail in a totally unknown environment. The integrated algorithm proposed and implemented in this paper incorporates an autonomous exploration technique into the hybrid method. The algorithm gives a mobile robot the ability to plan an optimal path and does online collision avoidance in a totally unknown environment.

Optimal Path Planning Using Swarm Intelligence Based Hybrid Techniques

2019 ◽  
Vol 16 (9) ◽  
pp. 3717-3727
Author(s):  
Monica Sood ◽  
Sahil Verma ◽  
Vinod Kumar Panchal ◽  
Kavita
Keyword(s):  
Path Planning ◽  
Swarm Intelligence ◽  
Optimal Path ◽  
Research Work ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Planning Problem ◽  
Optimal Path Planning ◽  
Hybrid Techniques ◽  
Different Types

The planning of optimal path is an important research domain due to vast applications of optimal path planning in the robotics, simulation and modeling, computer graphics, virtual reality estimation and animation, and bioinformatics. The optimal path planning application demands to determine the collision free shortest and optimal path. There can be numerous possibilities that to find the path with optimal length based on different types of available obstacles during the path and different types of workspace environment. This research work aims to identify the optimum path from the initial source-point to final point for the unknown workspace environment consists of static obstacles. For this experimentation, swarm intelligence based hybrid concepts are considered as the work collaboration and intelligence behavior of swarm agents provides the resourceful solution of NP hard problems. Here, the hybridization of concepts makes the solution of problem more efficient. Among swarm intelligence concepts, cuckoo search (CS) algorithm is one of the efficient algorithms due to clever behavior and brood parasitic property of cuckoo birds. In this research work, two hybrid concepts are proposed. First algorithm is the hybridized concept of cuckoo search with bat algorithm (BA) termed as CS-BAPP. Another algorithm is the hybridized concept of cuckoo search with firefly algorithm (FA) termed as CS-FAPP. Both algorithms are initially tested on the benchmarks functions and applied to the path planning problem. For path planning, a real time dataset area of Alwar region situated at Rajasthan (India) is considered. The selected region consists of urban and dense vegetation land cover features. The results for the optimal path planning on Alwar region are assessed using the evaluation metrics of minimum number of iterations, error rate, success rate, and simulation time. Moreover, the results are also compared with the individual FA, BA, and CS along with the comparison of hybrid concepts.

Search and Rescue Robot Path Planning in Unknown Environment

2012 ◽  
Vol 241-244 ◽  
pp. 1682-1687 ◽  
Author(s):  
Tao Pang ◽  
Xiao Gang Ruan ◽  
Er Shen Wang ◽  
Rui Yuan Fan
Keyword(s):  
Path Planning ◽  
Learning Algorithm ◽  
Optimal Path ◽  
Local Environment ◽  
Search And Rescue ◽  
Planning Problem ◽  
Self Organizing Map ◽  
Initial State ◽  
Rescue Robot ◽  
Unknown Environment

For the path planning problem of search and rescue robot in unknown environment, a bionic learning algorithm was proposed. The GSOM (Growing Self-organizing Map) algorithm was used to build the environment cognitive map. The heuristic search A* algorithm was used to find the global optimal path from initial state to target state. When the local environment was changed, reinforcement learning algorithm based on sensor information was used to guide the search and rescue robot behavior of local path planning. Simulation results show the method effectiveness.

scholarly journals Fleet management system for mobile robots in healthcare environments

2021 ◽  
Vol 14 (1) ◽  
pp. 55
Author(s):  
Eduardo Guzmán Ortiz ◽  
Beatriz Andres ◽  
Francisco Fraile ◽  
Raul Poler ◽  
Ángel Ortiz Bas
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Mobile Robots ◽  
Hospital Environment ◽  
Fleet Management ◽  
Planning Problem ◽  
Dynamic Path Planning ◽  
Healthcare Environments ◽  
Dynamic Path ◽  
Path Planning Problem

Purpose: The purpose of this paper is to describe the implementation of a Fleet Management System (FMS) that plans and controls the execution of logistics tasks by a set of mobile robots in a real-world hospital environment. The FMS is developed upon an architecture that hosts a routing engine, a task scheduler, an Endorse Broker, a controller and a backend Application Programming Interface (API). The routing engine handles the geo-referenced data and the calculation of routes; the task scheduler implements algorithms to solve the task allocation problem and the trolley loading problem using Integer Linear Programming (ILP) model and a Genetic Algorithm (GA) depending on the problem size. The Endorse Broker provides a messaging system to exchange information with the robotic fleet, while the controller implements the control rules to ensure the execution of the work plan. Finally, the Backend API exposes some FMS to external systems.Design/methodology/approach: The first part of the paper, focuses on the dynamic path planning problem of a set of mobile robots in indoor spaces such as hospitals, laboratories and shopping centres. A review of algorithms developed in the literature, to address dynamic path planning, is carried out; and an analysis of the applications of such algorithms in mobile robots that operate in real in-door spaces is performed. The second part of the paper focuses on the description of the FMS, which consists of five integrated tools to support the multi-robot dynamic path planning and the fleet management.Findings: The literature review, carried out in the context of path planning problem of multiple mobile robots in in-door spaces, has posed great challenges due to the environment characteristics in which robots move. The developed FMS for mobile robots in healthcare environments has resulted on a tool that enables to: (i) interpret of geo-referenced data; (ii) calculate and recalculate dynamic path plans and task execution plans, through the implementation of advanced algorithms that take into account dynamic events; (iii) track the tasks execution; (iv) fleet traffic control; and (v)  to communicate with one another external systems.Practical implications: The proposed FMS has been developed under the scope of ENDORSE project that seeks to develop safe, efficient, and integrated indoor robotic fleets for logistic applications in healthcare and commercial spaces. Moreover, a computational analysis is performed using a virtual hospital floor-plant.Originality/value: This work proposes a novel FMS, which consists of integrated tools to support the mobile multi-robot dynamic path planning in a real-world hospital environment. These tools include: a routing engine that handles the geo-referenced data and the calculation of routes. A task scheduler that includes a mathematical model to solve the path planning problem, when a low number of robots is considered. In order to solve large size problems, a genetic algorithm is also implemented to compute the dynamic path planning with less computational effort. An Endorse broker to exchanges information between the robotic fleet and the FMS in a secure way. A backend API that provides interface to manage the master data of the FMS, to calculate an optimal assignment of a set of tasks to a group of robots to be executed on a specific date and time, and to add a new task to be executed in the current shift. Finally, a controller to ensures that the robots execute the tasks that have been assigned by the task scheduler.

Sign in / Sign up

Export Citation Format

Share Document