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.

Path Planning for Vehicle-borne System Consisting of Multi Air–ground Robots

Robotica ◽  
2019 ◽  
Vol 38 (3) ◽  
pp. 493-511 ◽  
Author(s):  
Yang Chen ◽  
Shiwen Ren ◽  
Zhihuan Chen ◽  
Mengqing Chen ◽  
Huaiyu Wu
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Maximum Speed ◽  
Planning Problem ◽  
Robot System ◽  
Ground Vehicle ◽  
Dynamic Constraints ◽  
Charging Time ◽  
Path Planning Problem ◽  
Flying Robots

SummaryThis paper considers the path planning problem for deployment and collection of a marsupial vehicle system which consists of a ground mobile robot and two aerial flying robots. The ground mobile robot, usually unmanned ground vehicle (UGV), as a carrier, is able to deploy and harvest the aerial flying robots, and each aerial flying robot, usually unmanned aerial vehicles (UAVs), takes off from and lands on the carrier. At the same time, owing to the limited duration in the air in one flight, UAVs should return to the ground mobile robot timely for its energy-saving and recharge. This work is motivated by cooperative search and reconnaissance missions in the field of heterogeneous robot system. Especially, some targets with given positions are assumed to be visited by any of the UAVs. For the cooperative path planning problem, this paper establishes a mathematical model to solve the path of two UAVs and UGV. Many real constraints including the maximum speed of two UAVs and UGV, the minimum charging time of two UAVs, the maximum hovering time of UAVs, and the dynamic constraints among UAVs and UGV are considered. The objective function is constructed by minimizing the time for completing the whole mission. Finally, the path planning problem of the robot system is transformed into a multi-constrained optimization problem, and then the particle swarm optimization algorithm is used to obtain the path planning results. Simulations and comparisons verify the feasibility and effectiveness of the proposed method.

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.

Flight Path Planning Based on PSO and Minimum Threat Surface

2012 ◽  
Vol 461 ◽  
pp. 482-486
Author(s):  
Xun Qiang Hu ◽  
Xiao Fang Xie ◽  
Long Jie Zhang ◽  
Jian Cao
Keyword(s):  
Global Optimization ◽  
Path Planning ◽  
Polynomial Function ◽  
Cost Model ◽  
Flight Path ◽  
Planning Problem ◽  
Optimal Coefficients ◽  
Path Cost ◽  
Simulation Results ◽  
Path Planning Problem

Optimal flight path is restricted within Minimum Threat Surface (MTS). With Taylor’s expansion, the horizon projection of 3D flight path within MTS is fitted with a polynomial function with finite items. So the original path planning problem is simplified to search the optimals values in the coefficient space of the polynomial function. Flight path cost model of flight path is built and PSO is applied to search optimal coefficients. Flow of optimization is introduced and validated with three simulation cases. Simulation results show that MTS and PSO can effectively solve global optimization of 3D flight path.

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.

A Collision-Free Path Planning Method for an Articulated Mobile Robot in a Free Environment

2005 ◽  
Author(s):  
Patricia Quintero-Alvarez ◽  
Gabriel Ramirez ◽  
Sai¨d Zeghloul
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Free Path ◽  
Velocity Space ◽  
Control Law ◽  
Planning Problem ◽  
Optimization Approach ◽  
Reference Velocity ◽  
Free Environment ◽  
Path Planning Problem

In our previous work, we have treated the collision-free path-planning problem for a nonholonomic mobile robot in a cluttered environment. The method we have used is based on a representation of the obstacles in the robot’s velocity space, called Feasible Velocities Polygon (FVP). Every obstacle in the robot’s influence zone is represented by a linear constraint over the robot’s velocities such that it could not be collision between the robot and the obstacle. These constraints define a convex subset in the velocity space, the FVP. Every velocity vector of the FVP ensures a safe motion for the given obstacle configuration. The path-planning problem is solved by an optimization approach between the FVP and a reference velocity to reach the goal. In this paper, we have extended our work to an articulated mobile robot. This robot is composed of a differential mobile robot as tractor and a trailer, linked by off-center joints. We have modified the reference velocity in order to consider the constraints imposed by the trailer over the robot’s velocities. The control law is a nonlinear control law, which is asymptotically stable to the goal. We use the virtual robot concept, to solve the stability problem when the robot and its trailer move backwards. An articulated mobile robot is a strongly constrained system. Even in a free environment, under some circumstances, the robot may get blocked by its trailer in its progression towards the goal. To solve these situations, we have developed a heuristic algorithm. This algorithm is based in human experience: when a blocking situation is detected, a forward-backward maneuver is made, in order to increase the distance between the tows until a maximal value. After this maneuver, the robot takes the path to the original goal. Some numerical results show that our method leads the robot and the trailer to the final position in a stable way.

scholarly journals Mobile Robot Path Planning with a Non-Dominated Sorting Genetic Algorithm

2018 ◽  
Vol 8 (11) ◽  
pp. 2253 ◽  
Author(s):  
Yang Xue
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Path Planning ◽  
Optimization Problems ◽  
Planning Problem ◽  
Nsga Ii ◽  
Multi Objective ◽  
Mutation Operators ◽  
Comparison Results ◽  
Path Planning Problem

In many areas, such as mobile robots, video games and driverless vehicles, path planning has always attracted researchers’ attention. In the field of mobile robotics, the path planning problem is to plan one or more viable paths to the target location from the starting position within a given obstacle space. Evolutionary algorithms can effectively solve this problem. The non-dominated sorting genetic algorithm (NSGA-II) is currently recognized as one of the evolutionary algorithms with robust optimization capabilities and has solved various optimization problems. In this paper, NSGA-II is adopted to solve multi-objective path planning problems. Three objectives are introduced. Besides the usual selection, crossover and mutation operators, some practical operators are applied. Moreover, the parameters involved in the algorithm are studied. Additionally, another evolutionary algorithm and quality metrics are employed for examination. Comparison results demonstrate that non-dominated solutions obtained by the algorithm have good characteristics. Subsequently, the path corresponding to the knee point of non-dominated solutions is shown. The path is shorter, safer and smoother. This path can be adopted in the later decision-making process. Finally, the above research shows that the revised algorithm can effectively solve the multi-objective path planning problem in static environments.

The Study on Mobile Robot Path Planning Based on Frog Leaping Algorithm

2012 ◽  
Vol 490-495 ◽  
pp. 808-812
Author(s):  
Zheng Ran Zhang ◽  
Ji Ying Yin
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimization Problem ◽  
Optimal Path ◽  
Robot Path Planning ◽  
Unknown Environment ◽  
Simulation Results ◽  
Robot Path

We have proposed a method of robot path planning in a partially unknown environment in this paper. We regard the problem of robot path planning as an optimization problem and solve it with the SFL algorithm. The position of globally best frog in each iterative is selected, and reached by the robot in sequence. The obstacles are detected by the robot sensors are applied to update the information of the environment. The optimal path is generated until the robot reaches its target. The simulation results validate the feasibility of the proposed method.

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