Generalized task allocation and route planning for robots with multiple depots in indoor building environments

Capacitated field operations involve input/output material flows where there are capacity constraints in the form of a specific load that a vehicle can carry. As such, a specific normal-sized field cannot be covered in one single operation using only one load, and the vehicle needs to get serviced (i.e., refilling) from out-of-field facilities (depot). Although several algorithms have been developed to solve the routing problem of capacitated operations, these algorithms only considered one depot. The general goal of this paper is to develop a route planning tool for agricultural machines with multiple depots. The tool presented consists of two modules: the first one regards the field geometrical representation in which the field is partitioned into tracks and headland passes; the second one regards route optimization that is implemented by the metaheuristic simulated annealing (SA) algorithm. In order to validate the developed tool, a comparison between a well-known route planning approach, namely B-pattern, and the algorithm presented in this study was carried out. The results show that the proposed algorithm outperforms the B-pattern by up to 20.0% in terms of traveled nonworking distance. The applicability of the tool developed was tested in a case study with seven scenarios differing in terms of locations and number of depots. The results of this study illustrated that the location and number of depots significantly affect the total nonworking traversal distance during a field operation.

Download Full-text

Fleet size optimization and collaborative route planning for multi-vehicle task allocation

AIAA Scitech 2019 Forum ◽

10.2514/6.2019-1572 ◽

2019 ◽

Cited By ~ 2

Author(s):

Sukmin Yoon ◽

Jinwhan Kim

Keyword(s):

Task Allocation ◽

Route Planning ◽

Size Optimization ◽

Fleet Size

Download Full-text

Route Planning Integrated Multi Objective Task Allocation for Reconfigurable Robot Teams Using Genetic Algorithm

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2018.7137 ◽

2018 ◽

Vol 15 (2) ◽

pp. 627-636 ◽

Cited By ~ 1

Author(s):

K. Padmanabhan Panchu ◽

M. Rajmohan ◽

M. R. Sumalatha ◽

R. Baskaran

Keyword(s):

Genetic Algorithm ◽

Task Allocation ◽

Completion Time ◽

Route Planning ◽

Task Completion ◽

Task Completion Time ◽

Robot Teams ◽

Reconfigurable Robot ◽

Robot Tasks ◽

Multi Robot

This research work aims at multi objective optimization of integrated route planning and multi-robot task allocation for reconfigurable robot teams. Genetic Algorithm based methodology is used to minimize the overall task completion time for all the multi-robot tasks and to minimize the cumulative running time of all the robots. A modified matrix based chromosome is used to accommodate the robot information and task information for route planning integrated task allocation. The experimental validation is done with 3 robots and 4 tasks. For larger number of robots and tasks were simulated to perform route planning for maximum of 20 robots that would attend the maximum of 40 different multi-robot tasks. The results shows that the average task completion time per robot and average travel time per robot, decreases exponentially with increase in number of robots for fixed number of tasks. This method finds its application in allocating a robot teams to tasks and finding the best sequence for robots that work in coordination for material handling in hospital management, warehouse operations, military operations, cleaning tasks etc.

Download Full-text

Task allocation and route planning of multiple UAVs in a marine environment based on an improved particle swarm optimization algorithm

EURASIP Journal on Advances in Signal Processing ◽

10.1186/s13634-021-00804-9 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Ming Yan ◽

Huimin Yuan ◽

Jie Xu ◽

Ying Yu ◽

Libiao Jin

Keyword(s):

Particle Swarm Optimization ◽

Marine Environment ◽

Task Allocation ◽

Particle Swarm ◽

Route Planning ◽

Pso Algorithm ◽

Offshore Platforms ◽

Swarm Optimization ◽

Improved Particle Swarm Optimization ◽

Multiple Uavs

AbstractUnmanned aerial vehicles (UAVs) are considered a promising example of an automatic emergency task in a dynamic marine environment. However, the maritime communication performance between UAVs and offshore platforms has become a severe challenge. Due to the complex marine environment, the task allocation and route planning efficiency of multiple UAVs in an intelligent ocean are not satisfactory. To address these challenges, this paper proposes an intelligent marine task allocation and route planning scheme for multiple UAVs based on improved particle swarm optimization combined with a genetic algorithm (GA-PSO). Based on the simulation of an intelligent marine control system, the traditional particle swarm optimization (PSO) algorithm is improved by introducing partial matching crossover and secondary transposition mutation. The improved GA-PSO is used to solve the random task allocation problem of multiple UAVs and the two-dimensional route planning of a single UAV. The simulation results show that compared with the traditional scheme, the proposed scheme can significantly improve the task allocation efficiency, and the navigation path planned by the proposed scheme is also optimal.

Download Full-text