Task Assignment and Scheduling for Mobile Robot Teams

2018 ◽  
Author(s):  
Shaurya Shriyam ◽  
Satyandra K. Gupta
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Completion Time ◽  
Heuristic Algorithms ◽  
Task Assignment ◽  
Task Execution ◽  
Computational Performance ◽  
Robot Teams ◽  
Or Task

Most complex missions comprise of spatially separated tasks which have to be finished using teams of mobile robots. The main challenges for planning such missions are forming effective coalitions among available robots and assigning them to tasks in such a way that the expected mission completion time is minimized. Our model allows task execution by a fraction of the assigned team even when the rest of the team has not yet arrived at the task location. We also allow tasks to be interrupted and robots of assigned teams to be rescheduled from an unfinished task to another task. We describe five different heuristic algorithms to compute schedules for all robots assigned to the mission. We compare them and analyze the computational performance of the best performing strategy. We also show how to handle uncertainty that may arise during traveling or task execution and then study the effect of varying uncertainty on the minimization of mission completion time.

scholarly journals Multi-UAV Cooperative Task Assignment Based on Half Random Q-Learning

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2417
Author(s):  
Pengxing Zhu ◽  
Xi Fang
Keyword(s):  
Heuristic Algorithms ◽  
Learning Algorithm ◽  
Task Assignment ◽  
Complex Environments ◽  
Task Execution ◽  
Q Learning ◽  
Assignment Model ◽  
Distribution Process ◽  
Aerial Vehicle ◽  
Assignment Scheme

Unmanned aerial vehicle (UAV) clusters usually face problems such as complex environments, heterogeneous combat subjects, and realistic interference factors in the course of mission assignment. In order to reduce resource consumption and improve the task execution rate, it is very important to develop a reasonable allocation plan for the tasks. Therefore, this paper constructs a heterogeneous UAV multitask assignment model based on several realistic constraints and proposes an improved half-random Q-learning (HR Q-learning) algorithm. The algorithm is based on the Q-learning algorithm under reinforcement learning, and by changing the way the Q-learning algorithm selects the next action in the process of random exploration, the probability of obtaining an invalid action in the random case is reduced, and the exploration efficiency is improved, thus increasing the possibility of obtaining a better assignment scheme, this also ensures symmetry and synergy in the distribution process of the drones. Simulation experiments show that compared with Q-learning algorithm and other heuristic algorithms, HR Q-learning algorithm can improve the performance of task execution, including the ability to improve the rationality of task assignment, increasing the value of gains by 12.12%, this is equivalent to an average of one drone per mission saved, and higher success rate of task execution. This improvement provides a meaningful attempt for UAV task assignment.

A COOPERATIVE MOBILE ROBOT TASK ASSIGNMENT AND COVERAGE PLANNING BASED ON CHAOS SYNCHRONIZATION

2010 ◽  
Vol 20 (01) ◽  
pp. 161-176 ◽  
Author(s):  
KIA FALLAHI ◽  
HENRY LEUNG
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Chaotic Motion ◽  
Chaos Synchronization ◽  
Initial Conditions ◽  
Task Assignment ◽  
Topological Transitivity ◽  
Robot Controller ◽  
Work Cooperative ◽  
Coverage Planning

In this paper, we propose a cooperative task assignment and coverage planning for mobile robots based on chaos synchronization. The chaotic mobile robot implies that the robot controller that drives a chaotic motion is characterized by topological transitivity and sensitive dependence on initial conditions. Due to the topological transitivity, the chaotic mobile robot is guaranteed to scan a workspace completely and the robot requires neither a map of the workspace nor a global motion plan. Chen and Lorenz systems are used to generate chaotic motion in this work. Cooperative multirobot systems can operate faster with higher efficiency and better reliability than a single robot system. By synchronizing the chaotic robot controllers, effective cooperation can be achieved. The performance of the cooperative chaotic mobile robots can be attributed to the use of deterministic dynamical systems and extended Kalman filter for chaos synchronization. Computer simulations illustrate the effectiveness of the proposed approach.

Ultrasonic sensor system for the uses in intelligent motion control system of a mobile robots group

2010 ◽  
Vol 7 ◽  
pp. 109-117
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov ◽  
B.S. Yudintsev
Keyword(s):  
Control System ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Motion Control ◽  
High Speed ◽  
Ultrasonic Sensor ◽  
Sensor System ◽  
Motion Control System ◽  
Speed Sensor ◽  
Working Space

The article deals with the development of a high-speed sensor system for a mobile robot, used in conjunction with an intelligent method of planning trajectories in conditions of high dynamism of the working space.

Scheduling Jobs with Truncated Exponential Sum-of-Logarithm-Processing-Times Based and Position-based Learning Effects

2015 ◽  
Vol 32 (04) ◽  
pp. 1550026 ◽  
Author(s):  
Yuan-Yuan Lu ◽  
Fei Teng ◽  
Zhi-Xin Feng
Keyword(s):  
Single Machine ◽  
Minimization Problem ◽  
Completion Time ◽  
Heuristic Algorithms ◽  
Exponential Sum ◽  
Total Weighted Completion Time ◽  
Learning Effects ◽  
Processing Times ◽  
Time Minimization ◽  
Weighted Completion Time

In this study, we consider a scheduling problem with truncated exponential sum-of-logarithm-processing-times based and position-based learning effects on a single machine. We prove that the shortest processing time (SPT) rule is optimal for the makespan minimization problem, the sum of the θth power of job completion times minimization problem, and the total lateness minimization problem, respectively. For the total weighted completion time minimization problem, the discounted total weighted completion time minimization problem, the maximum lateness minimization problem, we present heuristic algorithms (the worst-case bound of these heuristic algorithms are also given) according to the corresponding single machine scheduling problems without learning considerations. It also shows that the problems of minimizing the total tardiness, the total weighted completion time and the discounted total weighted completion time are polynomially solvable under some agreeable conditions on the problem parameters.

Vision for mobile robots

1992 ◽  
Vol 337 (1281) ◽  
pp. 341-350 ◽  
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Structure From Motion ◽  
Optic Flow ◽  
Image Sequences ◽  
Additional Constraint ◽  
Feature Points ◽  
Scene Structure ◽  
Corner Points ◽  
Edge Points

Localized feature points, particularly corners, can be computed rapidly and reliably in images, and they are stable over image sequences. Corner points provide more constraint than edge points, and this additional constraint can be propagated effectively from corners along edges. Implemented algorithms are described to compute optic flow and to determine scene structure for a mobile robot using stereo or structure from motion. It is argued that a mobile robot may not need to compute depth explicitly in order to navigate effectively.

scholarly journals Energy Modeling and Power Measurement for Mobile Robots

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 27 ◽  
Author(s):  
Linfei Hou ◽  
Liang Zhang ◽  
Jongwon Kim
Keyword(s):  
Control System ◽  
Energy Consumption ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Electrical Power ◽  
Energy Model ◽  
Energy Modeling ◽  
Power Measurement ◽  
Sensor System ◽  
System Control

To improve the energy efficiency of a mobile robot, a novel energy modeling method for mobile robots is proposed in this paper. The robot can calculate and predict energy consumption through the energy model, which provides a guide to facilitate energy-efficient strategies. The energy consumption of the mobile robot is first modeled by considering three major factors: the sensor system, control system, and motion system. The relationship between the three systems is elaborated by formulas. Then, the model is utilized and experimentally tested in a four-wheeled Mecanum mobile robot. Furthermore, the power measurement methods are discussed. The energy consumption of the sensor system and control system was at the milliwatt level, and a Monsoon power monitor was used to accurately measure the electrical power of the systems. The experimental results showed that the proposed energy model can be used to predict the energy consumption of the robot movement processes in addition to being able to efficiently support the analysis of the energy consumption characteristics of mobile robots.

A Task Based Approach to Mechatronic Systems Education

Volume 3 ◽  
2004 ◽  
Author(s):  
Kevin Firth ◽  
Brian Surgenor ◽  
Peter Wild
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Systems Engineering ◽  
Educational Tool ◽  
Electronic Components ◽  
Mechatronic Systems ◽  
Student Workload ◽  
Hands On ◽  
Hands On Learning ◽  
Elective Course

This paper describes an elective course in mechatronic systems engineering that is project based and team-oriented with hands-on learning. Working in small teams, students add electronic components to a mobile robot base and write the programs required to make the robot perform a series of tasks. Although the application of mobile robots as an educational tool in a mechatronics course is becoming the norm at many universities, the task based organization of the Queen’s mechatronics course is believed to have a number of novel features. The paper will review the pedagogy of the course, including aspects of the student workload, the interplay between teams, and the task based approach to marking and organization of the laboratories.

scholarly journals The PETLON Algorithm to Plan Efficiently for Task-Level-Optimal Navigation

2020 ◽  
Vol 69 ◽  
pp. 471-500
Author(s):  
Shih-Yun Lo ◽  
Shiqi Zhang ◽  
Peter Stone
Keyword(s):  
Mobile Robot ◽  
Motion Planning ◽  
Mobile Robots ◽  
Large Scale ◽  
Accurate Estimate ◽  
Indoor Environments ◽  
Task Planning ◽  
Trade Offs ◽  
High Level ◽  
Task Level

Intelligent mobile robots have recently become able to operate autonomously in large-scale indoor environments for extended periods of time. In this process, mobile robots need the capabilities of both task and motion planning. Task planning in such environments involves sequencing the robot’s high-level goals and subgoals, and typically requires reasoning about the locations of people, rooms, and objects in the environment, and their interactions to achieve a goal. One of the prerequisites for optimal task planning that is often overlooked is having an accurate estimate of the actual distance (or time) a robot needs to navigate from one location to another. State-of-the-art motion planning algorithms, though often computationally complex, are designed exactly for this purpose of finding routes through constrained spaces. In this article, we focus on integrating task and motion planning (TMP) to achieve task-level-optimal planning for robot navigation while maintaining manageable computational efficiency. To this end, we introduce TMP algorithm PETLON (Planning Efficiently for Task-Level-Optimal Navigation), including two configurations with different trade-offs over computational expenses between task and motion planning, for everyday service tasks using a mobile robot. Experiments have been conducted both in simulation and on a mobile robot using object delivery tasks in an indoor office environment. The key observation from the results is that PETLON is more efficient than a baseline approach that pre-computes motion costs of all possible navigation actions, while still producing plans that are optimal at the task level. We provide results with two different task planning paradigms in the implementation of PETLON, and offer TMP practitioners guidelines for the selection of task planners from an engineering perspective.

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