GPS-INS state estimation for multi-robot systems with computational resource constraints

2009 ◽  
Author(s):  
Luke M. Wachter ◽  
Laura E. Ray
Keyword(s):  
State Estimation ◽  
Resource Constraints ◽  
Computational Resource ◽  
Robot Systems ◽  
Multi Robot

scholarly journals A Constraint-based Mission Planning Approach for Reconfigurable Multi-Robot Systems

2018 ◽  
Vol 21 (62) ◽  
pp. 25
Author(s):  
Thomas M Roehr
Keyword(s):  
Degrees Of Freedom ◽  
Resource Constraints ◽  
Mission Planning ◽  
General Nature ◽  
Planning Problem ◽  
Modular Systems ◽  
Planning Approach ◽  
Robot Systems ◽  
Revised Definitions ◽  
Multi Robot

The application of reconfigurable multi-robot systems introduces additional degrees of freedom to design robotic missions compared to classical multi-robot systems. To allow for autonomous operation of such systems, planning approaches have to be investigated that cannot only cope with the combinatorial challenge arising from the increased flexibility of modular systems, but also exploit this flexibility to improve for example the safety of operation. While the problem originates from the domain of robotics it is of general nature and significantly intersects with operations research. This paper suggests a constraint-based mission planning approach, and presents a set of revised definitions for reconfigurable multi-robot systems including the representation of the planning problem using spatially and temporally qualified resource constraints. Planning is performed using a multi-stage approach and a combined use of knowledge-based reasoning, constraint-based programming and integer linear programming. The paper concludes with the illustration of the solution of a planned example mission.

scholarly journals Cooperative Localization Approach for Multi-Robot Systems Based on State Estimation Error Compensation

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3842 ◽  
Author(s):  
Zhang ◽  
Cao
Keyword(s):  
State Estimation ◽  
Error Compensation ◽  
Estimation Error ◽  
The State ◽  
Cooperative Localization ◽  
Communication Delays ◽  
Extended Kalman Filtering ◽  
Robot Systems ◽  
Localization Approach ◽  
Multi Robot

In order to improve the localization accuracy of multi-robot systems, a cooperative localization approach with communication delays was proposed in this paper. In the proposed method, the reason for the time delay of the robots’ cooperative localization approach was analyzed first, and then the state equation and measure equation were reconstructed by introducing the communication delays into the states and measurements. Furthermore, the cooperative localization algorithm using the extended Kalman filtering technique based on state estimation error compensation was proposed to reduce the state estimation error of delay filtering. Finally, the simulation and experiment results demonstrated that the proposed algorithm can achieve good performance in location in the presence of communication delay while having reduced computational and communicative cost.

REAL-TIME SCHEDULING OF DISTRIBUTED MULTI-ROBOT MANIPULATOR SYSTEMS

2005 ◽  
Vol 29 (2) ◽  
pp. 179-194
Author(s):  
P. Yuan ◽  
M. Moallem ◽  
R.V. Patel
Keyword(s):  
Execution Time ◽  
Cooperative Control ◽  
Resource Constraints ◽  
Robot Manipulator ◽  
Scheduling Algorithm ◽  
Worst Case ◽  
Real Time Scheduling ◽  
Robot Systems ◽  
Time Scheduling ◽  
Multi Robot

This paper presents an online task-oriented scheduling method and an off-line scheduling algorithm that can be used for cooperative control of a distributed multi-robot manipulator system. Satisfaction of temporal deadlines and tasks-relative constraints are considered in this work. With the proposed algorithms, both the timing constraints and relative task dependencies can be satisfied when the worst-case execution time is unknown. The total execution time of the assembly tasks can be significantly improved compared with other known scheduling algorithms such as the First-In-First-Out and Round Robin scheduling methods. Experimental results are presented indicating that the proposed algorithm can be used for improving the performance of multi-robot systems in terms of timing and resource constraints.

scholarly journals Resilient Task Allocation in Heterogeneous Multi-Robot Systems

2021 ◽  
Vol 6 (2) ◽  
pp. 1327-1334
Author(s):  
Siddharth Mayya ◽  
Diego S. D'antonio ◽  
David Saldana ◽  
Vijay Kumar
Keyword(s):  
Task Allocation ◽  
Robot Systems ◽  
Multi Robot

scholarly journals Exploring Robot Connectivity and Collaborative Sensing in a High-School Enrichment Program

Robotics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Igor M. Verner ◽  
Dan Cuperman ◽  
Michael Reitman
Keyword(s):  
High School ◽  
Positive Contribution ◽  
Smart Transportation ◽  
Sensory Data ◽  
Robot Systems ◽  
Enrichment Program ◽  
Collaborative Sensing ◽  
Robot Tasks ◽  
Value Of Learning ◽  
Multi Robot

Education is facing challenges to keep pace with the widespread introduction of robots and digital technologies in industry and everyday life. These challenges necessitate new approaches to impart students at all levels of education with the knowledge of smart connected robot systems. This paper presents the high-school enrichment program Intelligent Robotics and Smart Transportation, which implements an approach to teaching the concepts and skills of robot connectivity, collaborative sensing, and artificial intelligence, through practice with multi-robot systems. The students used a simple control language to program Bioloid wheeled robots and utilized Phyton and Robot Operating System (ROS) to program Tello drones and TurtleBots in a Linux environment. In their projects, the students implemented multi-robot tasks in which the robots exchanged sensory data via the internet. Our educational study evaluated the contribution of the program to students’ learning of connectivity and collaborative sensing of robot systems and their interest in modern robotics. The students’ responses indicated that the program had a high positive contribution to their knowledge and skills and fostered their interest in the learned subjects. The study revealed the value of learning of internet of things and collaborative sensing for enhancing this contribution.

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