scholarly journals Adaptive Virtual Impedance Control of a Mobile Multi-Robot System

Robotics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 19
Author(s):  
Duanne Engelbrecht ◽  
Nico Steyn ◽  
Karim Djouani
Keyword(s):  
Impedance Control ◽  
Lower Layer ◽  
Dynamic Environment ◽  
Robot System ◽  
Collaborative Robots ◽  
Dynamic Obstacle Avoidance ◽  
Movement Stability ◽  
Dynamic Obstacle ◽  
Virtual Impedance ◽  
Multi Robot

The capabilities of collaborative robotics have transcended the conventional abilities of decentralised robots as it provides benefits such as scalability, flexibility and robustness. Collaborative robots can operate safely in complex human environments without being restricted by the safety cages or barriers that often accompany them. Collaborative robots can be used for various applications such as machine tending, packaging, process tasks and pick and place. This paper proposes an improvement of the current virtual impedance algorithm by developing an adaptive virtual impedance controlled mobile multi-robot system focused on dynamic obstacle avoidance with a controlled planar movement. The study includes the development of a mobile multi-robot platform whereby each robot plans a path individually without a supervisor. The proposed system would implement a two-layered hierarchy for robot path planning. The higher layer generates a trajectory from the current position to the desired position, and the lower layer develops a real-time strategy to follow the generated trajectory while avoiding static and dynamic obstacles. The key contribution of this paper is the adaptive virtual impedance controller for a multi-robot system that will maintain movement stability and improve the motion behaviour in a dynamic environment.

Recent Advances in Robot Trajectory Planning in a Dynamic Environment

2021 ◽  
Vol 14 ◽  
Author(s):  
Hongxin Zhang ◽  
Rongzijun Shu ◽  
Guangsen Li
Keyword(s):  
Trajectory Planning ◽  
Dynamic Environment ◽  
Local Planning ◽  
Robot Trajectory ◽  
Recent Advances ◽  
Dynamic Obstacle Avoidance ◽  
Dynamic Obstacle ◽  
Task Collaboration ◽  
Global Planning ◽  
Robot Trajectory Planning

Background: Trajectory planning is important to research in robotics. As the application environment changes rapidly, robot trajectory planning in a static environment can no longer meet actual needs. Therefore, a lot of research has turned to robot trajectory planning in a dynamic environment. Objective: This paper aims at providing references for researchers from related fields by reviewing recent advances in robot trajectory planning in a dynamic environment. Methods: This paper reviews the latest patents and current representative articles related to robot trajectory planning in a dynamic environment and introduces some key methods of references from the aspects of algorithm, innovation and principle. Results: In this paper, we classified the researches related to robot trajectory planning in a dynamic environment in the last 10 years, introduced and analyzed the advantages of different algorithms in these patents and articles, and the future developments and potential problems in this field are discussed. Conclusion: Trajectory planning in a dynamic environment can help robots to accomplish tasks in a complex environment, improving robots’ intelligence, work efficiency and adaptability to the environment. Current research focuses on dynamic obstacle avoidance, parameter optimization, real-time planning, and efficient work, which can be used to solve robot trajectory planning in a dynamic environment. In terms of the combination of multiple algorithms, multi-sensor information fusion, the combination of local planning and global planning, and multi-robot and multi-task collaboration, more improvements and innovations are needed. It should create more patents on robot trajectory planning in a dynamic environment.

scholarly journals Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment Using Fuzzy Logic and Online Learning

2021 ◽  
Vol 50 (2) ◽  
pp. 357-374
Author(s):  
Novak Zagradjanin ◽  
Aleksandar Rodic ◽  
Dragan Pamucar ◽  
Bojan Pavkovic
Keyword(s):  
Path Planning ◽  
High Efficiency ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Dynamic Environment ◽  
Cloud Services ◽  
Robot System ◽  
Inference System ◽  
Crowded Environment ◽  
Multi Robot

This paper considers an autonomous cloud-based multi-robot system designed to execute highly repetitive tasksin a dynamic environment such as a modern megastore. Cloud level is intended for performing the most demandingoperations in order to unload the robots that are users of cloud services in this architecture. For path planningon global level D* Lite algorithm is applied, bearing in mind its high efficiency in dynamic environments. In orderto introduce smart cost map for further improvement of path planning in complex and crowded environment, implementationof fuzzy inference system and learning algorithm is proposed. The results indicate the possibility ofapplying a similar concept in different real-world robotics applications, in order to reduce the total paths length,as well as to minimize the risk in path planning related to the human-robot interactions.

Multi-Robot Cooperative Transportation of Objects Using Modified Q-Learning

2010 ◽  
Author(s):  
Pallege Gamini Dilupa Siriwardana ◽  
Clarence de Silva
Keyword(s):  
Autonomous Robots ◽  
Dynamic Environment ◽  
Learning Approach ◽  
Robot System ◽  
Q Learning ◽  
Time Performance ◽  
Object Pose Estimation ◽  
Object Transportation ◽  
Blob Tracking ◽  
Multi Robot

In cooperative multi-robot object transportation, several autonomous robots navigate cooperatively in either a static or a dynamic environment to transport an object to a goal location and orientation. The environment may consist of both fixed and removable obstacles and it will be subject to uncertainty and unforeseen changes within the environment. More than one robot may be required for handling heavy and large objects. This paper presents a modified Q-learning approach for object transportation utilizing cooperative and autonomous multiple mobile robots. A modified version of Q-learning is presented, which employs for effective robot coordination. A solution to the action selection conflicts of the robots is presented, which helps to improve the real time performance and robustness of the system. As required in the task, the paper presents an algorithm for object pose estimation, by utilizing the laser range finder and color blob tracking. The developed techniques are implemented in a multi-robot system in laboratory. Experimental results are presented to demonstrate the effectiveness of the developed multi-robot system and its underlying methodologies.

scholarly journals Mobile Robot Path Planning Based on Time Taboo Ant Colony Optimization in Dynamic Environment

2021 ◽  
Vol 15 ◽  
Author(s):  
Ni Xiong ◽  
Xinzhi Zhou ◽  
Xiuqing Yang ◽  
Yong Xiang ◽  
Junyong Ma
Keyword(s):  
Path Planning ◽  
Ant Colony Optimization ◽  
Dynamic Environment ◽  
Global Search ◽  
Convergence Speed ◽  
Ant Colony ◽  
Time Varying ◽  
Ant Colony Optimization Algorithm ◽  
Dynamic Obstacle Avoidance ◽  
Dynamic Obstacle

This article aims to improve the problem of slow convergence speed, poor global search ability, and unknown time-varying dynamic obstacles in the path planning of ant colony optimization in dynamic environment. An improved ant colony optimization algorithm using time taboo strategy is proposed, namely, time taboo ant colony optimization (TTACO), which uses adaptive initial pheromone distribution, rollback strategy, and pheromone preferential limited update to improve the algorithm's convergence speed and global search ability. For the poor global search ability of the algorithm and the unknown time-varying problem of dynamic obstacles in a dynamic environment, a time taboo strategy is first proposed, based on which a three-step arbitration method is put forward to improve its weakness in global search. For the unknown time-varying dynamic obstacles, an occupancy grid prediction model is proposed based on the time taboo strategy to solve the problem of dynamic obstacle avoidance. In order to improve the algorithm's calculation speed when avoiding obstacles, an ant colony information inheritance mechanism is established. Finally, the algorithm is used to conduct dynamic simulation experiments in a simulated factory environment and is compared with other similar algorithms. The experimental results show that the TTACO can obtain a better path and accelerate the convergence speed of the algorithm in a static environment and can successfully avoid dynamic obstacles in a dynamic environment.

scholarly journals Dynamic Obstacle Avoidance Algorithm Based on DYNAMIC Environment MR

2022 ◽  
Vol 2146 (1) ◽  
pp. 012030
Author(s):  
Juan Guo
Keyword(s):  
Information Systems ◽  
Computer Technology ◽  
Rapid Development ◽  
Dynamic Environment ◽  
Database Systems ◽  
Huge Amount ◽  
Key Technology ◽  
Dynamic Obstacle Avoidance ◽  
Dynamic Obstacle ◽  
Important System

Abstract With the rapid development of computer technology, the importance of database systems as an indispensable part of information systems is becoming more and more prominent. And nowadays, the society has been increasingly using modern means to program databases. Database is a large and complex, huge amount of data and has a certain structure and independence of the important system, its programming requires certain technical means, the author will be in the text for the database programming involved in the key technology to explain.

scholarly journals Dynamic Obstacle Avoidance for an Omnidirectional Mobile Robot

2010 ◽  
Vol 2010 ◽  
pp. 1-14 ◽  
Author(s):  
Robert L. Williams ◽  
Jianhua Wu
Keyword(s):  
Obstacle Avoidance ◽  
Experimental Validation ◽  
Vision System ◽  
Dynamic Environment ◽  
Ohio University ◽  
Omnidirectional Mobile Robot ◽  
Dynamic Obstacle Avoidance ◽  
Novel Method ◽  
Dynamic Obstacle ◽  
Moving Obstacle

We have established a novel method of obstacle-avoidance motion planning for mobile robots in dynamic environments, wherein the obstacles are moving with general velocities and accelerations, and their motion profiles are not preknown. A hybrid system is presented in which a global deliberate approach is applied to determine the motion in the desired path line (DPL), and a local reactive approach is used for moving obstacle avoidance. A machine vision system is required to sense obstacle motion. Through theoretical analysis, simulation, and experimental validation applied to the Ohio University RoboCup robot, we show the method is effective to avoid collisions with moving obstacles in a dynamic environment.

Sign in / Sign up

Export Citation Format

Share Document