scholarly journals A Novel Double Layered Hybrid Multi-Robot Framework for Guidance and Navigation of Unmanned Surface Vehicles in a Practical Maritime Environment

2020 ◽  
Vol 8 (9) ◽  
pp. 624 ◽  
Author(s):  
Yogang Singh ◽  
Marco Bibuli ◽  
Enrica Zereik ◽  
Sanjay Sharma ◽  
Asiya Khan ◽  
...  
Keyword(s):  
Motion Planning ◽  
Formation Control ◽  
Optimal Path ◽  
Unmanned Surface Vehicles ◽  
Research Areas ◽  
Online Planning ◽  
Cooperative Motion ◽  
Aggregation Technique ◽  
Global And Local ◽  
Multi Robot

Formation control and cooperative motion planning are two major research areas currently being used in multi robot motion planning and coordination. The current study proposes a hybrid framework for guidance and navigation of swarm of unmanned surface vehicles (USVs) by combining the key characteristics of formation control and cooperative motion planning. In this framework, two layers of offline planning and online planning are integrated and applied on a practical marine environment. In offline planning, an optimal path is generated from a constrained A* path planning approach, which is later smoothed using a spline. This optimal trajectory is fed as an input for the online planning where virtual target (VT) based multi-agent guidance framework is used to navigate the swarm of USVs. This VT approach combined with a potential theory based swarm aggregation technique provides a robust methodology of global and local collision avoidance based on known positions of the USVs. The combined approach is evaluated with the different number of USVs to understand the effectiveness of the approach from the perspective of practicality, safety and robustness.

scholarly journals Reinforcement-Learning-Based Asynchronous Formation Control Scheme for Multiple Unmanned Surface Vehicles

2021 ◽  
Vol 11 (2) ◽  
pp. 546
Author(s):  
Jiajia Xie ◽  
Rui Zhou ◽  
Yuan Liu ◽  
Jun Luo ◽  
Shaorong Xie ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Formation Control ◽  
Rapid Development ◽  
Gradient Algorithm ◽  
Robot System ◽  
Physical Relationship ◽  
Unmanned Surface Vehicles ◽  
Main Challenge ◽  
Control Scheme ◽  
Multi Robot

The high performance and efficiency of multiple unmanned surface vehicles (multi-USV) promote the further civilian and military applications of coordinated USV. As the basis of multiple USVs’ cooperative work, considerable attention has been spent on developing the decentralized formation control of the USV swarm. Formation control of multiple USV belongs to the geometric problems of a multi-robot system. The main challenge is the way to generate and maintain the formation of a multi-robot system. The rapid development of reinforcement learning provides us with a new solution to deal with these problems. In this paper, we introduce a decentralized structure of the multi-USV system and employ reinforcement learning to deal with the formation control of a multi-USV system in a leader–follower topology. Therefore, we propose an asynchronous decentralized formation control scheme based on reinforcement learning for multiple USVs. First, a simplified USV model is established. Simultaneously, the formation shape model is built to provide formation parameters and to describe the physical relationship between USVs. Second, the advantage deep deterministic policy gradient algorithm (ADDPG) is proposed. Third, formation generation policies and formation maintenance policies based on the ADDPG are proposed to form and maintain the given geometry structure of the team of USVs during movement. Moreover, three new reward functions are designed and utilized to promote policy learning. Finally, various experiments are conducted to validate the performance of the proposed formation control scheme. Simulation results and contrast experiments demonstrate the efficiency and stability of the formation control scheme.

scholarly journals A Two Layered Optimal Approach towards Cooperative Motion Planning of Unmanned Surface Vehicles in a Constrained Maritime Environment

2018 ◽  
Vol 51 (29) ◽  
pp. 378-383 ◽  
Author(s):  
Marco Bibuli ◽  
Yogang Singh ◽  
Sanjay Sharma ◽  
Robert Sutton ◽  
Daniel Hatton ◽  
...  
Keyword(s):  
Motion Planning ◽  
Unmanned Surface Vehicles ◽  
Cooperative Motion ◽  
Optimal Approach ◽  
Maritime Environment

scholarly journals Research on Motion Planning Based on Flocking Control and Reinforcement Learning for Multi-Robot Systems

Machines ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 77
Author(s):  
Minghui Wang ◽  
Bi Zeng ◽  
Qiujie Wang
Keyword(s):  
Reinforcement Learning ◽  
Motion Planning ◽  
Obstacle Avoidance ◽  
Formation Control ◽  
Learning Strategy ◽  
Planning Method ◽  
Unknown Environment ◽  
Robot Systems ◽  
Avoidance Control ◽  
Multi Robot

Robots have poor adaptive ability in terms of formation control and obstacle avoidance control in unknown complex environments. To address this problem, in this paper, we propose a new motion planning method based on flocking control and reinforcement learning. It uses flocking control to implement a multi-robot orderly motion. To avoid the trap of potential fields faced during flocking control, the flocking control is optimized, and the strategy of wall-following behavior control is designed. In this paper, reinforcement learning is adopted to implement the robotic behavioral decision and to enhance the analytical and predictive abilities of the robot during motion planning in an unknown environment. A visual simulation platform is developed in this paper, on which researchers can test algorithms for multi-robot motion control, such as obstacle avoidance control, formation control, path planning and reinforcement learning strategy. As shown by the simulation experiments, the motion planning method presented in this paper can enhance the abilities of multi-robot systems to self-learn and self-adapt under a fully unknown environment with complex obstacles.

scholarly journals A survey of formation control and motion planning of multiple unmanned vehicles

Robotica ◽  
2018 ◽  
Vol 36 (7) ◽  
pp. 1019-1047 ◽  
Author(s):  
Yuanchang Liu ◽  
Richard Bucknall
Keyword(s):  
Motion Planning ◽  
Collision Avoidance ◽  
Formation Control ◽  
Unmanned Vehicles ◽  
Future Research ◽  
Research Areas ◽  
Open Research ◽  
Vehicle Systems ◽  
Avoidance Problem ◽  
Large Research

SUMMARYThe increasing deployment of multiple unmanned vehicles systems has generated large research interest in recent decades. This paper therefore provides a detailed survey to review a range of techniques related to the operation of multi-vehicle systems in different environmental domains, including land based, aerospace and marine with the specific focuses placed on formation control and cooperative motion planning. Differing from other related papers, this paper pays a special attention to the collision avoidance problem and specifically discusses and reviews those methods that adopt flexible formation shape to achieve collision avoidance for multi-vehicle systems. In the conclusions, some open research areas with suggested technologies have been proposed to facilitate the future research development.

scholarly journals Trust-Based Multi-Robot Symbolic Motion Planning with a Human-in-the-Loop

2018 ◽  
Vol 8 (4) ◽  
pp. 1-33 ◽  
Author(s):  
Yue Wang ◽  
Laura R. Humphrey ◽  
Zhanrui Liao ◽  
Huanfei Zheng
Keyword(s):  
Motion Planning ◽  
Human In The Loop ◽  
Multi Robot

scholarly journals Learning Interaction-Aware Trajectory Predictions for Decentralized Multi-Robot Motion Planning in Dynamic Environments

2021 ◽  
Vol 6 (2) ◽  
pp. 2256-2263
Author(s):  
Hai Zhu ◽  
Francisco Martinez Claramunt ◽  
Bruno Brito ◽  
Javier Alonso-Mora
Keyword(s):  
Motion Planning ◽  
Dynamic Environments ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Multi Robot

scholarly journals Research Progress on Synergistic Technologies of Agricultural Multi-Robots

2021 ◽  
Vol 11 (4) ◽  
pp. 1448
Author(s):  
Wenju Mao ◽  
Zhijie Liu ◽  
Heng Liu ◽  
Fuzeng Yang ◽  
Meirong Wang
Keyword(s):  
Path Planning ◽  
Formation Control ◽  
Research Progress ◽  
Hybrid Architecture ◽  
Labor Costs ◽  
Robot System ◽  
System Architectures ◽  
Research Results ◽  
Robot Systems ◽  
Multi Robot

Multi-robots have shown good application prospects in agricultural production. Studying the synergistic technologies of agricultural multi-robots can not only improve the efficiency of the overall robot system and meet the needs of precision farming but also solve the problems of decreasing effective labor supply and increasing labor costs in agriculture. Therefore, starting from the point of view of an agricultural multiple robot system architectures, this paper reviews the representative research results of five synergistic technologies of agricultural multi-robots in recent years, namely, environment perception, task allocation, path planning, formation control, and communication, and summarizes the technological progress and development characteristics of these five technologies. Finally, because of these development characteristics, it is shown that the trends and research focus for agricultural multi-robots are to optimize the existing technologies and apply them to a variety of agricultural multi-robots, such as building a hybrid architecture of multi-robot systems, SLAM (simultaneous localization and mapping), cooperation learning of robots, hybrid path planning and formation reconstruction. While synergistic technologies of agricultural multi-robots are extremely challenging in production, in combination with previous research results for real agricultural multi-robots and social development demand, we conclude that it is realistic to expect automated multi-robot systems in the future.

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