Geometric Region-Based Swarm Robotics Path Planning in an Unknown Occluded Environment

被引:15
|
作者
Roy, Dibyendu [1 ]
Chowdhury, Arijit [2 ]
Maitra, Madhubanti [3 ]
Bhattacharya, Samar [4 ]
机构
[1] Tata Consultancy Serv, TCS Res & Innovat Lab, Res & Innovat, Kolkata 700051, India
[2] Tata Consultancy Serv, Res & Innovat, Kolkata 700051, India
[3] Jadavpur Univ, Elect Engn Dept, Control Syst Sect, Kolkata 700032, India
[4] Jadavpur Univ, Dept Elect Engn, Kolkata 700032, India
关键词
Navigation; Robot kinematics; Shape control; Shape; Convergence; Robot sensing systems; Autonomous system; fault tolerance; obstacle avoidance; shape control; swarm robotics; MULTIAGENT SYSTEMS; SHAPE CONTROL;
D O I
10.1109/TIE.2020.2996158
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This article presents a geometrical region-based shape control methodology for navigating a cohesive swarm-robotic structure toward the goal even in a field occluded by unknown obstacles. In this control approach, initially, the robotic swarm is conceived to lie within a well-defined virtual circular region thus preserving a strict interagent cohesiveness among them. However, during the progression, for evading severely constricted obstacles, the virtual circle has been allowed to change its shape and in the process, varied elliptical shapes are made to evolve. In essence, for a collision-free solution, this shrinking aspect (from circle to ellipse) depends entirely on the number of agents in the swarm and at the same time also reliance on the sensed distance between two nearest obstacles through which the shrunken circle or the virtual ellipse will be able to pass. Consequently, shape switching is a dynamic as well as a stochastic process throughout the journey of the swarm. For achieving these objectives, a two-level hierarchical control strategy has been employed. Moreover, during aggregating toward the target, the actuation failure of any agent or agents may occur. In this perspective, the proposed control law has been updated adaptively throughout the route such that agent failure does not encumber the mission. Finally, the extensive simulation results along with the hardware experimentation are provided to demonstrate the efficacy of the proposed scheme.
引用
收藏
页码:6053 / 6063
页数:11
相关论文
共 50 条
  • [21] A Complete Coverage Path Planning Approach for an Autonomous Underwater Helicopter in Unknown Environment Based on VFH+ Algorithm
    Ma, Congcong
    Zou, Hongyu
    An, Xinyu
    JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2024, 12 (03)
  • [22] Online path planning of mobile robot using grasshopper algorithm in a dynamic and unknown environment
    Elmi, Zahra
    Efe, Mehmet Onder
    JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE, 2021, 33 (03) : 467 - 485
  • [23] Path Planning for a Formation of Autonomous Robots in an Unknown Environment Using Artificial Force Fields
    Anh Duc Dang
    Horn, Joachim
    2014 18TH INTERNATIONAL CONFERENCE SYSTEM THEORY, CONTROL AND COMPUTING (ICSTCC), 2014, : 773 - 778
  • [24] Obstacle Avoidance for Multi-agent Path Planning Based on Vectorized Particle Swarm Optimization
    Biswas, Sumana
    Anavatti, Sreenatha G.
    Garratt, Matthew A.
    INTELLIGENT AND EVOLUTIONARY SYSTEMS, IES 2016, 2017, 8 : 61 - 74
  • [25] Robot path planning in globally unknown environments based on rolling windows
    Zhang, CG
    Xi, YG
    SCIENCE IN CHINA SERIES E-TECHNOLOGICAL SCIENCES, 2001, 44 (02): : 131 - 139
  • [26] Multi-robot Path Planning Based on Spatio-Temporal Information in Large-scale Unknown Environment
    Ding, Junfeng
    Zhang, Lin
    Cheng, Jiyu
    2021 27TH INTERNATIONAL CONFERENCE ON MECHATRONICS AND MACHINE VISION IN PRACTICE (M2VIP), 2021,
  • [27] Navigation of Multiple Robots in Formative Manner in an Unknown Environment using Artificial Potential Field Based Path Planning Algorithm
    Das, Madhu Sudan
    Sanyal, Sourish
    Mandal, Sanjoy
    AIN SHAMS ENGINEERING JOURNAL, 2022, 13 (05)
  • [28] Robot path planning in globally unknown environments based on rolling windows
    张纯刚
    席裕庚
    Science in China(Series E:Technological Sciences), 2001, (02) : 131 - 139
  • [29] Robot path planning in globally unknown environments based on rolling windows
    Zhang Chungang
    Xi Yugeng
    Science in China Series E: Technolgical Science, 2001, 44 (2): : 131 - 139
  • [30] Robot Path Planning Based on Generative Learning Particle Swarm Optimization
    Wang, Lu
    Liu, Lulu
    Lu, Xiaoxia
    IEEE ACCESS, 2024, 12 : 130063 - 130072