Multi-Robot Mission Planning with Static Energy Replenishment

被引:18
|
作者
Li, Bingxi [1 ]
Moridian, Barzin [1 ]
Kamal, Anurag [1 ]
Patankar, Sharvil [1 ]
Mahmoudian, Nina [1 ]
机构
[1] Michigan Technol Univ, Dept Mech Engn Engn Mech, 1400 Townsend Dr, Houghton, MI 49931 USA
关键词
Mission planning; Energy replenishment; Static charging station; Multi-robot exploration; Area coverage; GENETIC ALGORITHM; COVERAGE; EFFICIENT;
D O I
10.1007/s10846-018-0897-2
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The success of numerous long-term robotic explorations in the air, on the ground, and under the water is dependent on the ability of robots to operate for an extended time. The long-term ubiquitous operation of robots hinges on smart energy consumption and the replenishment of the robots. This paper provides a heuristic method for planning missions that extend over multiple battery lives of working robots. This method simultaneously generates energy efficient trajectories for multiple robots, and schedules energy cycling using static charging stations through the mission. The mission planning algorithm accounts for environmental obstacles, current, and can adapt to a priority search distribution. The simulation results for a scenario similar to the MH370 airplane search mission demonstrate the effectiveness of the developed algorithm in area coverage and handling environmental constraints. The robustness of the developed method is evaluated through a Monte Carlo simulation. In addition, the proposed algorithm is tested in simulation environment in Gazebo and implemented and experimentally validated for an in-lab aerial coverage scenario with an obstacle and a priority mission area.
引用
收藏
页码:745 / 759
页数:15
相关论文
共 50 条
  • [1] Multi-Robot Mission Planning with Static Energy Replenishment
    Bingxi Li
    Barzin Moridian
    Anurag Kamal
    Sharvil Patankar
    Nina Mahmoudian
    Journal of Intelligent & Robotic Systems, 2019, 95 : 745 - 759
  • [2] Multi-Robot Mission Planning in Dynamic Semantic Environments
    Kalluraya, Samarth
    Pappas, George J.
    Kantaros, Yiannis
    2023 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, ICRA, 2023, : 1630 - 1637
  • [3] IMPERA: Integrated Mission Planning for Multi-Robot Systems
    Saur, Daniel
    Geihs, Kurt
    ROBOTICS, 2015, 4 (04) : 435 - 463
  • [4] Heterogeneous multi-robot system mission planning with cooperative replenishment through data-driven rendezvous point selection
    Do, Haggi
    Jang, Junwoo
    Kim, Jinwhan
    INTELLIGENT SERVICE ROBOTICS, 2025, 18 (01) : 61 - 73
  • [5] Incorporating Potential Contingency Tasks in Multi-robot Mission Planning
    Shriyam, Shaurya
    Gupta, S. K.
    2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), 2018, : 3709 - 3715
  • [6] Underwater Multi-robot Persistent Area Coverage Mission Planning
    Li, Bingxi
    Moridian, Barzin
    Mahmoudian, Nina
    OCEANS 2016 MTS/IEEE MONTEREY, 2016,
  • [7] Decentralized Multi-Robot Mission Planning using Evolutionary Computation
    Dumka, Sugandha
    Maheshwari, Smiti
    Kala, Rahul
    2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2018, : 321 - 328
  • [8] A multi-robot mission planning algorithm with balanced workload objective
    Gao, Pingan
    Cai, Zixing
    Yu, Lingli
    Gaojishu Tongxin/Chinese High Technology Letters, 2009, 19 (05): : 501 - 505
  • [9] A Novel Mission Planning Method for Multi-robot Collaborative Area Coverage
    Liu, Bei
    Tao, Cancan
    She, Wanqiang
    INTELLIGENT ROBOTICS AND APPLICATIONS, ICIRA 2024, PT IX, 2025, 15209 : 32 - 43
  • [10] Distributed Mission Planning of Complex Tasks for Heterogeneous Multi-Robot Systems
    Ferreira, Barbara Arbanas
    Petrovic, Tamara
    Bogdan, Stjepan
    2022 IEEE 18TH INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE), 2022, : 1224 - 1231