Research on cooperative algorithm of UGV-UAV based on TERRA in complex environment

Abstract

The utilization of heterogeneous multi-agent systems for planetary exploration represents a significant advancement in the field of future deep space exploration. The author’s research investigates a set of heterogeneous multi-agent systems, which include an Unmanned Ground Vehicle (UGV) and an Unmanned Aerial Vehicle (UAV), designed to explore a series of target points within a three-dimensional complex environment. In this 3D context, the complexity of environmental factors poses challenges, with terrain hazards being the primary concern that impacts UGV-UAV cooperative exploration. To facilitate the successful completion of target point exploration while minimizing risk, the authors proposes a cooperative search routing algorithm specifically tailored for UGV-UAV systems operating in complex environments. The algorithm capitalizes on the endurance advantages inherent to UGV alongside the functional benefits provided by UAVs; notably, it designates the UGV as a charging station for the UAV and positions the UAV as the principal detection device within this system to effectively accomplish detection tasks. Following the research framework established in the author’s research, it is essential first to define a set of target points within the 3D environment and subsequently project these points onto a 2D plane to identify docking locations for UGVs. These docking points are then re-projected into their corresponding positions in 3D space. The A* algorithm is employed to plan optimal paths between these UGV docking points within that three-dimensional context, thereby ensuring comprehensive exploration of all designated target points. Finally, the authors conducted simulation experiments in relation to Mars exploration. The authors developed a Mars exploration simulation platform based on virtual reality technology. This platform incorporates a physics engine designed to replicate Mars’s physical environment, enabling us to validate the effectiveness of the proposed system under conditions analogous to those found on Mars.

Keywords

UGV-UAV planetary exploration cooperative search routing algorithm improved A* algorithm path planning

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References

Liu

Zhao

Sun

. A review of collaborative air-ground robots research. J Intell Rob Syst 2022; 106: 60. https://doi.org/10.1007/s10846-022-01756-4

Lazna

Gabrlik

Jilek

, et al. Cooperation between an unmanned aerial vehicle and an unmanned ground vehicle in highly accurate localization of gamma radiation hotspots. Int J Adv Rob Syst 2018; 15: 1729881417750787. https://doi.org/10.1177/1729881417750787

Liang

Wang

Luo

. Collaborative pursuit-evasion strategy of UAV/UGV heterogeneous system in complex three dimensional polygonal environment. Complexity 2020; 2020: 1–13. https://doi.org/10.1155/2020/7498740

Ayranci

Erkmen

. Edge computing and robotic applications in modern agriculture. In: Proceedings of the 2024 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), Istanbul, Turkey, May 23, 2024, pp. 23–25.

Leahy

Zhou

Vasile

, et al. Persistent surveillance for unmanned aerial vehicles subject to charging and temporal logic constraints. Auton Robots 2016; 40: 1363–1378. https://doi.org/10.1007/s10514-015-9519-z

Mohsan

SAH

Khan

Noor

, et al. Towards the unmanned aerial vehicles (UAVs): a comprehensive review. Drones 2022; 6: 147. https://doi.org/10.3390/drones6060147

Shakhatreh

Sawalmeh

Al-Fuqaha

, et al. Unmanned aerial vehicles (UAVs): a survey on civil applications and key research challenges. IEEE Access 2019; 7: 48572–48634. https://doi.org/10.1109/access.2019.2909530

Zeng

Zhang

Lim

. Wireless communications with unmanned aerial vehicles: opportunities and challenges. IEEE Commun Mag 2016; 54: 36–42. https://doi.org/10.1109/mcom.2016.7470933

Ergezer

Leblebiciolu

. 3D path planning for multiple UAVs for maximum information collection. J Intell Rob Syst 2014; 73: 737–762. https://doi.org/10.1007/s10846-013-9895-6

10.

Tian

Shen

Zheng

. Genetic algorithm based approach for multi-UAV cooperative reconnaissance mission planning problem. In: International symposium on methodologies for intelligent systems, Bari, Italy, 27 September 2006, pp. 101–110. Springer.

11.

Liu

Peng

, et al. Joint routing and scheduling for vehicle-assisted multi-drone surveillance. IEEE Internet Things J 2018; 6: 1781–1790. https://doi.org/10.1109/jiot.2018.2878602

12.

Zhang

Mao

, et al. IADRL: imitation augmented deep reinforcement learning enabled UGV-UAV coalition for tasking in complex environments. IEEE Access 2020; 8: 102335–102347. https://doi.org/10.1109/access.2020.2997304

13.

Brotee

Kabir

Razzaque

, et al. Optimizing UAV-UGV coalition operations: a hybrid clustering and multi-agent reinforcement learning approach for path planning in obstructed environment. Ad Hoc Netw 2024; 160: 103519. https://doi.org/10.1016/j.adhoc.2024.103519

14.

Mathew

Smith

Waslander

. Multirobot rendezvous planning for recharging in persistent tasks. IEEE Trans. Robot 2015; 31: 128–142. https://doi.org/10.1109/tro.2014.2380593

15.

Zhou

Chen

, et al. Human-robot skills transfer interface for UAV-based precision pesticide in dynamic environments [J]. Assem Autom 2021; 41(3): 345–357. https://doi.org/10.1108/aa-11-2020-0161

16.

Munasinghe

Perera

Deo

. A comprehensive review of UAV-UGV collaboration: Advancements and challenges. J Sens Actuator Netw 2024; 13: 81. https://doi.org/10.3390/jsan13060081

17.

Tang

Huang

, et al. Formation control with mismatched orientation in multi-agent systems [J]. IEEE Transactions on Network Science and Engineering 2018; 6(3): 314–325. https://doi.org/10.1109/tnse.2018.2851199

18.

Thelasingha

Julius

Humann

, et al.

Iterative motion planning in multi-agent systems with opportunistic communication

under disturbance [J]. arXiv preprint arXiv: 2503.12457. 2025. https://doi.org/10.48550/arXiv.2503.12457

19.

Scherer

. Model-based real-time robust controller for a small helicopter [J]. Mech Syst Signal Process 2021; 146: 107022. https://doi.org/10.1016/j.ymssp.2020.107022

20.

Yue

Zhang

Chen

, et al. LiDAR-based SLAM for robotic mapping: state of the art and new frontiers [J]. Ind Robot: the international journal of robotics research and application 2024; 51(2): 196–205. https://doi.org/10.1108/ir-09-2023-0225

21.

Ropero

Pablo

María

D.R-M

. TERRA: A path planning algorithm for cooperative UGV–UAV exploration. Engineering Applications of Artificial Intelligence. 2019; 78: 260–272. https://doi.org/10.1016/j.engappai.2018.11.008

22.

Maini

Sujit

. On cooperation between a fuel constrained UAV and a refueling UGV for large scale mapping applications. In: Unmanned Aircraft Systems (ICUAS), 2015 International Conference on. IEEE, Denver, Colorado, USA, June 9, 2015, pp. 1370–1377.

23.

Murray

Chu

. The flying sidekick traveling salesman problem: optimization of drone-assisted parcel delivery [J]. Transport Res C Emerg Technol 2015; 54: 86–109. https://doi.org/10.1016/j.trc.2015.03.005

24.

Poikonen

Golden

Wasil

. A branch-and-bound approach to the traveling salesman problem with a drone [J]. Inf J Comput 2019; 31(2): 335–346. https://doi.org/10.1287/ijoc.2018.0826

25.

Manyam

Sundar

Casbeer

. Cooperative routing for an air-ground vehicle team — exact algorithm, transformation method, and heuristics. IEEE Trans Autom Sci Eng 2019; 16(4): 1529–1542.

26.

Karapetyan

Asghar

A B

Bhaskar

, et al. Ag-cvg:Coverage planning with a mobile recharging ugv and an energy-constrained uav[C]//2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024: 2617–2623.

27.

Watson

. Computing the n-dimensional delaunay tessellation with application to voronoi polytopes. Comput J 1981; 24: 167–172. https://doi.org/10.1093/comjnl/24.2.167

28.

Reddy

Venkatramana

. Comparison of different spatial interpolation techniques of field measurements to construct radio environment map [J]. Int. J. Future Gener. Commun. Netw 2018; 11: 21–31. https://doi.org/10.14257/ijfgcn.2018.11.3.03

29.

Magid

Lavrenov

Afanasyev

. Voronoi-based trajectory optimization for UGV path planning[C]//2017 international conference on mechanical, system and Control Engineering (ICMSC), St. Petersburg, Russia, May 19, 2017, pp. 383–387. IEEE.

30.

REN

Rathinam

Choset

. CBSS: a new approach for multiagent combinatorial path finding [J]. IEEE Trans Robot 2023; 39(4):2669–2683. https://doi.org/10.1109/tro.2023.3266993

31.

Albani

Hönig

Nardi

, et al. Hierarchical task assignment and path finding with limited communication for robot swarms [J]. Appl Sci 2021; 11(7):3115. https://doi.org/10.3390/app11073115

32.

Wang

, et al. Research on AGV task path planning based on improved A* algorithm [J]. Virtual Reality & Intelligent Hardware 2023; 5(3):249–265. https://doi.org/10.1016/j.vrih.2022.11.002

33.

Liu

Wang

Zhu

, et al. Path planning method for rover based on virtual reality [J]. J Comput Appl 2019; 44(S1):273–276. (In Chinese).

34.

Miaolei

Ren

, et al. A horse inspired eight-wheel unmanned ground vehicle with four-swing arms[C]. In: //IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, Nevada, USA, October 25, 2020, pp. 7723–7728. IEEE.