In swarm planning for ground robots, the risk of collisions due to trajectory intersections and the challenges of planning in unknown environments are critical issues. To address these challenges, this paper presents a swarm planning method based on asynchronous planning to achieve collision-free swarm planning in dense unknown environments. Path points are generated based on a multi-constraint framework, and trajectory optimization is performed by combining a “spring-damping” system with convex decomposition, thereby improving the method of generating local target points. To avoid trajectory conflicts between robots, we introduce a triangular trajectory conflict model and establish a velocity control method based on the “spring-damping” system. This enables two robots whose planned trajectories conflict to either accelerate simultaneously to escape or decelerate to avoid a collision. Simulation-based and real-world experiments show that the proposed method can generate executable, collision-free trajectories at a relatively high speed in unknown environments while effectively preventing collisions between robots during swarm planning.
Alonso-MoraJBreitenmoserABeardsleyP, et al. (2012) Reciprocal collision avoidance for multiple car-like robots. IEEE International Conference on Robotics and Automation: 360–366.
2.
ErdmannMLozano-PerezT (1987) On multiple moving objects. Algorithmica2(1): 477–521.
3.
GaoJLiYLiX, et al. (2024) A review of graph-based multi-agent pathfinding solvers: From classical to beyond classical. Knowledge-based Systems283: 111121.
4.
HanZWuYLiT, et al. (2023) An efficient spatial-temporal trajectory planner for autonomous vehicles in unstructured environments. IEEE Transactions on Intelligent Transportation Systems25(2): 1797–1814.
5.
HaraborDGrastienA (2011) Online graph pruning for pathfinding on grid maps. Proceedings of the AAAI Conference on Artificial Intelligence25(1): 1114–1119.
6.
HeJLiaoJ (2024) Formation tracking control with disturbance rejection in leader-follower multi-agent systems under dynamic event-triggered mechanism. Engineering Applications of Artificial Intelligence133: 108441.
7.
KondoKFigueroaRRachedJ, et al. (2023) Robust MADER: Decentralized multiagent trajectory planner robust to communication delay in dynamic environments. IEEE Robotics and Automation Letters9(2): 1476–1483.
8.
LiBOuyangYZhangY, et al. (2021) Optimal cooperative maneuver planning for multiple nonholonomic robots in a tiny environment via adaptive-scaling constrained optimization. IEEE Robotics and Automation Letters6(2): 1511–1518.
9.
LiJRanMXieL (2020) Efficient trajectory planning for multiple non-holonomic mobile robots via prioritized trajectory optimization. IEEE Robotics and Automation Letters6(2): 405–412.
10.
LiuSWattersonMMohtaK, et al. (2017) Planning dynamically feasible trajectories for quadrotors using safe flight corridors in 3-D complex environments. IEEE Robotics and Automation Letters2(3): 1688–1695.
11.
MaCHanZZhangT, et al. (2023) Decentralized planning for car-like robotic swarm in cluttered environments. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): 9293–9300.
12.
MaZTongDChenQ, et al. (2025) Fixed/prescribed-time synchronization and energy consumption for Kuramoto-Oscillator networks. IEEE Transactions on Cybernetics55(7): 3379–3389.
13.
ParkJKimJJangI, et al. (2020) Efficient multi-agent trajectory planning with feasibility guarantee using relative Bernstein polynomial. IEEE International Conference on Robotics and Automation (ICRA): 434–440.
14.
QiWLanPYangJ, et al. (2024) Multi-U-style micro-channel in liquid cooling plate for thermal management of power batteries. Applied Thermal Engineering256: 123984.
15.
QiWYangJZhangZ, et al. (2025) Investigation on thermal management of cylindrical lithium-ion batteries based on interwound cooling belt structure. Energy Conversion and Management340: 119962.
16.
ShiMTongDChenQ, et al. (2023) Pth moment exponential synchronization for delayed multi-agent systems with Lévy noise and Markov switching. IEEE Transactions on Circuits and Systems II: Express Briefs71(2): 697–701.
17.
TongDMaBChenQ, et al. (2023) Finite-time synchronization and energy consumption prediction for multilayer fractional-order networks. IEEE Transactions on Circuits and Systems II: Express Briefs70(6): 2176–2180.
18.
TordesillasJHowJP (2021) MADER: Trajectory planner in multiagent and dynamic environments. IEEE Transactions on Robotics38(1): 463–476.
19.
TordesillasJLopezBTEverettM, et al. (2021) Faster: Fast and safe trajectory planner for navigation in unknown environments. IEEE Transactions on Robotics38(2): 922–938.
20.
XuCQiWZhangZ, et al. (2025) Rapid trajectory planning under multiple constraints in unknown environments. Computing107(4): 95.
21.
YangJLeeBG (2024) Distributed adaptive tracking control of hidden leader-follower multi-agent systems with unknown parameters. Mathematics12(7): 1013.
22.
YinXCaiPZhaoK, et al. (2023) Dynamic path planning of AGV based on kinematical constraint A* algorithm and following DWA fusion algorithms. Sensors23(8): 4102.
23.
ZhangYXuSSongY, et al. (2024) Real-time global optimal energy management strategy for connected PHEVs based on traffic flow information. IEEE Transactions on Intelligent Transportation Systems25(12): 20032–20042.
24.
ZhouBGaoFWangL, et al. (2019) Robust and efficient quadrotor trajectory generation for fast autonomous flight. IEEE Robotics and Automation Letters4(4): 3529–3536.
25.
ZhouBPanJGaoF, et al. (2021a) Raptor: Robust and perception-aware trajectory replanning for quadrotor fast flight. IEEE Transactions on Robotics37(6): 1992–2009.
26.
ZhouJHeRWangY, et al. (2020a) Autonomous driving trajectory optimization with dual-loop iterative anchoring path smoothing and piecewise-jerk speed optimization. IEEE Robotics and Automation Letters6(2): 439–446.
27.
ZhouXWangZYeH, et al. (2020b) Ego-planner: An ESDF-free gradient-based local planner for quadrotors. IEEE Robotics and Automation Letters6(2): 478–485.
28.
ZhouXZhuJZhouH, et al. (2021b) Ego-swarm: A fully autonomous and decentralized quadrotor swarm system in cluttered environments. IEEE International Conference on Robotics and Automation (ICRA): 4101–4107.
29.
ZhuZSchmerlingEPavoneM (2015) A convex optimization approach to smooth trajectories for motion planning with car-like robots. IEEE Conference on Decision and Control (CDC): 835–842.
