Safe multi-agent coordination typically assumes what real-world, deployed, resource-constrained robots cannot provide: continuous, perfect state sharing. Robust control strategies must be complemented with robust communication strategies to deliver resilient deployments. In this work, we propose a unified control/communication strategy that provides safe control to guarantee inter-agent safety under intermittent communication and demonstrate its effectiveness in a pursuit-evasion scenario. Each agent computes the reachable set of neighboring agents based on the last state information received, then solves for a safe control input based on control barrier functions to ensure agents avoid collision. We prove that inter-agent collision avoidance is guaranteed provided communication staleness remains bounded. As the reachable set inflates between state transmissions, each agent reasons about when to preemptively transmit its state information to neighboring agents based on novel metrics characterizing uncertainty risk and kinematic risk. We implement and simulate a variety of pursuit-evasion scenarios and show that preemptive communication reduces messages by 77.5% compared to periodic and event-triggered baselines with zero collisions.
Figures & tables
Fig. 1: Demonstration of the preemptive communication mechanism. Pursuers i and j are represented as squares, their assigned evader is represented as a circle. On the left, pursuer i identifies that it is at risk of collision with pursuer j and therefore communicates its current state. Subsequently, j ’s estimated reachable set for i is reset, illustrated by the reachable set around i shrinking on the right.
Fig. 2: Snapshot of a simulation with 3 pursuers and 2 evaders. Pursuers are represented by squares, evaders by circles. Arrows indicate current velocity direction. Faint large spheres surrounding pursuers represent evader sensing range. Darker spheres represent predicted reachable sets.
Fig. 3: Simulation metrics for the 3 pursuer 2 evader case shown in Figure 2 . Distance, velocity, total risk, kinematic risk, and uncertainty risk plots follow pursuer 1 in Figure 2 . The preemptive communication mechanism ensures pursuers 1 and 0 communicate more frequently than pursuers 1 and 2 as they are at higher risk of collision. The pursuer-evader distance graph shows pursuers closing on and capturing their assigned evaders.
Description
Parameter
Value
Time step
Δt
0.1 s
Pursuer maximum velocity
vmax
2.0 m/s
Pursuer maximum control
umax
1.5 m/s 2
Safe distance
dsafe
0.5 m
Pursuer’s sensing range
κ
4 m
HOCBF controller gains
k0,k2
2
TABLE I: Simulation Parameters
Scenario
Metric
None
Periodic 1Hz
Periodic 5Hz
Event Triggered
Full
Preemptive
8P, 8E
Avg Total Msgs
0.00
817.60
3183.60
1356.18
8536.08
259.82
Msgs/Agent/s
0.00
6.71
26.14
11.13
70.01
2.12
Total Collisions
10
0.0
0.0
0.0
0.0
0.0
Avg Total Time
15.34s
15.24s
15.22s
15.23s
15.24s
15.33s
12P, 6E
Avg Total Msgs
0.00
1632.84
6306.96
2829.75
16675.56
604.80
Msgs/Agent/s
0.00
10.64
41.45
18.67
110.02
3.95
TABLE II: Communication Mechanism Performance Comparison
A central challenge in many mobile multi-robot applications is that communication topologies are inherently time-varying. Agents may enter or exit the network and such changes cannot generally be restricted a priori. This work introduces a distributed multi-agent control algorithm based on local communication that supports anytime agent joining and leaving the communication network without centralized coordination. The method scales efficiently with the number of agents by relying on a distance-based neighbor definition and on contracts derived from predicted trajectories. The resulting contract constraints guarantee collision avoidance and constraint satisfaction. We validate the proposed method in an autonomous multi-agent driving scenario, demonstrating effective collision avoidance in high-speed, dynamic environments with agents moving in opposite directions, in both simulated and real-world experiments.
Sabrina Bodmer, Danilo Saccani, Melanie N. Zeilinger +1
Institute for Dynamic Systems and Control, ETH Zurich, Switzerland · Institute of Mechanical Engineering, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
Asymmetric 3D pursuit-evasion in cluttered voxel environments is difficult under communication latency, partial observability, and nonholonomic maneuver limits. While many MARL methods rely on richer inter-agent coupling or centralized signals, these dependencies can become fragility sources when communication is delayed or noisy. Building on an inherited path-guided decentralized pursuit scaffold, we study a robustness-oriented question: can representational parsimony improve communication-free coordination? We instantiate this principle with (i) a parsimonious actor observation interface that removes team-coupled channels (83-D to 50-D), and (ii) Contribution-Gated Credit Assignment (CGCA), a locality-aware credit structure for communication-denied cooperation. In Stage-5 evaluation (4 pursuers vs. 1 evader), our configuration reaches 0.753 +/- 0.091 success and 0.223 +/- 0.066 collision, outperforming the 83-D FULL OBS counterpart (0.721 +/- 0.071, 0.253 +/- 0.089). It further shows graceful degradation under speed/yaw/noise/delay stress tests and resilient zero-shot transfer on urban-canyon maps (about 61% success at density 0.24). These results support a practical paradigm shift: explicitly severing redundant cross-agent channels can suppress compounding error cascades and improve robustness in latency-prone deployment.
Jialin Ying, Zhihao Li, Zicheng Dong +2
Department of Automation, Central South University, Changsha, China
To enable an efficient interaction of non-communicating mobile robots in collision avoidance scenarios, we present a novel combined trajectory planning and prediction algorithm. Inverse optimal control is used to estimate unknown goal states of all robots based on observed past trajectories. Each robot also takes the perspective of other robots in considering self-prediction and solves a joint prediction problem using the estimated goal states. The resulting predictions are then considered for planning. Simulation results of scenarios with 2-8 robots show that the median of the durations until all vehicles reach their goals is 9.8 % faster compared to planning with constant acceleration based estimated goal states. Moreover, the proposed approach never leads to the solver being unable to find a solution to the planning or prediction problem.
Nina Majer, Yannick Epple, Xin Ye +2
FZI Research Center for Information Technology, Haid-und-Neu-Str.10-14, 76131 Karlsruhe, Germany · Institute of Control Systems, Karlsruhe Institute of Technology, Kaiserstraße 12, 76131 Karlsruhe, Germany