Robots sometimes have to work together with a mixture of partially-aligned or conflicting goals. Flocking - coordinated motion through cohesion, alignment, and separation - traditionally assumes uniform desired inter-agent distances. Many practical applications demand greater flexibility, as the diversity of types and configurations grows with the popularity of multi-agent systems in society. Moreover, agents often operate without guarantees of trust or secure communication. Motivated by these challenges we update well-established frameworks by relaxing this assumption of shared inter-agent distances and constraints. Through a new form of constrained collective potential function, we introduce a solution that permits negotiation of these parameters. In the spirit of the traditional flocking control canon, this negotiation is achieved purely through local observations and does not require any global information or inter-agent communication. The approach is robust to semi-trust scenarios, where neighbouring agents pursue conflicting goals. We validate the effectiveness of the approach through a series of simulations.

翻译：机器人有时需要在目标部分一致或相互冲突的混合状态下协同工作。集群运动——通过内聚、对齐和分离实现的协调运动——传统上假定智能体间具有统一的期望距离。随着多智能体系统在社会中的普及，其类型与配置的多样性日益增长，许多实际应用需要更大的灵活性。此外，智能体常在缺乏信任保证或安全通信保障的环境中运行。受这些挑战驱动，我们通过放宽共享智能体间距离与约束的假设，对现有成熟框架进行了更新。通过一种新型约束集体势函数，我们提出了一种允许对这些参数进行协商的解决方案。秉承传统集群控制规范的精神，该协商完全通过局部观测实现，无需任何全局信息或智能体间通信。该方法对半信任场景具有鲁棒性，即相邻智能体可能追求相互冲突的目标。我们通过一系列仿真验证了该方法的有效性。