This study presents an internalized morphogenesis model for autonomous systems, such as swarm robotics and micro-nanomachines, that eliminates the need for external spatial computation. Traditional self-organizing models often require calculations across the entire coordinate space, including empty areas, which is impractical for resource-constrained physical modules. Our proposed model achieves complex morphogenesis through strictly local interactions between adjacent modules within the "body." By extending the "Ishida token model," modules exchange integer values using an RD-inspired discrete analogue without solving differential equations. The internal potential, derived from token accumulation and aging, guides autonomous growth, shrinkage, and replication. Simulations on a hexagonal grid demonstrated the emergence of limb-like extensions, self-division, and robust regeneration capabilities following structural amputation. A key feature is the use of the body boundary as a natural sink for information entropy (tokens) to maintain a dynamic equilibrium. These results indicate that sophisticated morphological behaviors can emerge from minimal, internal-only rules. This framework offers a computationally efficient and biologically plausible approach to developing self-repairing, adaptive, and autonomous hardware.

翻译：本研究提出了一种适用于自主系统（如群体机器人和微纳机器）的内部化形态发生模型，该模型消除了对外部空间计算的需求。传统的自组织模型通常需要在包括空区域在内的整个坐标空间进行计算，这对于资源受限的物理模块而言并不实用。我们提出的模型通过“体内”相邻模块之间的严格局部相互作用实现复杂的形态发生。通过扩展“Ishida令牌模型”，模块利用一种受反应-扩散启发的离散模拟交换整数值，而无需求解微分方程。源自令牌积累与老化的内部势能引导着自主生长、收缩和复制。在六边形网格上的仿真演示了肢体状延伸、自我分裂以及结构截除后鲁棒再生能力的涌现。一个关键特征是使用身体边界作为信息熵（令牌）的自然汇，以维持动态平衡。这些结果表明，复杂的形态行为可以从极简的、纯内部规则中涌现。该框架为开发自修复、自适应和自主的硬件提供了一种计算高效且生物学上合理的方法。