Modular soft robot arms (MSRAs) are composed of multiple independent modules connected in a sequence. Due to their modular structure and high degrees of freedom (DOFs), these modules can simultaneously bend at different angles in various directions, enabling complex deformation. This capability allows MSRAs to perform more intricate tasks than single module robots. However, the modular structure also induces challenges in accurate planning, modeling, and control. Nonlinearity, hysteresis, and gravity complicate the physical model, while the modular structure and increased DOFs further lead to accumulative errors along the sequence. To address these challenges, we propose a flexible task space planning and control strategy for MSRAs, named S2C2A (State to Configuration to Action). Our approach formulates an optimization problem, S2C (State to Configuration planning), which integrates various loss functions and a forward MSRA model to generate configuration trajectories based on target MSRA states. Given the model complexity, we leverage a biLSTM network as the forward model. Subsequently, a configuration controller C2A (Configuration to Action control) is implemented to follow the planned configuration trajectories, leveraging only inaccurate internal sensing feedback. Both a biLSTM network and a physical model are utilized for configuration control. We validated our strategy using a cable-driven MSRA, demonstrating its ability to perform diverse offline tasks such as position control, orientation control, and obstacle avoidance. Furthermore, our strategy endows MSRA with online interaction capability with targets and obstacles. Future work will focus on addressing MSRA challenges, such as developing more accurate physical models and reducing configuration estimation errors along the module sequence.

翻译：模块化软体机械臂由多个独立模块按顺序连接构成。得益于其模块化结构和高自由度，这些模块能够在不同方向上同时以不同角度弯曲，从而实现复杂形变。这种能力使得模块化软体机械臂能够执行比单模块机器人更为复杂的任务。然而，模块化结构也给精确规划、建模与控制带来了挑战。非线性、迟滞效应和重力作用使物理模型复杂化，而模块化结构及增加的自由度进一步导致沿模块序列的累积误差。为应对这些挑战，我们提出了一种面向模块化软体机械臂的灵活任务空间规划与控制策略，命名为S2C2A（状态到构型到动作）。我们的方法构建了一个优化问题S2C（状态到构型规划），该问题整合了多种损失函数和一个模块化软体机械臂正向模型，以基于目标机械臂状态生成构型轨迹。鉴于模型复杂性，我们采用双向长短期记忆网络作为正向模型。随后，配置控制器C2A（构型到动作控制）被部署以跟踪规划的构型轨迹，该控制器仅利用不精确的内部传感反馈。构型控制中同时采用了双向长短期记忆网络和物理模型。我们通过缆绳驱动的模块化软体机械臂验证了该策略，证明了其执行多样化离线任务的能力，如位置控制、姿态控制和避障。此外，该策略赋予模块化软体机械臂与目标及障碍物在线交互的能力。未来工作将聚焦于解决模块化软体机械臂的关键挑战，例如开发更精确的物理模型以及降低沿模块序列的构型估计误差。