Shared control combines human intention with autonomous decision-making. At the low level, the primary goal is to maintain safety regardless of the user's input to the system. However, existing shared control methods-based on, e.g., Model Predictive Control, Control Barrier Functions, or learning-based control-often face challenges with feasibility, scalability, and mixed constraints. To address these challenges, we propose a Constraint-Aware Assistive Controller that computes control actions online while ensuring recursive feasibility, strict constraint satisfaction, and minimal deviation from the user's intent. It also accommodates a structured class of non-convex constraints common in real-world settings. We leverage Robust Controlled Invariant Sets for recursive feasibility and a Mixed-Integer Quadratic Programming formulation to handle non-convex constraints. We validate the approach through a large-scale user study with 66 participants-one of the most extensive in shared control research-using a simulated environment to assess task load, trust, and perceived control, in addition to performance. The results show consistent improvements across all these aspects without compromising safety and user intent. Additionally, a real-world experiment on a robotic manipulator demonstrates the framework's applicability under bounded disturbances, ensuring safety and collision-free operation.

翻译：共享控制将人类意图与自主决策相结合。在底层控制层面，其主要目标是在不考虑用户输入的情况下确保系统安全。然而，现有的共享控制方法——例如基于模型预测控制、控制屏障函数或基于学习的控制——常常面临可行性、可扩展性以及混合约束方面的挑战。为应对这些挑战，我们提出了一种约束感知辅助控制器，该控制器在线计算控制动作，同时确保递归可行性、严格约束满足以及与用户意图的最小偏差。它还能适应现实场景中常见的一类结构化非凸约束。我们利用鲁棒受控不变集保证递归可行性，并采用混合整数二次规划公式处理非凸约束。我们通过一项包含66名参与者的大规模用户研究——这是共享控制研究领域规模最广泛的实验之一——在模拟环境中评估任务负荷、信任度、感知控制力以及性能，从而验证了该方法的有效性。结果表明，在不影响安全性和用户意图的前提下，所有评估维度均获得持续改善。此外，在机器人机械臂上进行的真实世界实验验证了该框架在有界扰动下的适用性，确保了安全性与无碰撞运行。