Biohybrid microrobots with autonomous movement capabilities have broad application prospects in targeted delivery, attracting researchers to study their movement characteristics. However, its automatic control is still challenging, and exploring real-time detection of its environment for path planning to achieve stable closed-loop control is highly important for its practical application. Here, we applied deep learning for the detection of biohybrid microrobots and their targets and obstacles, followed by real-time path planning and trajectory tracking of biohybrid microrobots for targeted delivery. The proposed detection algorithm introduces attention and multi-scale feature fusion mechanisms in YOLOv7 algorithm (AM-YOLOv7) with the aim of enhancing the precision of detecting small-scale targets when robots, obstacles and targets are displayed globally, and the detection capabilities are verified through simulations and experiments. The proposed planning algorithm introduces a turning penalty function and a path smoothing strategy into A* algorithm (PS-A*) to make the planned path short and smooth, which has been verified through simulation and experiments. The adaptive fuzzy PID method is used to track the robot's trajectory, and experiments and simulations show that the biohybrid microrobot can move according to the preset trajectory better. The final cell scene experimental results show that the biohybrid microrobot using this system can effectively avoid obstacle cells and be delivered to target cells. The system can detect biohybrid microrobots, obstacle cells and target cells, plan short and smooth trajectories, and track them accurately. The proposed method has certain generalizability and broad application prospects in targeted delivery.

Magnetic microrobots can be navigated by an external magnetic field to autonomously move within living organisms with complex and unstructured environments. Potential applications include drug delivery, diagnostics, and therapeutic interventions. Existing techniques commonly impart magnetic properties to the target object, or drive the robot to contact and then manipulate the object, both probably inducing physical damage. This paper considers a non-contact formulation, where the robot spins to generate a repulsive field to push the object without physical contact. Under such a formulation, the main challenge is that the motion model between the input of the magnetic field and the output velocity of the target object is commonly unknown and difficult to analyze. To deal with it, this paper proposes a data-driven-based solution. A neural network is constructed to efficiently estimate the motion model. Then, an approximate model-based optimal control scheme is developed to push the object to track a time-varying trajectory, maintaining the non-contact with distance constraints. Furthermore, a straightforward planner is introduced to assess the adaptability of non-contact manipulation in a cluttered unstructured environment. Experimental results are presented to show the tracking and navigation performance of the proposed scheme.