In the field of modern medicine, endoscopic surgery has become a widely adopted minimally invasive technique, and the integration of robot technology has significantly enhanced surgical precision and flexibility. As a researcher in this domain, I have focused on developing advanced control strategies for endoscopic surgical robots to address challenges such as hand-eye coordination and trajectory accuracy. The master-slave teleoperation control approach is central to this effort, enabling surgeons to perform complex procedures with improved dexterity. This article presents a comprehensive design and analysis of a master-slave control strategy, incorporating spatial mapping algorithms and real-time adjustments to ensure high performance in clinical settings. The application of robot technology in surgery continues to evolve, and our work aims to contribute to this progress by refining control mechanisms for better outcomes.
The endoscopic surgical robot system is built upon a robust foundation of mechanical, electrical, and software components. The mechanical structure includes a master operator hand and a slave operator hand, with the slave comprising a remote center mechanism and surgical instruments. The master hand, such as the omega.7 series, offers seven degrees of freedom through a combination of serial and parallel configurations, allowing for intuitive control. The slave’s remote center mechanism is designed with three degrees of freedom—roll, pitch, and translation—using a double parallelogram linkage to maintain a fixed pivot point at the surgical incision, minimizing tissue damage. This mechanism ensures that the surgical instrument moves within a conical workspace, essential for precise manipulations. The surgical instruments themselves feature four degrees of freedom: roll, pitch, yaw, and grip, driven by wire transmission systems for smooth actuation. The integration of these elements exemplifies the sophistication of modern robot technology in medical applications.
In terms of the electrical control system, a distributed serial input/output architecture is employed, utilizing a Beckhoff C6015 industrial computer as the central processing unit. This setup supports real-time communication via EtherCAT protocol, connecting to ELMO drivers that control Maxon motor groups for each joint. For instance, the roll and pitch degrees of freedom in the remote center mechanism are driven by Maxon 849576 motor sets, while translation and instrument motions use Maxon 634917 motor sets. This hierarchical control ensures stable and responsive operation, which is critical for the high demands of surgical procedures. The software layer is structured into three levels: the surgical application layer for initialization and parameter settings, the motion planning layer for kinematic analysis and coordinate transformations using D-H parameters, and the device driver layer for executing motor commands. This comprehensive system design highlights the role of robot technology in achieving reliable and efficient surgical automation.
The core of our research lies in the master-slave teleoperation control strategy, which is based on an incremental spatial mapping algorithm in Cartesian space. This strategy consists of three modules: consistent mapping control, relative motion control, and proportional motion control. Consistent mapping control ensures that the master hand’s pose aligns with the slave instrument’s end-effector pose, facilitating hand-eye coordination. The transformation between coordinate systems is defined by the following equations, where $\{D\}$ represents the master hand handle frame, $\{M\}$ the master base frame, $\{O\}$ the remote center mechanism base frame, and $\{I\}$ the instrument end-effector frame. The relationship is given by:
$$ T_H^M = T_I^O $$
Here, $T_I^O$ is the transformation matrix to be determined, and $T_H^M$ is obtained from the master hand functions. This can be expanded as:
$$ T_I^O = \begin{bmatrix} R_I^O & P_I^O \\ 0 & 1 \end{bmatrix} $$
For consecutive control cycles $i$ and $i+1$, the orientation and position are updated as:
$$ R_I^O(i+1) = R_H^M(i+1) $$
$$ P_I^O(i+1) = P_H^M(i+1) – P_H^M(i) + P_I^O(i) $$
This ensures that the slave instrument accurately replicates the master’s movements, a key aspect of robot technology in surgical environments.
Relative motion control addresses scenarios where the master and slave workspaces are misaligned or when the surgeon needs to adjust the hand position for comfort. This method relies on incremental position changes rather than absolute positions, resetting the master’s position data after each adjustment. The position description in the reference frame of the experimental platform is given by:
$$ P_{O_{hl}}^{O_{ml}}(t+1) = P_{O_{hl}}^{O_{ml}}(t) + R_{O_{te}}^{O_{E}} \left( P_{O_{tl}}^{O_{LI}}(t+1) – P_{O_{tl}}^{O_{LI}}(t) \right) $$
Here, $P_{O_{hl}}^{O_{ml}}(t)$ denotes the position vector of the master hand movement frame relative to the master reference frame at time $t$, and $P_{O_{tl}}^{O_{LI}}(t)$ represents the slave movement frame relative to the slave base frame. This approach enhances flexibility in robot technology applications, allowing for seamless re-engagement of master-slave control.
Proportional motion control enables scaling of movements to suit different surgical tasks, with default ratio parameters set to 3:1 or 5:1, adjustable via the user interface. This control ensures that large master hand movements result in smaller, more precise slave instrument motions, which is vital for delicate procedures. The position update in the experimental platform reference frame is expressed as:
$$ P_{O_{hl}}^{O_{ml}}(t+1) = P_{O_{hl}}^{O_{ml}}(t) + \frac{1}{k} T_{O_{te}}^{O_{E}} R_{O_{LI}}^{O_{tl}} \left( P_{O_{tl}}^{O_{LI}}(t+1) – P_{O_{tl}}^{O_{LI}}(t) \right) $$
where $k$ is the proportionality coefficient. The block diagram for proportional motion control illustrates how the master hand’s input is scaled before being sent to the slave, emphasizing the adaptability of robot technology in varying surgical contexts.
To validate the control strategy, we conducted trajectory tracking experiments on a dedicated platform. The setup involved controlling the slave hand via the master hand omega.7, with a proportional parameter of 3:1. A trajectory tracking device with nine vertical protrusions of 0.4 mm diameter was used, and rubber rings were placed along a predefined path. The master and slave trajectories were recorded and analyzed for consistency and error. The results demonstrated that the slave hand closely followed the master’s commands, with trajectory errors summarized in the table below.
| Direction | Maximum Error (mm) | Average Error (mm) |
|---|---|---|
| x | 0.682 | 0.132 |
| y | 0.587 | 0.146 |
| z | 0.612 | 0.153 |
The maximum trajectory error was only 0.682 mm, which meets the precision requirements for surgical operations. These errors primarily stem from factors like wire tension deformation and friction in the transmission system. To mitigate this, we plan to upgrade to higher-strength tungsten wires in future iterations. The experiment confirms that our control strategy effectively enables hand-eye coordination and accurate trajectory replication, showcasing the potential of robot technology to enhance surgical outcomes.
In conclusion, the master-slave teleoperation control strategy for endoscopic surgical robots has been successfully designed and validated through rigorous experimentation. By integrating consistent mapping, relative motion, and proportional motion controls, we have achieved a system that supports surgeon intuition and precision. The use of robot technology in this context not only improves control accuracy but also paves the way for more advanced automated systems in healthcare. Future work will focus on optimizing wire transmission systems and exploring adaptive control algorithms to further reduce errors and expand the capabilities of surgical robots. As robot technology continues to advance, we anticipate even greater integration into medical practices, ultimately benefiting patient care and surgical efficiency.