article · IEEE Robotics and Automation Letters
Flexible continuum robots offer notable advantages over traditional rigid robots regarding safety and manoeuvrability, making them suitable for complex unstructured settings such as medical operations. Controlling and planning movements for these systems remains difficult due to their compliant physical nature and inherent system uncertainties. To tackle this, a new Imitation-based Motion Planning approach paired with dynamic impedance control has been developed for a two-section soft continuum manipulator. The framework records human-guided point-to-point demonstrations using a motion capture system and a flexible interface to capture tip position and orientation. A singularity-free dynamic model using Lagrangian mechanics and Taylor expansion series supports the planning process. In simulations, this integrated method effectively adapted to varying start and target poses, avoided both static and moving obstacles, and restored trajectory tracking following unexpected disturbances.
Soft, flexible robots are safer and more adaptable than rigid machines, which makes them promising candidates for delicate tasks such as minimally invasive surgery. However, their bendable bodies are difficult to control reliably. By teaching these robots through demonstration and enabling them to avoid obstacles and recover from disturbances, this approach helps bring flexible robotic systems closer to dependable operation in sensitive environments.
The method is aimed at systems operating in unstructured environments, pointing towards developers of flexible surgical tools and medical manipulators. The technique enables robots to adapt to moving obstacles and return to their path after physical disruptions. Because validation has been completed entirely in simulation with demonstrations captured via laboratory tracking hardware, the technology remains early-stage research that requires physical prototyping and experimental testing before clinical or industrial translation.
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Recently, flexible robots are growing in importance owing to their merits over rigid robots in maneuverability and safety, which equips them to work in unstructured environments, such as occur in medical applications. However, motion planning and control of flexible manipulators is challenging due to their compliance behavior and system uncertainties. Thus, this letter presents an Imitation-based Motion Planning (IbMP) approach, along with dynamic impedance control for learning, planning, and trajectory tracking of a two-section soft continuum robot in a dynamic environment. Point-to-point motion demonstrations, including the robot's tip position and orientation are intuitively provided by a motion capture system (OptiTrack V120-trio) and a similar kinematic flexible interface. Additionally, a singularity-free dynamic model based on Lagrangian formulation and Taylor expansion series of a two-section continuum robot is derived while planning for robot motions. The simulation results show that the IbMP approach, along with the dynamic impedance control, generalizes the robot's motion by varying the initial and goal of the robot's tip pose while avoiding static and dynamic obstacles, and moving back to the desired track after disturbances. Finally, the stability and performance of the IbMP algorithm against input disturbances are assessed using a Monte Carlo approach that can guide the selection of gain values.
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DOI: 10.1109/lra.2023.3239306
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