Tendon-driven continuum robots have been gaining popularity in medical applications due to their ability to curve around complex anatomical structures, potentially reducing the invasiveness of surgery. However, accurate modeling is required to plan and control the movements of these flexible robots. Physics-based models have limitations due to unmodeled effects, leading to mismatches between model prediction and actual robot shape. Recently proposed learning-based methods have been shown to overcome some of these limitations but do not account for hysteresis, a significant source of error for these robots. To overcome these challenges, we propose a novel deep decoder neural network that predicts the complete shape of tendon-driven robots using point clouds as the shape representation, conditioned on prior configurations to account for hysteresis. We evaluate our method on a physical tendon-driven robot and show that our network model accurately predicts the robot's shape, significantly outperforming a state-of-the-art physics-based model and a learning-based model that does not account for hysteresis.

One of the most important features of tendon-driven robots is the ease of wire arrangement and the degree of freedom it affords, enabling the construction of a body that satisfies the desired characteristics by modifying the wire arrangement. Various wire arrangement optimization methods have been proposed, but they have simplified the configuration by assuming that the moment arm of wires to joints are constant, or by disregarding wire arrangements that span multiple joints and include relay points. In this study, we formulate a more flexible wire arrangement optimization problem in which each wire is represented by a start point, multiple relay points, and an end point, and achieve the desired physical performance based on black-box optimization. We consider a multi-objective optimization which simultaneously takes into account both the feasible operational force space and velocity space, and discuss the optimization results obtained from various configurations.

Tendon-driven robots, where one or more tendons under tension bend and manipulate a flexible backbone, can improve minimally invasive surgeries involving difficult-to-reach regions in the human body. Planning motions safely within constrained anatomical environments requires accuracy and efficiency in shape estimation and collision checking. Tendon robots that employ arbitrarily-routed tendons can achieve complex and interesting shapes, enabling them to travel to difficult-to-reach anatomical regions. Arbitrarily-routed tendon-driven robots have unintuitive nonlinear kinematics. Therefore, we envision clinicians leveraging an assistive interactive-rate motion planner to automatically generate collision-free trajectories to clinician-specified destinations during minimally-invasive surgical procedures. Standard motion-planning techniques cannot achieve interactive-rate motion planning with the current expensive tendon robot kinematic models. In this work, we present a 3-phase motion-planning system for arbitrarily-routed tendon-driven robots with a Precompute phase, a Load phase, and a Supervisory Control phase. Our system achieves an interactive rate by developing a fast kinematic model (over 1,000 times faster than current models), a fast voxel collision method (27.6 times faster than standard methods), and leveraging a precomputed roadmap of the entire robot workspace with pre-voxelized vertices and edges. In simulated experiments, we show that our motion-planning method achieves high tip-position accuracy and generates plans at 14.8 Hz on average in a segmented collapsed lung pleural space anatomical environment. Our results show that our method is 17,700 times faster than popular off-the-shelf motion planning algorithms with standard FK and collision detection approaches. Our open-source code is available online.

The robotic limb has an architecture resembling the muscle and tendon structure that powers human and vertebrate movement [1,2]. Tendons connect muscles to bones, making it possible for the biological motors (muscles) to exert force on bones from a distance [3,4]. While tendons have mechanical and structural advantages, a tendon-driven robot is significantly more challenging to control than a traditional robot, where a simple PID controller to control joint angles directly is often sufficient. In a tendon-driven robotic limb, multiple motors may act on a single joint, which means that a given motor may act on multiple joints. As a result, the system is simultaneously nonlinear, over-determined, and under-determined, greatly increasing the control design complexity and calling for a new control design approach.