Continuum arms are bio-inspired devices that exhibit continuous, smooth bending and generate motion through structural deformation. Rapidly-exploring random trees (RRT) is a traditional approach for performing efficient path planning. RRT approaches are usually based on exploring the configuration space (C-Space) of the robot to find a desirable work space (W-Space) path. Due to the complex kinematics and the highly non-linear mapping between the C-Space and W-Space of continuum arms, a high-quality path in the C-Space (e.g., a linear path) may not correspond to a desirable path/movement in the W-Space. Consequently, the C-Space RRT approaches that are based on C-Space cost functions do not lead to reliable and effective path planning when applied to continuum arms. In this letter, we propose a RRT* path planning approach for continuum arms that is based on exploring the W-Space of the robot as opposed to its C-Space. We show the successful applications of the proposed W-Space RRT* path planner in performing path planning with obstacle avoidance and in performing trajectory tracking. In all the aforementioned tasks, the quality of the paths generated by the proposed planner is superior to that of previous approaches and to its counterpart C-Space based RRT* approach, while the paths are generated in substantially less time.

In this article, the control problem of one segment pneumatically actuated soft robotic arm is investigated in detail. To date, extensive prior work has been done in soft robotics kinematics and dynamics modeling. Proper controller designs can complement the modeling work since they are able to compensate the effects that have not been considered in the modeling, such as the model uncertainties, system parameter identification error, hysteresis, external forces, disturbances, etc. In this letter, we explored different control approaches (kinematic control, PD+feedback linearization, passivity control, adaptive passivity control) and summarized the advantages and disadvantages of each controller. We further investigated the robot control problem in the practical scenarios when the sensor noise exists, actuator velocity measurement is not available, the hysteresis effect is non-neglectable, as well as the existence of external force. Our simulation results indicated that the adaptive passivity control with sigma modification terms, along with a high-gain observer presents a better performance in comparison with other approaches. Although this paper mainly presented the simulation results of various controllers, the work here will pave the way for practical implementation of soft robot control.

Currently, flexible surfaces enabled to be actuated by robotic arms are experiencing high interest and demand for robotic applications in various areas such as healthcare, automotive, aerospace, and manufacturing. However, their design and control thus far has largely been based on “trial and error” methods requiring multiple trials and/or high levels of user specialization. Robust methods to realize flexible surfaces with the ability to deform into large curvatures therefore require a reliable, validated model that takes into account many physical and mechanical properties including elasticity, material characteristics, gravity, external forces, and thickness shear effects. The derivation of such a model would then enable the further development of predictive-based control methods for flexible robotic surfaces. This paper presents a lumped-mass model for flexible surfaces undergoing large deformation due to actuation by continuum robotic arms. The resulting model includes mechanical and physical properties for both the surface and actuation elements to predict deformation in multiple curvature directions and actuation configurations. The model is validated against an experimental system where measured displacements between the experimental and modeling results showed considerable agreement with a mean error magnitude of about 1% of the length of the surface at the final deformed shapes.

Multisection continuum arms offer complementary characteristics to those of traditional rigid-bodied robots. Inspired by biological appendages, such as elephant trunks and octopus arms, these robots trade rigidity for compliance and accuracy for safety and, therefore, exhibit strong potential for applications in human-occupied spaces. Prior work has demonstrated their superiority in operation in congested spaces and manipulation of irregularly shaped objects. However, they are yet to be widely applied outside laboratory spaces. One key reason is that, due to compliance, they are difficult to control. Sophisticated and numerically efficient dynamic models are a necessity to implement dynamic control. In this paper, we propose a novel numerically stable center-of-gravity-based dynamic model for variable-length multisection continuum arms. The model can accommodate continuum robots having any number of sections with varying physical dimensions. The dynamic algorithm is of O (n ² ) complexity, runs at 9.5 kHz, simulates six to eight times faster than real time for a three-section continuum robot, and, therefore, is ideally suited for real-time control implementations. The model accuracy is validated numerically against an integral-dynamic model proposed by the authors and experimentally for a three-section pneumatically actuated variable-length multisection continuum arm. This is the first sub-real-time dynamic model based on a smooth continuous deformation model for variable-length multisection continuum arms.

Objective : We aim to develop and evaluate an MR-conditional concentric tube robot for intracerebral hemorrhage (ICH) evacuation. Methods: We fabricated the concentric tube robot hardware with plastic tubes and customized pneumatic motors. The robot kinematic model was developed using a discretized piece-wise constant curvature (D-PCC) approach to account for variable curvature along the tube shape, and tube mechanics modeling with friction to compensate torsional deflection of the inner tube. The MR-safe pneumatic motors were controlled using a variable gain PID algorithm. The robot hardware was validated in a series of systematic bench-top and MRI experiments, and the robot's evacuation efficacy was tested in MR-guided phantom trials. Results: The pneumatic motor was able to achieve a rotational accuracy of 0.32°±0.30° with the proposed variable gain PID control algorithm. The kinematic model provided a positional accuracy of the tube tip of 1.39±0.54 mm. The robot was able to evacuate an initial 38.36 mL clot, leaving a residual hematoma of 8.14 mL after 5 minutes, well below the 15 mL guideline suggesting good post-ICH evacuation clinical outcomes. Conclusion: This robotic platform provides an effective method for MR-guided ICH evacuation. Significance: ICH evacuation is feasible under MRI guidance using a plastic concentric tube, indicating potential feasibility in future animal studies.

The objective of this letter is to investigate the dynamic control of a soft robotic arm. First, a modular soft robotic hardware and an affordable actuator-space encoder were presented. We then discussed the soft robot modeling, and adaptive passivity control strategy with stability proof. The proposed controller was tested in different operation scenarios, and compared to the standard PD feedback linearization control and passivity control. In all experimental settings, the adaptive passivity control scheme was able to achieve superior performance, even with significant modeling uncertainties and disturbances. The theoretical analysis and experimental validations of the proposed controller will pave the way toward the practical implementations of the soft robotic system in the dynamic scenarios.