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Continuum arms, with their mix of compliance, payload, safety, and manipulability, are perfectly suited to serve as co-robots, and their applications range from industry and manufacturing to human healthcare. Their hyper-redundancy serves as their most significant challenge for path planning and path planning approaches commonly used with rigid-link robots, such as inverse kinematics, that fail to provide reliable trajectories for continuum arms. We propose an Inverse Kinematics-based approach to address the limitations of previously-proposed Kinematics-based approaches. Using this new approach, we are able to efficiently generate very rich sets of configurations, which, in turn, lead to smooth path planning for such continuum manipulators. To validate the smoothness of the paths generated by our approach, we apply dynamics constraints to the generated trajectories. We show that, when tracked by a controller, the paths that are generated using the proposed approach are much smoother than previously-proposed Kinematics-based approaches: The proposed approach allows the continuum arm to traverse the trajectories very accurately and in time less than half of that taken by previous (reliable) path planning approaches. 

Continuum arms are more adaptable to their environments and inherently human-friendly compared to their rigid counterparts. Path planning of continuum arms is an active research area with many challenges. The hyper-redundancy of continuum arms, which renders them highly versatile, is their curse in path planning. This problem becomes even more challenging in dynamic environments in the presence of mobile obstacles. In this paper, we propose an anticipatory path planning approach for continuum arms in dynamic environments. Our approach is based on obstacle prediction coupled with temporal graphs to model the dynamic environment. We evaluate the proposed approach’s performance and compare it to prevailing path planning approaches for continuum arms in dynamic environments. 

We study the path planning problem for continuum-arm robots, in which we are given a starting and an end point, and we need to compute a path for the tip of the continuum arm between the two points. We consider both cases where obstacles are present and where they are not. We demonstrate how to leverage the continuum arm features to introduce a new model that enables a path planning approach based on the configurations graph, for a continuum arm consisting of three sections, each consisting of three muscle actuators. The algorithm we apply to the configurations graph allows us to exploit parallelism in the computation to obtain efficient implementation. We conducted extensive tests, and the obtained results show the completeness of the proposed algorithm under the considered discretizations, in both cases where obstacles are present and where they are not. We compared our approach to the standard inverse kinematics approach. While the inverse kinematics approach is much faster when successful, our algorithm always succeeds in finding a path or reporting that no path exists, compared to a roughly 70% success rate of the inverse kinematics approach (when a path exists).