This paper explores the concept of external magnetic control for vine robots to enable their high curvature steering and navigation for use in endoluminal applications. Vine robots, inspired by natural growth and locomotion strategies, present unique shape adaptation capabilities that allow passive deformation around obstacles. However, without additional steering mechanisms, they lack the ability to actively select the desired direction of growth. The principles of magnetically steered growing robots are discussed, and experimental results showcase the effectiveness of the proposed magnetic actuation approach. We present a 25 mm diameter vine robot with integrated magnetic tip capsule, including 6 Degrees of Freedom (DOF) localization and camera and demonstrate a minimum bending radius of 3.85 cm with an internal pressure of 30 kPa. Furthermore, we evaluate the robot's ability to form tight curvature through complex navigation tasks, with magnetic actuation allowing for extended free-space navigation without buckling. The suspension of the magnetic tip was also validated using the 6 DOF localization system to ensure that the shear-free nature of vine robots was preserved. Additionally, by exploiting the magnetic wrench at the tip, we showcase preliminary results of vine retraction. The findings contribute to the development of controllable vine robots for endoluminal applications, providing high tip force and shear-free navigation.

Examples range from multiagent planning, That is, we look for the most representative voter and return to crowdsourcing and human computation, to collaborative her ballot as the outcome. In our example, a natural choice filtering for recommender systems, to rank aggregation would be any of the voters voting (0, 1, 1). The distance of for search engines, to coordination and resource allocation this choice to the individual ballots is 42 (21 voters disagree in multiagent systems. Several frameworks have been on 2 issues each), i.e., this solution is only marginally worse proposed in the literature on computational social choice than the solution returned by the distance-based rule--and it (Chevaleyre et al. 2007; Brandt, Conitzer, and Endriss 2013) is optimal in case (1, 1, 1) is infeasible.