![]() To solve this problem, it is advisable to use tensegrity drones with the deformable structure and the ability to adapt to the changing environment parameters taking into account the obstacles encountered in the flight. An urgent task is to ensure the drone safety against their mechanical damage when interacting with the external environment, as well as the safety of people in case of contact with the drones. ![]() Drones appear to be a promising area in robotics performing dangerous tasks during search and rescue operations, as well as in practical applications such as photography and cinematography. Modern aerial robots, in particular the drones, are developing at a rapid pace. At the same time, the approach can provide accurate pose estimation throughout the robot’s motion, while motion capture often fails due to occlusions. Given ground truth data from a motion capture system, the proposed method achieves less than 1 cm translation error and 3∘ rotation error, which significantly outperforms alternatives. Real-world experiments are performed with a 3-bar tensegrity robot, which performs locomotion gaits. To ensure that the pose estimates of rigid elements are physically feasible, i.e., they are not resulting in collisions between rods or with the environment, physical constraints are introduced during the optimization. In particular, an iterative optimization process is proposed to track the 6-DoF pose of each rigid element of a tensegrity robot from an RGB-D video as well as endcap distance measurements from the cable sensors. This work aims to address what has been recognized as a grand challenge in this domain, i.e., the state estimation of tensegrity robots through a marker-less, vision-based method, as well as novel, on-board sensors that can measure the length of the robot’s cables. Nevertheless, the hybrid soft-rigid nature of these robots also complicates the ability to localize and track their state. Tensegrity robots, which are composed of compressive elements (rods) and flexible tensile elements (e.g., cables), have a variety of advantages, including flexibility, low weight, and resistance to mechanical impact. This work represents a preliminary step toward soft modular systems capable of independent and collective behaviors, and provide a platform for future studies on distributed control. The soft lattice modules are capable of independent locomotion, and can also join with other modules to achieve collective, self-assembled, larger scale tasks such as collective locomotion and moving an object across the surface of the lattice assembly. The soft lattice modules are comprised of 3D printed plastic “skeletons,” linear contracting shape mem- ory alloy spring actuators, and permanent magnets that enable adhesion between modules. In this letter, we exploit the opportunities presented by soft, modular, and tensegrity robots to introduce soft lattice modules that parallel the sub-units seen in biological systems. Natural systems integrate the work of many sub-units (cells) toward a large-scale unified goal (morphological and behav- ioral), which can counteract the effects of unexpected experiences, damage, or simply changes in tasks demands. This makes the robot safe for the humans around it and protects the drone itself during aggressive maneuvers in constrained and cluttered environments, a feature that is becoming increasingly important for challenging applications that include cave exploration and indoor disaster response. The Tensodrone is based on a six-bar tensegrity structure that is inherently compliant and can withstand crash landings and frontal collisions with obstacles. To show the viability of tensegrity drones, the first tensegrity quadrotor Tensodrone was build. The numerical properties of the algorithm are demonstrated in numerical studies. The proposed method takes advantage of the need to use mixed-integer variables in choosing the drone path (using big-M relaxation) to simultaneously choose the configuration of the drone, eliminating the need to use semidefinite matrices to encode configurations, as was done previously. Previous work in the field required the use of bounding surfaces, making the planning more conservative. ![]() This paper proposes a method for simultaneously planning a path and a sequence of deformations for a tensegrity drone.
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