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Unmanned aerial manipulators have been growing in popularity over the years, alongside the complexity of the tasks they undertake. Many of these tasks include physical interaction with the environment, where a force control or sensing component is desirable. In these types of applications, the forces and torques, or the wrench, acting on the robot by the environment must be known. This paper presents a wrench observer based on an Extended Kalman filter (EKF), and compares it against acceleration-based, momentum-based, and hybrid wrench observers. Simulations using each of these observers are conducted with an underactuated aerial manipulator composed of a hexarotor with coplanar propellers and a 2-DOF manipulator. Measurement noise on par with what is expected in real-world applications is added to the sensor signals, and results show that the EKF-based wrench observer is superior at noise reduction and wrench estimation in many cases compared to the other observers. 

Controlling soft continuum robotic arms is challenging due to their hyper-redundancy and dexterity. In this paper we experimentally demonstrate, for the first time, closed-loop control of the configuration space variables of a soft robotic arm, composed of independently controllable segments, using a Cosserat rod model of the robot and the distributed sensing and actuation capabilities of the segments. Our controller solves the inverse dynamic problem by simulating the Cosserat rod model in MATLAB using a computationally efficient numerical solution scheme, and it applies the computed control output to the actual robot in real time. The position and orientation of the tip of each segment are measured in real time, while the remaining unknown variables that are needed to solve the inverse dynamics are estimated simultaneously in the simulation. We implement the controller on a multi-segment silicone robotic arm with pneumatic actuation, using a motion capture system to measure the segments' positions and orientations. The controller is used to reshape the arm into configurations that are achieved through combinations of bending and extension deformations in 3D space. Although the possible deformations are limited for this robot platform, our study demonstrates the potential for implementing the control approach on a wide range of continuum robots in practice. The resulting tracking performance indicates the effectiveness of the controller and the accuracy of the simulated Cosserat rod model. 

Flying robots can exploit perching abilities to position themselves on strategically-chosen locations and monitor the areas of interest from a critical vantage point. Moreover, they can significantly extend their battery life by turning off the propulsion systems when carrying out a surveillance mission. However, unknown disturbances arise from the physical interactions between the robot and the object, making it challenging to stabilize the robot during perching. In this paper, we present a Whole-body Grasping and Perching (WHOPPEr) Drone, which is capable of fast and robust perching by utilizing its entire body as the grasper in lieu of an add-on grasper. We first present the design concept, parameter selection and characterization of the novel whole-body grasping drone. Next, we analyze the grasping ability of the morphing chassis and present an aerodynamic analysis for the effect of motor thrust on the compliant arm. We finally demonstrate, via real-time experiments, the performance of WHOPPEr in autonomous perching and payload delivery tasks. 

Collecting gait data and providing haptic feedback are essential for the safety and efficiency of robot-based rehabilitation. However, readily available devices that can perform both are scarce. This work presents a novel method for mutual sensing and haptic feedback, through the development of an inflatable soft haptic sensor (ISHASE). The design, modeling, and characterization of ISHASE are discussed. Four ISHASEs are embedded in the insole of a shoe to measure ground reaction forces and provide haptic feedback. Four participants were recruited to evaluate the performance of ISHASE as a sensor and haptic device. Experimental results indicate that ISHASE can accurately estimate user’s ground reaction forces while walking, with a maximum and a minimum accuracy of 91% and 85%, respectively. Haptic feedback was delivered to four different locations under the foot, and users could identify the location with an average 92% accuracy. A case study that exemplifies a rehabilitation scenario is presented to demonstrate ISHASE’s usefulness for mutual sensing and haptic feedback.

 

Current aerial robots demonstrate limited interaction capabilities in unstructured environments when compared with their biological counterparts. Some examples include their inability to tolerate collisions and to successfully land or perch on objects of unknown shapes, sizes, and texture. Efforts to include compliance have introduced designs that incorporate external mechanical impact protection at the cost of reduced agility and flight time due to the added weight. In this work, we propose and develop a lightweight, inflatable, soft-bodied aerial robot (SoBAR) that can pneumatically vary its body stiffness to achieve intrinsic collision resilience. Unlike the conventional rigid aerial robots, SoBAR successfully demonstrates its ability to repeatedly endure and recover from collisions in various directions, not only limited to in-plane ones. Furthermore, we exploit its capabilities to demonstrate perching where the three-dimensional collision resilience helps in improving the perching success rates. We also augment SoBAR with a novel hybrid fabric-based bistable (HFB) grasper that can utilize impact energies to perform contact-reactive grasping through rapid shape conforming abilities. We exhaustively study and offer insights into the collision resilience, impact absorption, and manipulation capabilities of SoBAR with the HFB grasper. Finally, we compare the performance of conventional aerial robots with the SoBAR through collision characterizations, grasping identifications, and experimental validations of collision resilience and perching in various scenarios and on differently shaped objects. 

Finding Nash equilibrial policies for two-player differential games requires solving Hamilton-Jacobi-Isaacs (HJI) PDEs. Self-supervised learning has been used to approximate solutions of such PDEs while circumventing the curse of dimensionality. However, this method fails to learn discontinuous PDE solutions due to its sampling nature, leading to poor safety performance of the resulting controllers in robotics applications when player rewards are discontinuous. This paper investigates two potential solutions to this problem: a hybrid method that leverages both supervised Nash equilibria and the HJI PDE, and a value-hardening method where a sequence of HJIs are solved with a gradually hardening reward. We compare these solutions using the resulting generalization and safety performance in two vehicle interaction simulation studies with 5D and 9D state spaces, respectively. Results show that with informative supervision (e.g., collision and near-collision demonstrations) and the low cost of self-supervised learning, the hybrid method achieves better safety performance than the supervised, self-supervised, and value hardening approaches on equal computational budget. Value hardening fails to generalize in the higher-dimensional case without informative supervision. Lastly, we show that the neural activation function needs to be continuously differentiable for learning PDEs and its choice can be case dependent.