Ensuring robot safety in complex environments is a difficult task due to actuation limits, such as torque bounds. This paper presents a safety-critical control framework that leverages learning-based switching between multiple backup controllers to formally guarantee safety under bounded control inputs while satisfying driver intention. By leveraging backup controllers designed to uphold safety and input constraints, backup control barrier functions (BCBFs) construct implicitly defined control invariant sets via a feasible quadratic program (QP). However, BCBF performance largely depends on the design and conservativeness of the chosen backup controller, especially in our setting of human-driven vehicles in complex, e.g, off-road, conditions. While conservativeness can be reduced by using multiple backup controllers, determining when to switch is an open problem. Consequently, we develop a broadcast scheme that estimates driver intention and integrates BCBFs with multiple backup strategies for human-robot interaction. An LSTM classifier uses data inputs from the robot, human, and safety algorithms to continually choose a backup controller in real-time. We demonstrate our method’s efficacy on a dualtrack robot in obstacle avoidance scenarios. Our framework guarantees robot safety while adhering to driver intention.

PARSEC (Payload Anchoring Robotic System for the Exploration of Cliffs) is an autonomy-equipped aerial manipulator that can deploy self-anchoring payloads on rocky vertical surfaces. It consists of a hexacopter and a two Degrees of Freedom (2 DoF) mass balancing manipulator, which can autonomously deploy a self-anchoring payload from its custom end-effector. The payload anchors itself via an actuated microspine gripper. Payload sensor data is wirelessly transmitted to the primary vehicle during and after deployment. A novel state machine controls the four-stage PARSEC deployment process. First, the rotorcraft brings the payload into contact with the surface and applies a constant 6 N normal force through a feedback control loop to preload the payload microspine gripper. Second, while the rotorcraft maintains the constant normal force, the gripper is commanded to close until engagement with the surface is confirmed through the current feedback sensing. Then, the aerial manipulator pulls with 5 N force on the anchored payload to ensure a secure grip before releasing the package and flying away. We present experimental validation of a successful deployment of a 430 g payload on a vertical vesicular basalt surface.

Nonlinear dynamical effects are crucial to the operation of many agile robotic systems. Koopman-based model learning methods can capture these nonlinear dynamical system effects in higher dimensional lifted bilinear models that are amenable to optimal control. However, standard methods that lift the system state using a fixed function dictionary before model learning result in high dimensional models that are intractable for real time control. This paper presents a novel method that jointly learns a function dictionary and lifted bilinear model purely from data by incorporating the Koopman model in a neural network architecture. Nonlinear MPC design utilizing the learned model can be performed readily. We experimentally realized this method on a multirotor drone for agile trajectory tracking at low altitudes where the aerodynamic ground effect influences the system's behavior. Experimental results demonstrate that the learning-based controller achieves similar performance as a nonlinear MPC based on a nominal dynamics model in medium altitude. However, our learning-based system can reliably track trajectories in near-ground flight regimes while the nominal controller crashes due to unmodeled dynamical effects that are captured by our method.