Safe Navigation in Unmapped Environments for Robotic Systems with Input Constraints

This paper presents an approach for navigation and control in unmapped environments under input and state constraints using a composite control barrier function (CBF). We consider the scenario where real-time perception feedback (e.g., LiDAR) is used online to construct a local CBF that models local state constraints (e.g., local safety constraints such as obstacles) in the a priori unmapped environment. The approach employs a soft-maximum function to synthesize a single time-varying CBF from the N most recently obtained local CBFs. Next, the input constraints are transformed into controller-state constraints through the use of control dynamics. Then, we use a soft-minimum function to compose the input constraints with the time-varying CBF that models the a priori unmapped environment. This composition yields a single relaxed CBF, which is used in a constrained optimization to obtain an optimal control that satisfies the state and input constraints. The approach is validated through simulations of a nonholonomic ground robot that is equipped with LiDAR and navigates an unmapped environment. The robot successfully navigates the environment while avoiding the a priori unmapped obstacles and satisfying both speed and input constraints.

Time-Varying Soft-Maximum Barrier Functions for Safety in Unmapped and Dynamic Environments

We present a closed-form optimal feedback control method that ensures safety in an a prior unknown and potentially dynamic environment. This article considers the scenario where local perception data (e.g., LiDAR) is obtained periodically, and this data can be used to construct a local control barrier function (CBF) that models a local set that is safe for a period of time into the future. Then, we use a smooth time-varying soft-maximum function to compose the N most recently obtained local CBFs into a single barrier function that models an approximate union of the N most recently obtained local sets. This composite barrier function is used in a constrained quadratic optimization, which is solved in closed form to obtain a safe-and-optimal feedback control. We also apply the time-varying soft-maximum barrier function control to 2 robotic systems (nonholonomic ground robot with nonnegligible inertia, and quadrotor robot), where the objective is to navigate an a priori unknown environment safely and reach a target destination. In these applications, we present a simple approach to generate local CBFs from periodically obtained perception data.

Control Barrier Function Based UAV Safety Controller in Autonomous Airborne Tracking and Following Systems

Safe operations of UAVs are of paramount importance for various mission-critical and safety-critical UAV applications. In context of airborne target tracking and following, UAVs need to track a flying target avoiding collision and also closely follow its trajectory. The safety situation becomes critical and more complex when the flying target is non-cooperative and has erratic movements. This paper proposes a method for collision avoidance in an autonomous fast moving dynamic quadrotor UAV tracking and following another target UAV. This is achieved by designing a safety controller that minimally modifies the control input from a trajectory tracking controller and guarantees safety. This method enables pairing our proposed safety controller with already existing flight controllers. Our safety controller uses a control barrier function based quadratic program (CBF-QP) to produce an optimal control input enabling safe operation while also follow the trajectory of the target closely. We implement our solution on AirSim simulator over PX4 flight controller and with numerical results, we validate our approach through several simulation experiments with multiple scenarios and trajectories.