Online safety verification of trajectories for unmanned flight with offline computed robust invariant sets

Abstract

Abstract-We address the problem of verifying motion plans for aerial robots in uncertain and partially-known environments. Thereby, the initial state of the robot is uncertain due to errors from the state estimation and the motion is uncertain due to wind disturbances and control errors caused by sensor noise. Since the environment is perceived at runtime, the verification of partial motion plans must be performed online (i.e. during operation) to ensure safety within the planning horizon and beyond. This is achieved by efficiently generating robust control invariant sets based on so-called loiter circles, where the position of the aerial robot follows a circular pattern. Verification of aerial robots is challenging due to the nonlinearity of their dynamics, the high dimensionality of their state space, and their potentially high velocities.We use novel techniques from reachability analysis to overcome those challenges. In order to ensure that the robot never finds itself in a situation for which no safe maneuver exists, we provide a technique that ensures safety of aerial robots beyond the planning horizon. Our method is applicable to all kinds of robotic systems that follow reference trajectories, such as bipedal robotic walking, robotic manipulators, automated vehicles, and the like. We evaluate our method by simulations of high speed helicopter flights.

Enhancing motion safety by identifying safety-critical passageways

“…Invariant sets are also used for safety verification. For instance, CIS are used to verify the safety of unmanned aerial vehicles (UAVs) [23], [24] or for safe controller design [25]. In combination with reachability analysis, invariant sets are used to verify the safety of adaptive cruise control systems [26], [27] or for predicitive threat assessment [28].…”

Safe, Aggressive Quadrotor Flight via Reachability-Based Trajectory Design

“…The feedback gains and rotor speed saturation parameters are reported in Table I. Note that, by including feedforward terms for angular acceleration and fulfilling other mild assumptions, one can modify (11) to provably asymptotically drive tracking error to zero as time tends to infinity for any particular reference trajectory [1]; however, since we are planning in a recedinghorizon way, we find that (11) tracks trajectories well over the time horizon T when commanding speeds up to v max = 5 m/s and |κ pk − κ v | ≤ 3 m/s as in (7). We express this notion of "tracking well" mathematically in the following subsections.…”

“…Now, we construct each element D ( j) ∈ D so that we can approximate E ( j) while only measuring e x at a finite number of points in D ( j) . Recall that, by Proposition 8, for a given desired trajectory, the tracking error at any time is maximized at the ends of an interval of possible initial speeds, since the feedback law u k given by (11) is as in the proposition when R is fixed. Therefore, we construct each D ( j) ⊂ T × K v using intervals:…”

UAV Fleet Mobility Model with Multiple Pheromones for Tracking Moving Observation Targets

“…However most existing works consider either centralised methods or distributed ones but using offline pre-computation and online decision taking [6,7].…”