Motion primitives and 3D path planning for fast flight through a forest

Abstract

This paper presents two families of motion primitives for enabling fast, agile flight through a dense obstacle field. The first family of primitives consists of a time-delay dependent 3D circular path between two points in space and the control inputs required to fly the path. In particular, the control inputs are calculated using algebraic equations which depend on the flight parameters and the location of the waypoint. Moreover, the transition between successive maneuver states, where each state is defined by a unique combination of constant control inputs, is modeled rigorously as an instantaneous switch between the two maneuver states following a time delay which is directly related to the agility of the robotic aircraft. The second family consists of aggressive turn-around (ATA) maneuvers which the robot uses to retreat from impenetrable pockets of obstacles. The ATA maneuver consists of an orchestrated sequence of three sets of constant control inputs. The duration of the first segment is used to optimize the ATA for the spatial constraints imposed by the turning volume. The motion primitives are validated experimentally and implemented in a simulated receding horizon control (RHC)-based motion planner. The paper concludes with inverse-design pointers derived from the primitives.

A Survey on Aerial Swarm Robotics

“…In [33], the full 6-DOF aircraft model is used with actuator time delays to compute optimal motion primitives and 3-D path planning for fast flight through a forest. It shows that a conventional 2-D Dubin's vehicle model, often times used for 2-D aircraft motion planning and swarm control, is not appropriate for aerial robots moving in 3-D. For the purpose of studying swarms of fixed-or flapping-wing aerial robots, it may suffice to model the aerial robots as point-masses (mass m) with velocity dynamics (speed V , climb angle γ, and heading χ) described by…”

“…This model is accurate under the assumption that the rotational dynamics (α and µ) are stable and converge rapidly to the commanded value. The 3-D aerial robot model can be used effectively to reduce the computational burden on a motion planning system and generate trajectories that are optimal, stable, and safe (i.e., with collision avoidance) [14], [33]. In [34], model-based control laws were derived, together with a collision-avoiding system, for a swarm of parafoilpayload systems.…”

Dynamic Inversion Control for Performing Herbst Manoeuver with Lateral Center-of-Gravity Offset

“…This help the aircraft rapidly reverse its flight direction and therefore, quickly return to the base after performing some combat mission [12][13] . Recently, a Herbst like manoeuver has been implemented for micro aerial vehicles also for smooth navigation through a forest 14 . To simulate the manoeuver using the nominal NDI control under zero CG offset, suitable bell-shaped desired profile are considered for AOA and bank angle for a total manoeuver time of 18 s starting at t = 5s.…”

Multirobot Coordination With Counting Temporal Logics

“…In this work, we assume that robot dynamics are modeled by action deterministic transition systems. This implies that, if the transition relation includes (s, s ), then there exists a controller that can steer a robot from state s ∈ S to state s ∈ S. Action deterministic transition systems could capture the behavior of many complex systems and could be obtained using abstraction methods [30], [39] or motion primitives [11], [21], [27]. Such abstract graph-based representations are commonly used for describing the behavior of robotic teams [3], [41].…”