Autonomous obstacle avoidance for fixed-wing unmanned aerial vehicles

Abstract

This paper investigates a method for autonomous obstacle avoidance for fixed-wing unmanned aerial vehicles (UAVs), utilising potential fluid flow theory. The obstacle avoidance algorithm needs only compute the instantaneous local potential velocity vector, which is passed to the flight control laws as a direction command. The approach is reactive, and can readily accommodate real-time changes in obstacle information. UAV manoeuvring constraints on turning or pull-up radii, are accounted for by approximating obstacles by bounding rectangles, with wedges added to their front and back to shape the resulting fluid pathlines. It is shown that the resulting potential flow velocity field is completely determined by the obstacle field geometry, allowing one to determine a non-dimensional relationship between obstacle added wedge-length and the corresponding minimum pathline radius of curvature, which can then be readily scaled in on-board implementation. The efficacy of the proposed approach has been demonstrated numerically with an Aerosonde UAV model.

Hybrid Planning and Control for Multiple Fixed-Wing Aircraft under Input Constraints

“…where F i1 is given by (9) with p ob (i) = r gitemp and r ob (i) = r c . The gain k v5 (t s ) is chosen so that u i is continuous at the time of switching:…”

Guidance law for intercepting target with multiple no-fly zone constraints

“…During the last decades, many works have been carried out on the design of guidance laws with NFZ constraints, which can be classified into two types: offline path planning methods (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17) and online guidance laws (18)(19)(20)(21)(22)(23)(24) . Some off-line path planning methods are based on a series of waypoints and then employ path search algorithms to find a feasible trajectory by connecting proper waypoints, such as A* search algorithm, artificial bee colony algorithm (7) , bat algorithm (8) , heuristic algorithm (9) , core paths graph algorithm (10) , Newton iteration scheme (11) or dynamic programming (12) .…”

“…During the last decades, many works have been carried out on the design of guidance laws with NFZ constraints, which can be classified into two types: offline path planning methods (7–17) and online guidance laws (18–24) .…”

“…In one kind, the target is assumed to generate an attractive force on the missile whereas the NFZs are assumed to exert repulsive forces (22,23) . In the other kind, the NFZs are assumed to generate a potential velocity vector field such that the missile can avoid the NFZs by following a trajectory identical to that of a fluid particle in the same flow field (24) . In the presence of multiple NFZs, the Artificial Potential Field Methods (APFM) are likely to obtain a curved trajectory due to the difficulty of balancing the coefficients of different forces.…”

Dynamic path planning based on improved boundary value problem for unmanned aerial vehicle

“…Another common feature of the above methods is that the environment should be modeled in grid, and unreasonable grid model will result in large curvature of path which is not suitable for UAV [8]. On the contrary, potential field method has a strong advantage in the smoothness of path.…”