6.1 Conclusions
Autonomous control for small UAVs imposes severe restrictions on the control algorithmdevelopment, stemming from the limitations imposed by the on-board hardwareand the requirement for on-line implementation. In this thesis we have proposed anew hierarchical control scheme for the navigation and guidance of a small UAV forobstacle avoidance. The multi-stage control hierarchy for a complete path control algorithmis comprised of several control steps: Top-level path planning, mid-level pathsmoothing, and bottom-level path following controls. In each stage of the control hierarchy,the limitation of the on-board computational resources has been taken intoaccount to come up with a practically feasible control solution. We have validatedthese developments in realistic non-trivial scenarios.
In Chapter 2 we proposed a multiresolution path planning algorithm. The algorithmcomputes at each step a multiresolution representation of the environment usingthe fast lifting wavelet transform. The main idea is to employ high resolution closeto th
e agent (where is needed most), and a coarse resolution at large distances fromthe current location of the agent. It has been shown that the proposed multiresolutionpath planning algorithm provides an on-line path solution which is most reliableclose to the agent, while ultimately reaching the goal. In addition, the connectivityrelationship of the corresponding multiresolution cell decomposition can be computed directly from the the approximation and detail coefficients of the FLWT. The path planning algorithm is scalable and can be tailored to the available computational resources of the agent.
The on-line path smoothing algorithm incorporating the path templates is presentedin Chapter 3. The path templates are comprised of a set of B-spline curves,which have been obtained from solving the off-line optimization problem subject tothe channel constraints. The channel is closely related to the obstacle-free high resolutioncells over the path sequence calculated from the high-level path planner. Theobstacle avoidance is implicitly dealt with since each B-spline curve is constrainedto stay inside the prescribed channel, thus avoiding obstacles outside the channel.By the affine invariance property of B-spline, each component in the B-spine pathtemplates can be adapted to the discrete path seque
nce obtained from the high-levelpath planner. We have shown that the smooth reference path over the entire pathcan be calculated on-line by utilizing the path templates and path stitching scheme. The simulation results with the D_-lite path planning algorithm validates the effectivenessof the on-line path smoothing algorithm. This approach has the advantageof minimal on-line computational cost since most of computations are done off-line.
In Chapter 4 a nonlinear path following control law has been developed for asmall fixed-wing UAV. The kinematic control law realizes cooperative path followingso that the motion of a virtual target is controlled by an extra control input to helpthe convergence of the error variables. We applied the backstepping to derive theroll command for a fixed-wing UAV from the heading rate command of the kinematiccontrol law. Furthermore, we applied parameter adaptation to compensate for theinaccurate time constant of the roll closed-loop dynamics. The proposed path followingcontrol algorithm is validated through a high-fidelity 6-DOF simulation of a
fixed-wing UAV using a realistic sensor measurement, which verifies the applicabilityof the proposed algorithm to the actual UAV.
Finally, the complete hierarchical path control algorithm proposed in this thesis isvalidated thorough a high-fidelity hardware-in-the-loop simulation environment usingthe actual hardware platform. From the simulation results, it has been demonstratedthat the proposed hierarchical path control law has been successfully applied for pathcontrol of a small UAV equipped with an autopilot that has limited computational resources.
  6.2 Future Research
In this section, several possible extensions of the work presented in this thesis are
transform中文翻译outlined.
6.2.1 Reusable graph structure The proposed path planning algorithm involves calculating the multiresolution cell decomposition and the corresponding graph structure at each of iteration. Hence, the connectivity graph G(t) changes as the agent proceeds to
ward the goal. Subsequently, let x 2 W be a state (location) which corresponds to nodes of two distinct graphs as follows
By the respective A_ search on those graphs, the agent might be rendered to visit x at different time steps of ti and tj , i 6= j. As a result, a cyclic loop with respect to x is formed for the agent to repeat this pathological loop, while never reaching the goal. Although it has been presented that maintaining a visited set might be a means of avoiding such pathological situations[142], it turns out to be a trial-and-error scheme is not a systemical approach. Rather, suppose that we could employ a unified graph structure over the entire iteration, which retains the information from the previous search. Similar to the D_-lite path planning algorithm, the incremental search over the graph by reusing the previous information results in not only overcoming the pathological situation but also reducing the computational time. In contrast to D_ or D_-lite algorithms where a uniform graph structure is employed, a challenge lies in building the unified graph structure from a multir
esolution cell decomposition. Specifically, it includes a dynamic, multiresolution scheme for constructing the graph connectivity between nodes at different levels. The unified graph structure will evolveitself as the agent moves, while updating nodes and edges associated with the multiresolutioncell decomposition from the FLWT. If this is the case, we might be ableto adapt the proposed path planning algorithm to an incremental search algorithm, hence taking advantages of both the efficient multiresolution connectivity (due tothe FLWT) and the fast computation (due to the incremental search by using the previous information).

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