6.1 Conclusions童装怎么拿货
Autonomous control for small UAVs impos vere restrictions on the control algorithmdevelopment, stemming from the limitations impod by the on-board hardwareand the requirement for on-line implementation. In this thesis we have propod 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 comprid of veral 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 validatedthe developments in realistic non-trivial scenarios.
In Chapter 2 we propod a multiresolution path planning algorithm. The algorithmcomputes at each step a multiresolution reprentation of the environment usingthe fast lifting wavelet transform. The main idea is to employ high resolution cloto th
e agent (where is needed most), and a coar resolution at large distances fromthe current location of the agent. It has been shown that the propod multiresolutionpath planning algorithm provides an on-line path solution which is most reliableclo 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好看的ppt背景图片 planning algorithm is scalable and can be tailored to the available computational单词记忆法 resources of the agent.
The on-line path smoothing algorithm incorporating the path templates is prentedin Chapter 3. The path templates are comprid of a t of B-spline curves,which have been obtained from solving the off-line optimization problem subject tothe channel constraints. The channel is cloly related to the obstacle-free high resolutioncells over the path quence 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 p
ath quence 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 clod-loop dynamics. The propod path followingcontrol algorithm is validated through a high-fidelity 6-DOF simulation of a
fixed-wing UAV using a realistic nsor measurement, which verifies the applicabilityof the propod algorithm to the actual UAV.
Finally, the complete hierarchical path control algorithm propod 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 propod hierarchical path control law has been successfully applied for pathcontrol of a small UAV equipped with an autopilot that has limited computationalwhom resources.
6.2 Future Rearch
In this ction, veral possible extensions of the work prented in this thesis are
outlined.
6.2.1 Reusable graph structure The propod path planning algorithm involves calculating the multiresolution cell机器人翻译 decomposition and the corresponding graph structure at each of iteration. Hence, the hungaryconnectivity graph G(t) changes as the agent proceeds to
ward the goal. Subquently, let x 2 W be a state (location) which corresponds to nodes of two distinct graphs as follows
By the respective A_ arch on tho 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 prented that maintaining a visited t 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, suppo that we could employ a unified graph structure over the entire iteration, which retains the information from the previous arch. Similar to the D_-lite path planning algorithm, the incremental arch 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_ ericclaptonor Dsheetal_-lite algorithms where a uniform graph structure is employed, a challenge lies in building the unified graph structure from a multir
英文书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 evolveitlf as the agent moves, while updating nodes and edges associated with the multiresolutioncell decomposition from the FLWT. If this is the ca, we might be ableto adapt the propod path planning algorithm to an incremental arch algorithm, hence taking advantages of both the efficient multiresolution connectivity (due tothe FLWT) and the fast computation (due to the incremental arch by using the previous information).
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