Bv
B.J. van Maris
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Small drones increasingly operate indoors, where GPS is unavailable and the heavier sensors normally used for indoor positioning exceed a micro-drone's weight, size, and power budget. Visible light is a practical alternative, because the spaces a drone operates in are already lit. An ordinary lamp can be modulated to act as a navigation beacon, while the drone carries only a small light sensor to detect it. Prior work by Huang et al. navigates a micro-drone toward a single such beacon, recovering the direction of the light from a ring of photodiodes, but it keeps no record of the light field. It therefore cannot distinguish one beacon from another, nor route around an obstacle that blocks the light.
This thesis gives a weight-, size-, and power-constrained micro-drone the navigation those methods lacked, using only on-board light sensing. We design a fused controller that combines two directions. The first is the bearing toward the beacon, read from a ring of photodiodes. The second is a gradient, which the drone estimates from a map of the light field that it builds as it flies. The sensing runs on a custom light-sensing board that fits well within a micro-drone's payload budget. A signal pipeline on the drone then recovers each beacon from the board's noisy output through a frequency analysis, and reports a confidence measure for each reading. We validate the approach in a Webots simulation and in real flight. The drone reaches modulated beacons forming a predetermined path, and routes around an obstacle, using its stored map of the light field when the obstacle blocks the beacon. ...
This thesis gives a weight-, size-, and power-constrained micro-drone the navigation those methods lacked, using only on-board light sensing. We design a fused controller that combines two directions. The first is the bearing toward the beacon, read from a ring of photodiodes. The second is a gradient, which the drone estimates from a map of the light field that it builds as it flies. The sensing runs on a custom light-sensing board that fits well within a micro-drone's payload budget. A signal pipeline on the drone then recovers each beacon from the board's noisy output through a frequency analysis, and reports a confidence measure for each reading. We validate the approach in a Webots simulation and in real flight. The drone reaches modulated beacons forming a predetermined path, and routes around an obstacle, using its stored map of the light field when the obstacle blocks the beacon. ...
Small drones increasingly operate indoors, where GPS is unavailable and the heavier sensors normally used for indoor positioning exceed a micro-drone's weight, size, and power budget. Visible light is a practical alternative, because the spaces a drone operates in are already lit. An ordinary lamp can be modulated to act as a navigation beacon, while the drone carries only a small light sensor to detect it. Prior work by Huang et al. navigates a micro-drone toward a single such beacon, recovering the direction of the light from a ring of photodiodes, but it keeps no record of the light field. It therefore cannot distinguish one beacon from another, nor route around an obstacle that blocks the light.
This thesis gives a weight-, size-, and power-constrained micro-drone the navigation those methods lacked, using only on-board light sensing. We design a fused controller that combines two directions. The first is the bearing toward the beacon, read from a ring of photodiodes. The second is a gradient, which the drone estimates from a map of the light field that it builds as it flies. The sensing runs on a custom light-sensing board that fits well within a micro-drone's payload budget. A signal pipeline on the drone then recovers each beacon from the board's noisy output through a frequency analysis, and reports a confidence measure for each reading. We validate the approach in a Webots simulation and in real flight. The drone reaches modulated beacons forming a predetermined path, and routes around an obstacle, using its stored map of the light field when the obstacle blocks the beacon.
This thesis gives a weight-, size-, and power-constrained micro-drone the navigation those methods lacked, using only on-board light sensing. We design a fused controller that combines two directions. The first is the bearing toward the beacon, read from a ring of photodiodes. The second is a gradient, which the drone estimates from a map of the light field that it builds as it flies. The sensing runs on a custom light-sensing board that fits well within a micro-drone's payload budget. A signal pipeline on the drone then recovers each beacon from the board's noisy output through a frequency analysis, and reports a confidence measure for each reading. We validate the approach in a Webots simulation and in real flight. The drone reaches modulated beacons forming a predetermined path, and routes around an obstacle, using its stored map of the light field when the obstacle blocks the beacon.
Landmarks in Planning
Using landmarks as Intermediary Golas or as a Pseudo-Heuristic
Algorithmic planners occasionally waste effort and thus computing time trying to solve certain tasks, as they often lack the human ability to recognize essential paths. These essential paths, termed landmarks, are vital for optimizing planning processes. This study revisits landmark-based planning methods introduced by Richter, Helmert, and Westphal in their 2008 paper, adapting and implementing them within a different framework, SymbolicPlanners, using the Julia programming language. The primary research question explores the performance of using landmarks as intermediary goals and pseudo-heuristics in the SymbolicPlanner framework. Sub-questions delve into the effectiveness of specific planning strategies, such as A∗ Planner with GoalCount and HAdd heuristics, as well as planners utilizing landmarks. Evaluation over diverse domains reveals that LMLocal and LMLocalSmart outperform the basic GoalCount
heuristic and are on par with the HAdd heuristic. LMCount, despite solving fewer instances, exhibits speed improvements over GoalCount in the instances that they both solve. Discussion highlights limitations, such as the non-exhaustive interference check in LMLocalSmart and limiting factors in the SymbolicPlanner framework. ...
heuristic and are on par with the HAdd heuristic. LMCount, despite solving fewer instances, exhibits speed improvements over GoalCount in the instances that they both solve. Discussion highlights limitations, such as the non-exhaustive interference check in LMLocalSmart and limiting factors in the SymbolicPlanner framework. ...
Algorithmic planners occasionally waste effort and thus computing time trying to solve certain tasks, as they often lack the human ability to recognize essential paths. These essential paths, termed landmarks, are vital for optimizing planning processes. This study revisits landmark-based planning methods introduced by Richter, Helmert, and Westphal in their 2008 paper, adapting and implementing them within a different framework, SymbolicPlanners, using the Julia programming language. The primary research question explores the performance of using landmarks as intermediary goals and pseudo-heuristics in the SymbolicPlanner framework. Sub-questions delve into the effectiveness of specific planning strategies, such as A∗ Planner with GoalCount and HAdd heuristics, as well as planners utilizing landmarks. Evaluation over diverse domains reveals that LMLocal and LMLocalSmart outperform the basic GoalCount
heuristic and are on par with the HAdd heuristic. LMCount, despite solving fewer instances, exhibits speed improvements over GoalCount in the instances that they both solve. Discussion highlights limitations, such as the non-exhaustive interference check in LMLocalSmart and limiting factors in the SymbolicPlanner framework.
heuristic and are on par with the HAdd heuristic. LMCount, despite solving fewer instances, exhibits speed improvements over GoalCount in the instances that they both solve. Discussion highlights limitations, such as the non-exhaustive interference check in LMLocalSmart and limiting factors in the SymbolicPlanner framework.