Obstacle Detection and Avoidance Device for Multirotor UAVs through interface with Pixhawk Flight Controller

Gonzalez, Jessenia; Chávez, Alfredo; Paredes, Juan Augusto; Saito, Carlos

doi:10.1109/coase.2018.8560370

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…Algorithms involved in image processing can be data-heavy that the flight controller cannot handle while doing flight stabilization. Some applications are seen in Hu [16] and Gonzalez [17], where the former made use of a Pixhawk 1 with a RealSense camera, and the latter made use of a Pixhawk 1 with an Odroid and Lidar both for obstacle avoidance. Both were made to avoid simple obstacles through a low-cost system.…”

Section: Introductionmentioning

confidence: 99%

A Novel Flight Controller Design for Modular Applications

Say

Chua

Sybingco

et al. 2022

AEJ

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A local flight controller was developed for modular applications based on the Pixhawk 1 flight controller with modifications to accommodate companion computer provision for future integration. This design is made for future modular applications, but for now, the functionality and performance of the local flight controller were tested and compared to the Pixhawk flight controller. A simple 3D printed enclosure was made to house the local flight controller for easier mounting on UAV frames. To compare the performance of the developed controller, two setups were made: local flight controller and Pixhawk 1 on separate quadcopter frames, and local flight controller and Pixhawk Cube on separate fixed-wing frames. The flight controllers made use of the Ardupilot firmware, specifically ArduCopter and ArduPlane in conducting the flight tests. Auto flight mode was used to have autonomous flights which were then used to compare the flight data between test setups. The desired position and actual position were compared for each flight controller, and their differences with the other flight controllers were compared to see the variation between the different controllers used. After analyzing the data, the local flight controller developed was proven able to produce comparable results with the Pixhawk flight controllers. The percent difference between the mean values of the Pixhawk 1 and the local flight controller were 3.7064% and 8.6128% using the quadcopter frame for Position X and Y, respectively, while for the Pixhawk Cube and the local flight controller, the values were 12.6866% and 1.1045% using the fixed-wing frame, respectively.

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Section: Introductionmentioning

confidence: 99%

A Novel Flight Controller Design for Modular Applications

Say

Chua

Sybingco

et al. 2022

AEJ

View full text Add to dashboard Cite

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Evaluation of Possible Flight Strategies for Close Object Evasion from Bumblebee Experiments

Thoma

Fisher

Bertrand

et al. 2020

Biomimetic and Biohybrid Systems

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Towards a Multi-Pixel Time-of-Flight Indoor Navigation System for Nano-Drone Applications

Niculescu

Muller

Ostovar

et al. 2022

2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

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Perceiving and mapping the surroundings are essential for enabling autonomous navigation in any robotic platform. The algorithm class that enables accurate mapping while correcting the odometry errors present in most robotics systems is Simultaneous Localization and Mapping (SLAM). Today, fully onboard mapping is only achievable on robotic platforms that can host high-wattage processors, mainly due to the significant computational load and memory demands required for executing SLAM algorithms. For this reason, pocketsize hardware-constrained robots offload the execution of SLAM to external infrastructures. To address the challenge of enabling SLAM algorithms on resource-constrained processors, this paper proposes NanoSLAM, a lightweight and optimized end-to-end SLAM approach specifically designed to operate on centimetersize robots at a power budget of only 87.9 mW. We demonstrate the mapping capabilities in real-world scenarios and deploy NanoSLAM on a nano-drone weighing 44 g and equipped with a novel commercial RISC-V low-power parallel processor called GAP9. The algorithm is designed to leverage the parallel capabilities of the RISC-V processing cores and enables mapping of a general environment with an accuracy of 4.5 cm and an end-to-end execution time of less than 250 ms.

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Obstacle Detection and Avoidance Device for Multirotor UAVs through interface with Pixhawk Flight Controller

Cited by 4 publications

References 7 publications

A Novel Flight Controller Design for Modular Applications

A Novel Flight Controller Design for Modular Applications

Evaluation of Possible Flight Strategies for Close Object Evasion from Bumblebee Experiments

Towards a Multi-Pixel Time-of-Flight Indoor Navigation System for Nano-Drone Applications

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