S. Hamaza
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19 records found
1
Robots operating in confined or visually degraded environments require sensing modalities that provide reliable close-range feedback. Biologically inspired whisker sensors address this need: passive and lightweight, they encode contact geometry through time-varying signals. Spiking neural networks (SNNs) form a natural processing counterpart — also biologically motivated, they perform sparse, event-driven computation that mirrors neural processing and promises energy-efficient inference on neuromorphic hardware. Despite this complementary fit, training data for neuromorphic tactile sensing remains scarce, and fully spiking architectures have not been evaluated on whisker-based classification, leaving their practical viability an open question.
To address this gap, we collected two tactile datasets using a 10-whisker MEMS-based sensor array swept across three object geometries under varying contact conditions. A compact fully spiking network with 901 parameters achieved 100% test accuracy, outperforming larger spiking architectures; classification was reliable from as few as 20 timesteps of contact, and raw pressure signals alone were sufficient. Generalization experiments showed that sweep speed had minimal effect on performance, while indentation depth and sweep direction introduced larger domain shifts. Compared to a structurally equivalent non-spiking baseline, the spiking model matched classification accuracy with an estimated 9.5% reduction in overall inference energy under analog input and a potential 95% reduction under spike-encoded input.
These results demonstrate the feasibility of a fully bio-inspired pipeline — from biomimetic whisker sensing to spiking neural inference — for tactile object classification. ...
To address this gap, we collected two tactile datasets using a 10-whisker MEMS-based sensor array swept across three object geometries under varying contact conditions. A compact fully spiking network with 901 parameters achieved 100% test accuracy, outperforming larger spiking architectures; classification was reliable from as few as 20 timesteps of contact, and raw pressure signals alone were sufficient. Generalization experiments showed that sweep speed had minimal effect on performance, while indentation depth and sweep direction introduced larger domain shifts. Compared to a structurally equivalent non-spiking baseline, the spiking model matched classification accuracy with an estimated 9.5% reduction in overall inference energy under analog input and a potential 95% reduction under spike-encoded input.
These results demonstrate the feasibility of a fully bio-inspired pipeline — from biomimetic whisker sensing to spiking neural inference — for tactile object classification. ...
Robots operating in confined or visually degraded environments require sensing modalities that provide reliable close-range feedback. Biologically inspired whisker sensors address this need: passive and lightweight, they encode contact geometry through time-varying signals. Spiking neural networks (SNNs) form a natural processing counterpart — also biologically motivated, they perform sparse, event-driven computation that mirrors neural processing and promises energy-efficient inference on neuromorphic hardware. Despite this complementary fit, training data for neuromorphic tactile sensing remains scarce, and fully spiking architectures have not been evaluated on whisker-based classification, leaving their practical viability an open question.
To address this gap, we collected two tactile datasets using a 10-whisker MEMS-based sensor array swept across three object geometries under varying contact conditions. A compact fully spiking network with 901 parameters achieved 100% test accuracy, outperforming larger spiking architectures; classification was reliable from as few as 20 timesteps of contact, and raw pressure signals alone were sufficient. Generalization experiments showed that sweep speed had minimal effect on performance, while indentation depth and sweep direction introduced larger domain shifts. Compared to a structurally equivalent non-spiking baseline, the spiking model matched classification accuracy with an estimated 9.5% reduction in overall inference energy under analog input and a potential 95% reduction under spike-encoded input.
These results demonstrate the feasibility of a fully bio-inspired pipeline — from biomimetic whisker sensing to spiking neural inference — for tactile object classification.
To address this gap, we collected two tactile datasets using a 10-whisker MEMS-based sensor array swept across three object geometries under varying contact conditions. A compact fully spiking network with 901 parameters achieved 100% test accuracy, outperforming larger spiking architectures; classification was reliable from as few as 20 timesteps of contact, and raw pressure signals alone were sufficient. Generalization experiments showed that sweep speed had minimal effect on performance, while indentation depth and sweep direction introduced larger domain shifts. Compared to a structurally equivalent non-spiking baseline, the spiking model matched classification accuracy with an estimated 9.5% reduction in overall inference energy under analog input and a potential 95% reduction under spike-encoded input.
These results demonstrate the feasibility of a fully bio-inspired pipeline — from biomimetic whisker sensing to spiking neural inference — for tactile object classification.
Master thesis
(2025)
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M.A. Elahi, S. Hamaza, E.J.J. Smeur, A. Bombelli, A. Bredenbeck, M.B.J. Brummelhuis
This thesis presents the design, development, and experimental validation of a perching drone equipped with an underactuated robotic gripper and tactile sensing for grasping onto structures. Perching extends drone endurance for applications such as long-term monitoring, while tactile sensing enables precise alignment when visual data is unreliable. A control strategy combining position-based control with tactile feedback is implemented using DIGIT tactile sensors for contact-aware adjustments. Two per-pixel inference models convert RGB images into tactile information: a sensitive contact model for binary contact detection and a depth reconstruction model that estimates surface normals, which is then used to determine the contact surface orientation. After outlier filtering, the depth model achieves a mean absolute error of 5.32° in orientation estimation. Experiments demonstrate reliable grasping with up to 12 cm of position error and successful correction of both position and orientation using simulated tactile input. These results highlight the potential of tactile-based strategies for robust aerial manipulation in uncertain environments.
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This thesis presents the design, development, and experimental validation of a perching drone equipped with an underactuated robotic gripper and tactile sensing for grasping onto structures. Perching extends drone endurance for applications such as long-term monitoring, while tactile sensing enables precise alignment when visual data is unreliable. A control strategy combining position-based control with tactile feedback is implemented using DIGIT tactile sensors for contact-aware adjustments. Two per-pixel inference models convert RGB images into tactile information: a sensitive contact model for binary contact detection and a depth reconstruction model that estimates surface normals, which is then used to determine the contact surface orientation. After outlier filtering, the depth model achieves a mean absolute error of 5.32° in orientation estimation. Experiments demonstrate reliable grasping with up to 12 cm of position error and successful correction of both position and orientation using simulated tactile input. These results highlight the potential of tactile-based strategies for robust aerial manipulation in uncertain environments.
Curupira
A dual-drone platform for biodiversity surveys in tropical rainforest environments
Bachelor thesis
(2025)
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A. Dima, C.E. Ochola, C.L. Visser, D. Raonic, E. MERGAERT, G.D.F. Schellekens, I.M. Dolman, J.M. Oppelaar, L. Ledeganck, Y. Cavalcante Tavares, S. Hamaza, Anandini Jayanthi, B.S. Dsouza, J.A. Melkert
Rainforests are estimated to shelter between 40% and 75% of Earth’s biodiversity, including countless endemic species that are vital to the ecosystem health. The large uncertainty in these estimates underscores the significant gaps in our knowledge of this biome, with numerous species yet to be discovered. Simultaneously, human-driven habitat destruction is causing species to disappear before they can ever be identified. This combination of limited knowledge and accelerating loss has transformed the effort to document rainforest biodiversity into a pressing ‘race against the clock’. Ongoing developments in drone technologies offer a promising solution for conducting large-scale surveys of rainforest biodiversity. To enable swift, sustainable and minimally invasive collection of high-quality biodiversity data in rainforest environments, Curupira was developed, an innovative system designed to enhance our understanding of species diversity and ecosystem health. Curupira features a coordinated pair of autonomous drones, working in tandem to conduct efficient, in-depth biodiversity surveys in tropical rainforests.
...
Rainforests are estimated to shelter between 40% and 75% of Earth’s biodiversity, including countless endemic species that are vital to the ecosystem health. The large uncertainty in these estimates underscores the significant gaps in our knowledge of this biome, with numerous species yet to be discovered. Simultaneously, human-driven habitat destruction is causing species to disappear before they can ever be identified. This combination of limited knowledge and accelerating loss has transformed the effort to document rainforest biodiversity into a pressing ‘race against the clock’. Ongoing developments in drone technologies offer a promising solution for conducting large-scale surveys of rainforest biodiversity. To enable swift, sustainable and minimally invasive collection of high-quality biodiversity data in rainforest environments, Curupira was developed, an innovative system designed to enhance our understanding of species diversity and ecosystem health. Curupira features a coordinated pair of autonomous drones, working in tandem to conduct efficient, in-depth biodiversity surveys in tropical rainforests.
This study explores the potential of biomimetic vibrissal sensors for tactile navigation in aerial robotics. Inspired by the sophisticated sensory system of rodents, we developed a non-intrusive, whisker-based tactile sensor system integrated into a drone platform. The system enables real-time detection and navigation through complex environments by emulating the tactile perception capabilities of natural whiskers. Our approach includes a novel platform design for easy sensor integration, a preprocessing solution to mitigate signal distortion as well as a simple static calibration set-up to estimate normal contact distances. The effectiveness of the system was validated through contour following tasks, where the whisker sensors provided feedback for precise navigation along surfaces with varying orientations. Results demonstrate that our system can estimate normal distances and wall orientations with sufficient accuracy, despite challenges such as lateral slip. This research highlights the potential of whisker-inspired sensors in enhancing the tactile sensing capabilities of aerial robots, offering significant advantages over traditional tactile sensors in terms of weight, power consumption, and operational flexibility in diverse environmental conditions.
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This study explores the potential of biomimetic vibrissal sensors for tactile navigation in aerial robotics. Inspired by the sophisticated sensory system of rodents, we developed a non-intrusive, whisker-based tactile sensor system integrated into a drone platform. The system enables real-time detection and navigation through complex environments by emulating the tactile perception capabilities of natural whiskers. Our approach includes a novel platform design for easy sensor integration, a preprocessing solution to mitigate signal distortion as well as a simple static calibration set-up to estimate normal contact distances. The effectiveness of the system was validated through contour following tasks, where the whisker sensors provided feedback for precise navigation along surfaces with varying orientations. Results demonstrate that our system can estimate normal distances and wall orientations with sufficient accuracy, despite challenges such as lateral slip. This research highlights the potential of whisker-inspired sensors in enhancing the tactile sensing capabilities of aerial robots, offering significant advantages over traditional tactile sensors in terms of weight, power consumption, and operational flexibility in diverse environmental conditions.
Climate change poses a serious threat to ecosystems and increases the need for accurate and rigorous monitoring of ecosystems. Current monitoring solutions are often bulky, expensive, and lack critical functionalities such as on-board inference capabilities, robust wireless connections, and a diverse sensor suite. Ecological monitoring projects often suffer from inefficiencies caused by the large time delays between collecting data and analyzing said data, as well as having to spend large amounts of time in the field setting up the sensors manually. This thesis addresses many of these issues by designing a sensor with an extensive sensor suite, robust wireless capabilities and an on-board audio classifier able to perform real-time inference. Furthermore, attention is paid to making the system extendable in the future and allow for potentially integrating the sensors with a drone delivery- and retrieval system. The system tests performed indicate that the system has great potential given more time to tweak some of its identified shortcomings.
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Climate change poses a serious threat to ecosystems and increases the need for accurate and rigorous monitoring of ecosystems. Current monitoring solutions are often bulky, expensive, and lack critical functionalities such as on-board inference capabilities, robust wireless connections, and a diverse sensor suite. Ecological monitoring projects often suffer from inefficiencies caused by the large time delays between collecting data and analyzing said data, as well as having to spend large amounts of time in the field setting up the sensors manually. This thesis addresses many of these issues by designing a sensor with an extensive sensor suite, robust wireless capabilities and an on-board audio classifier able to perform real-time inference. Furthermore, attention is paid to making the system extendable in the future and allow for potentially integrating the sensors with a drone delivery- and retrieval system. The system tests performed indicate that the system has great potential given more time to tweak some of its identified shortcomings.
Drones are increasingly used nowadays, primarily for visual inspection tasks facilitated by onboard cameras. The field of aerial manipulation tries to expand the capabilities of drones by attaching a manipulator, enabling physical interaction. Unfortunately, the usability of aerial manipulators is hindered by disturbances resulting from the movements of the manipulator. These disturbances, including reaction forces and a shifting centre of mass, not only affect manipulation accuracy but also pose safety risks by potentially destabilizing the drone. In this thesis, a design is presented that addresses this challenge by leveraging the theory of dynamic balance.
A new design approach of making a manipulator fly, instead of the common approach of mounting a manipulator arm to a drone was used. This new approach avoids interference with the drone's components, allowing to focus on the design of the manipulator arm. Furthermore, it made it possible to create a manipulator which can manipulate above, to the side and underneath itself. This makes the presented manipulator arm more versatile than common aerial manipulators whose workspace is mostly located only above or below the drone. The kinematics, workspace and balance conditions of the manipulator arm are presented. Furthermore, the design's workspace is optimised while the mass of the manipulator is minimized in a bilevel optimisation. Finally, the design is validated both by simulation and measurements performed with the built prototype.
The design presented is the first inherently fully dynamically balanced manipulator with omnidirectional workspace which can be used for aerial manipulation.
...
A new design approach of making a manipulator fly, instead of the common approach of mounting a manipulator arm to a drone was used. This new approach avoids interference with the drone's components, allowing to focus on the design of the manipulator arm. Furthermore, it made it possible to create a manipulator which can manipulate above, to the side and underneath itself. This makes the presented manipulator arm more versatile than common aerial manipulators whose workspace is mostly located only above or below the drone. The kinematics, workspace and balance conditions of the manipulator arm are presented. Furthermore, the design's workspace is optimised while the mass of the manipulator is minimized in a bilevel optimisation. Finally, the design is validated both by simulation and measurements performed with the built prototype.
The design presented is the first inherently fully dynamically balanced manipulator with omnidirectional workspace which can be used for aerial manipulation.
...
Drones are increasingly used nowadays, primarily for visual inspection tasks facilitated by onboard cameras. The field of aerial manipulation tries to expand the capabilities of drones by attaching a manipulator, enabling physical interaction. Unfortunately, the usability of aerial manipulators is hindered by disturbances resulting from the movements of the manipulator. These disturbances, including reaction forces and a shifting centre of mass, not only affect manipulation accuracy but also pose safety risks by potentially destabilizing the drone. In this thesis, a design is presented that addresses this challenge by leveraging the theory of dynamic balance.
A new design approach of making a manipulator fly, instead of the common approach of mounting a manipulator arm to a drone was used. This new approach avoids interference with the drone's components, allowing to focus on the design of the manipulator arm. Furthermore, it made it possible to create a manipulator which can manipulate above, to the side and underneath itself. This makes the presented manipulator arm more versatile than common aerial manipulators whose workspace is mostly located only above or below the drone. The kinematics, workspace and balance conditions of the manipulator arm are presented. Furthermore, the design's workspace is optimised while the mass of the manipulator is minimized in a bilevel optimisation. Finally, the design is validated both by simulation and measurements performed with the built prototype.
The design presented is the first inherently fully dynamically balanced manipulator with omnidirectional workspace which can be used for aerial manipulation.
A new design approach of making a manipulator fly, instead of the common approach of mounting a manipulator arm to a drone was used. This new approach avoids interference with the drone's components, allowing to focus on the design of the manipulator arm. Furthermore, it made it possible to create a manipulator which can manipulate above, to the side and underneath itself. This makes the presented manipulator arm more versatile than common aerial manipulators whose workspace is mostly located only above or below the drone. The kinematics, workspace and balance conditions of the manipulator arm are presented. Furthermore, the design's workspace is optimised while the mass of the manipulator is minimized in a bilevel optimisation. Finally, the design is validated both by simulation and measurements performed with the built prototype.
The design presented is the first inherently fully dynamically balanced manipulator with omnidirectional workspace which can be used for aerial manipulation.
This research proposes a novel reconfigurable and force-balanced aerial manipulator design for fast variable payload tasks. Its force-balancing properties allow for fast end-effector movements while minimizing disturbances introduced to the aerial platform. The manipulator is composed of three pantograph legs connecting the end-effector to the drone base. Each pantograph is equipped with two moving counter-masses that provide the balancing properties to the manipulator. The counter masses are moved by fast linear actuators allowing the manipulator to be force-balanced for different payloads. Extensive testing, performing end-effector trajectory tracking tasks, was performed both on a floating base setup and in flight. The results indicate that the manipulator significantly decreased the reaction forces transmitted to the base. Specifically, it achieved a 45% reduction when comparing the unbalanced and balanced configurations, and a 17% reduction when these configurations included a 53 [g] payload. The drone's position-tracking error during flight also improved, with reductions of 19% and 34% for the same two configurations, respectively.
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This research proposes a novel reconfigurable and force-balanced aerial manipulator design for fast variable payload tasks. Its force-balancing properties allow for fast end-effector movements while minimizing disturbances introduced to the aerial platform. The manipulator is composed of three pantograph legs connecting the end-effector to the drone base. Each pantograph is equipped with two moving counter-masses that provide the balancing properties to the manipulator. The counter masses are moved by fast linear actuators allowing the manipulator to be force-balanced for different payloads. Extensive testing, performing end-effector trajectory tracking tasks, was performed both on a floating base setup and in flight. The results indicate that the manipulator significantly decreased the reaction forces transmitted to the base. Specifically, it achieved a 45% reduction when comparing the unbalanced and balanced configurations, and a 17% reduction when these configurations included a 53 [g] payload. The drone's position-tracking error during flight also improved, with reductions of 19% and 34% for the same two configurations, respectively.
The effects of climate change put increasing strain on rainforests and their inhabitants, highlighting the demand for technological developments to aid in biodiversity monitoring and conservation efforts. This research work proposes a novel frame work for the autonomous placement of acoustic sensor networks on top of the rainforest canopy, using a quadrotor platform. In order to tackle the challenge posed by multirotors’ limited autonomy, an initial canopy exploration mission is considered and an exploration planner is developed to detect suitable locations on top of the rainforest canopy for sensor node deployment. Specifically, green detection and pointcloud projection is performed online and combined with our proposed sampling method to accomplish targeted exploration, towards the estimated location of detected green components. Flight experiments are conducted across multiple scenarios that mimic distinct rainforest features. The results validate the system architecture and demonstrate the effectiveness of our smart sampling method, laying the foundation for future autonomous sensor network exploration missions at larger-scale.
...
The effects of climate change put increasing strain on rainforests and their inhabitants, highlighting the demand for technological developments to aid in biodiversity monitoring and conservation efforts. This research work proposes a novel frame work for the autonomous placement of acoustic sensor networks on top of the rainforest canopy, using a quadrotor platform. In order to tackle the challenge posed by multirotors’ limited autonomy, an initial canopy exploration mission is considered and an exploration planner is developed to detect suitable locations on top of the rainforest canopy for sensor node deployment. Specifically, green detection and pointcloud projection is performed online and combined with our proposed sampling method to accomplish targeted exploration, towards the estimated location of detected green components. Flight experiments are conducted across multiple scenarios that mimic distinct rainforest features. The results validate the system architecture and demonstrate the effectiveness of our smart sampling method, laying the foundation for future autonomous sensor network exploration missions at larger-scale.
Low visibility in hard-to-reach environments, such as natural caves and tunnels, poses challenges for exploration due to the limited effectiveness of traditional sensors, such as cameras and distance sensors. Nature offers a solution to this problem embedded in the body of nocturnal animals in the form of vibrissal sensors, whiskers. In this study, we take inspiration from this principle and develop artificial biomimetic whiskers to aid quadrotors' navigation. Specifically, we design and control a whiskered drone for ceiling contour-following tasks, enabling tactile tracing of rugged ceilings while mitigating aerodynamic disturbances like the "ceiling effect". The proposed whiskered drone design allows for multiple whiskers in various orientations, providing real-time tactile feedback to guide the ceiling-following controller. Flight experiments validate the system's effectiveness in navigating both smooth and rugged ceilings, demonstrating the potential for whisker-based tactile navigation and future autonomy.
...
Low visibility in hard-to-reach environments, such as natural caves and tunnels, poses challenges for exploration due to the limited effectiveness of traditional sensors, such as cameras and distance sensors. Nature offers a solution to this problem embedded in the body of nocturnal animals in the form of vibrissal sensors, whiskers. In this study, we take inspiration from this principle and develop artificial biomimetic whiskers to aid quadrotors' navigation. Specifically, we design and control a whiskered drone for ceiling contour-following tasks, enabling tactile tracing of rugged ceilings while mitigating aerodynamic disturbances like the "ceiling effect". The proposed whiskered drone design allows for multiple whiskers in various orientations, providing real-time tactile feedback to guide the ceiling-following controller. Flight experiments validate the system's effectiveness in navigating both smooth and rugged ceilings, demonstrating the potential for whisker-based tactile navigation and future autonomy.
The design of aerial robots capable of perching poses significant challenges, from requiring pilots to master precise manoeuvres, to devising hardware and software capable of adapting to diverse perch structures and complex field environments. The Slapper drone presented in this paper tackles these challenges through three main innovations. First, a lightweight, vision-based system for autonomous perch detection using onboard flight hardware detects (imperfect) cylindrical objects found in both natural and artificial environments. Second, an onboard flight planning algorithm autonomously handles the detection, approach and perching flight phases, removing the need for a pilot. Third, a completely passive gripper utilises bistable shell structures to allow for perching on general long narrow features without any precise control inputs or power consumption. This design was successfully validated through both simulation and multiple indoor flights to result in reliable autonomous quadrotor perching in real-world environments.
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The design of aerial robots capable of perching poses significant challenges, from requiring pilots to master precise manoeuvres, to devising hardware and software capable of adapting to diverse perch structures and complex field environments. The Slapper drone presented in this paper tackles these challenges through three main innovations. First, a lightweight, vision-based system for autonomous perch detection using onboard flight hardware detects (imperfect) cylindrical objects found in both natural and artificial environments. Second, an onboard flight planning algorithm autonomously handles the detection, approach and perching flight phases, removing the need for a pilot. Third, a completely passive gripper utilises bistable shell structures to allow for perching on general long narrow features without any precise control inputs or power consumption. This design was successfully validated through both simulation and multiple indoor flights to result in reliable autonomous quadrotor perching in real-world environments.
Bachelor thesis
(2023)
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L.X.A. Ronsse, M. De Vusser, B.R.N. Seffer, S. Kumar, E.H.Q. Oosthoek, K.J. Krzyżanowski, J.F. Grobusch, E.M.E. van Zutphen, M.K. Heliński, R.J.N. Zwikker, S. Hamaza, C.A. Dransfeld, S.J.M. van Oosterom, K. Jigjid
Aerial physical interaction opens the door for many operations at height to be automatised using aerial robots. This research presents a novel manipulator design mounted on a traditional quadrotor, which utilises both mechanical and software compliance to perform physical interaction on vertical walls and overhanging surfaces, such as those found under bridges. A centralised impedance control scheme allows direct control of the end-effector pose without needing separate modes for free-flight and contact. A spring-loaded prismatic joint provides passive compliance while doubling as a force-feedback for the impedance controller through measuring the spring displacement. Simulation and flight experiments prove the feasibility and robustness of this approach for exchanging high forces at height, with a total of 44 successful experiments carried out in four sets. An average maximum force of 5.66 N or 19.3\% of the system's weight was achieved over one set of 11 experiments.
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Aerial physical interaction opens the door for many operations at height to be automatised using aerial robots. This research presents a novel manipulator design mounted on a traditional quadrotor, which utilises both mechanical and software compliance to perform physical interaction on vertical walls and overhanging surfaces, such as those found under bridges. A centralised impedance control scheme allows direct control of the end-effector pose without needing separate modes for free-flight and contact. A spring-loaded prismatic joint provides passive compliance while doubling as a force-feedback for the impedance controller through measuring the spring displacement. Simulation and flight experiments prove the feasibility and robustness of this approach for exchanging high forces at height, with a total of 44 successful experiments carried out in four sets. An average maximum force of 5.66 N or 19.3\% of the system's weight was achieved over one set of 11 experiments.
Aerial manipulators, characterized by their ability to actively engage with the environment, are gaining popularity for their versatility in performing diverse tasks.
This research focuses on augmenting the capabilities of aerial manipulators through the integration of tactile feedback, specifically employing a compliant bio-inspired three-fingered manipulator equipped with tactile capacitive sensors on each finger. The manipulator is affixed to a drone, enabling tactile-guided navigation for precise object localization, subsequent grasping, and perching. Additionally, a grasp evaluator assesses grasp quality, allowing the system to adapt by suggesting alternative grasp locations after an initial attempt is unsuccessful. A comparative analysis between the system’s performance using tactile feedback and open-loop perching/grasping in perching scenarios demonstrates that the grasp evaluator improves the perching success rate by 55%-point and increases the allowable object uncertainty by 0.14 [m]. These findings highlight the efficacy of this approach in advancing aerial manipulator capabilities. ...
This research focuses on augmenting the capabilities of aerial manipulators through the integration of tactile feedback, specifically employing a compliant bio-inspired three-fingered manipulator equipped with tactile capacitive sensors on each finger. The manipulator is affixed to a drone, enabling tactile-guided navigation for precise object localization, subsequent grasping, and perching. Additionally, a grasp evaluator assesses grasp quality, allowing the system to adapt by suggesting alternative grasp locations after an initial attempt is unsuccessful. A comparative analysis between the system’s performance using tactile feedback and open-loop perching/grasping in perching scenarios demonstrates that the grasp evaluator improves the perching success rate by 55%-point and increases the allowable object uncertainty by 0.14 [m]. These findings highlight the efficacy of this approach in advancing aerial manipulator capabilities. ...
Aerial manipulators, characterized by their ability to actively engage with the environment, are gaining popularity for their versatility in performing diverse tasks.
This research focuses on augmenting the capabilities of aerial manipulators through the integration of tactile feedback, specifically employing a compliant bio-inspired three-fingered manipulator equipped with tactile capacitive sensors on each finger. The manipulator is affixed to a drone, enabling tactile-guided navigation for precise object localization, subsequent grasping, and perching. Additionally, a grasp evaluator assesses grasp quality, allowing the system to adapt by suggesting alternative grasp locations after an initial attempt is unsuccessful. A comparative analysis between the system’s performance using tactile feedback and open-loop perching/grasping in perching scenarios demonstrates that the grasp evaluator improves the perching success rate by 55%-point and increases the allowable object uncertainty by 0.14 [m]. These findings highlight the efficacy of this approach in advancing aerial manipulator capabilities.
This research focuses on augmenting the capabilities of aerial manipulators through the integration of tactile feedback, specifically employing a compliant bio-inspired three-fingered manipulator equipped with tactile capacitive sensors on each finger. The manipulator is affixed to a drone, enabling tactile-guided navigation for precise object localization, subsequent grasping, and perching. Additionally, a grasp evaluator assesses grasp quality, allowing the system to adapt by suggesting alternative grasp locations after an initial attempt is unsuccessful. A comparative analysis between the system’s performance using tactile feedback and open-loop perching/grasping in perching scenarios demonstrates that the grasp evaluator improves the perching success rate by 55%-point and increases the allowable object uncertainty by 0.14 [m]. These findings highlight the efficacy of this approach in advancing aerial manipulator capabilities.
In this work, we present the ADAPT, a novel reconfigurable force-balanced parallel manipulator with pantograph legs for spatial motions applied underneath a drone. The reconfigurable aspect allows different motion-based 3-DoF operation modes like translational, rotational, mixed, planar without disassembly. For the purpose of this study, the manipulator is used in translation mode only. A kinematic model is developed and validated for the manipulator. The design and motion capabilities are also validated both by conducting dynamics simulations of a simplified model on MSC ADAMS, and experiments on the physical setup.
The force-balanced nature of this novel design decouples the motion of the manipulator’s end-effector from the base, zeroing the reaction forces, making this design ideally suited for aerial manipulation in unmanned aerial vehicles (UAVs) applications, or generic floating-base applications. ...
The force-balanced nature of this novel design decouples the motion of the manipulator’s end-effector from the base, zeroing the reaction forces, making this design ideally suited for aerial manipulation in unmanned aerial vehicles (UAVs) applications, or generic floating-base applications. ...
In this work, we present the ADAPT, a novel reconfigurable force-balanced parallel manipulator with pantograph legs for spatial motions applied underneath a drone. The reconfigurable aspect allows different motion-based 3-DoF operation modes like translational, rotational, mixed, planar without disassembly. For the purpose of this study, the manipulator is used in translation mode only. A kinematic model is developed and validated for the manipulator. The design and motion capabilities are also validated both by conducting dynamics simulations of a simplified model on MSC ADAMS, and experiments on the physical setup.
The force-balanced nature of this novel design decouples the motion of the manipulator’s end-effector from the base, zeroing the reaction forces, making this design ideally suited for aerial manipulation in unmanned aerial vehicles (UAVs) applications, or generic floating-base applications.
The force-balanced nature of this novel design decouples the motion of the manipulator’s end-effector from the base, zeroing the reaction forces, making this design ideally suited for aerial manipulation in unmanned aerial vehicles (UAVs) applications, or generic floating-base applications.
Flapping wing micro aerial vehicles (FWMAVs) are known for their flight agility and maneuverability. However, their in-gust flight performance and stability is still inferior to their biological counterparts. To this end, a simplified in-gust dynamic model, which could capture the main gust effects on FWMAVs, has been identified with real in-gust flights' data of a FWMAV, the Flapper Drone. Based on this model, an adaptive position and velocity controller was proposed with gain scheduling and implemented for in-gust flights under gust speeds up to 2.4 m/s. With this airflow-sensing based adaptive controller, the in-gust hovering stability of the Flapper Drone has been improved when the gust's intensity and frequency changes, comparing with the original fixed-gain cascaded PID controller case.
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Flapping wing micro aerial vehicles (FWMAVs) are known for their flight agility and maneuverability. However, their in-gust flight performance and stability is still inferior to their biological counterparts. To this end, a simplified in-gust dynamic model, which could capture the main gust effects on FWMAVs, has been identified with real in-gust flights' data of a FWMAV, the Flapper Drone. Based on this model, an adaptive position and velocity controller was proposed with gain scheduling and implemented for in-gust flights under gust speeds up to 2.4 m/s. With this airflow-sensing based adaptive controller, the in-gust hovering stability of the Flapper Drone has been improved when the gust's intensity and frequency changes, comparing with the original fixed-gain cascaded PID controller case.
In this thesis, we develop a novel aerial manipulator system with an omni-directional workspace. The system comprises of a quadrotor platform equipped with a rotating five-bar linkage and serves the purpose of demonstrating the ability to perform contour tracing tasks on complex shapes, whilst airborne. In order to remove the dependency on additional force sensors and keep the design lightweight, an onboard force estimation scheme is implemented based on the generalized momentum of the system, using the torque feedback from the manipulator's motors. The computed force estimate feeds in a position-based impedance controller with the purpose of maintaining continuous contact through the manipulator's end-effector as the system traces contours of unknown curved geometry. Results demonstrate the estimator's ability to track the applied forces, while the impedance controller shows adequate contour following. The preliminary results obtained on both stationery and flight experiments validate this approach and show potential for aerial contact inspections of more complex structures.
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In this thesis, we develop a novel aerial manipulator system with an omni-directional workspace. The system comprises of a quadrotor platform equipped with a rotating five-bar linkage and serves the purpose of demonstrating the ability to perform contour tracing tasks on complex shapes, whilst airborne. In order to remove the dependency on additional force sensors and keep the design lightweight, an onboard force estimation scheme is implemented based on the generalized momentum of the system, using the torque feedback from the manipulator's motors. The computed force estimate feeds in a position-based impedance controller with the purpose of maintaining continuous contact through the manipulator's end-effector as the system traces contours of unknown curved geometry. Results demonstrate the estimator's ability to track the applied forces, while the impedance controller shows adequate contour following. The preliminary results obtained on both stationery and flight experiments validate this approach and show potential for aerial contact inspections of more complex structures.
Bachelor thesis
(2022)
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M. Xausa, J.S. van der Saag, H.S. Hennecken, K. Aristodemou, L. Naets, V.D.F. Morizon, Šimon Čajági, L.H. Uptmoor, T. Potočnik, S.T.A. Van den Abeele, C.A. Dransfeld, S. Hamaza, S. Hwang, N.C. Gomes de Paula
Bachelor thesis
(2022)
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V.D. van Deursen, M.T. Hitzerd, Wikash Chitoe, Pooh Laohamethanee, J.A. Evans, N.F. Gebhardt, A.C. Papuc, L. Madi, Dries Allaerts, R. Saathof, M. Rehbein, S. Hamaza
The goal of this report is to outline the sub-system design of the local sensing system chosen as the final concept in [1], to satisfy the mission need statement: measure the atmospheric conditions with full three-dimensional coverage of a wind farm to optimize its operational performance and control. This statement is derived from the need to improve the control and performance of wind farms through more informed processes and decisions, a task that meteorological masts would usually take on. However, the providable coverage is very low in comparison to the one a UAV based system could provide. UAVs have the potential to significantly increase the measurement coverage around an entire wind farm and in turn return to the user more valuable data. To approach the finding of a solution to this problem, the project was divided into four: planning, concept definition, concept exploration and detailed design. From the first two phases came unique concepts exploring remote and local sensing options, combined with a range of UAV types including hybrid, fixed-wing and rotor. Through a detailed trade-off process and sensitivity analysis, the agreed upon final solution came to be a local sensing concept that makes use of many hybrid drones. In the fourth and final phase, where we now find ourselves, the detailed concept is unpacked and designed into a marketable system that is capable of satisfying the underlying MNS. In this stage the design was split into three design groups: UAV design, ground station design, swarm design.
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The goal of this report is to outline the sub-system design of the local sensing system chosen as the final concept in [1], to satisfy the mission need statement: measure the atmospheric conditions with full three-dimensional coverage of a wind farm to optimize its operational performance and control. This statement is derived from the need to improve the control and performance of wind farms through more informed processes and decisions, a task that meteorological masts would usually take on. However, the providable coverage is very low in comparison to the one a UAV based system could provide. UAVs have the potential to significantly increase the measurement coverage around an entire wind farm and in turn return to the user more valuable data. To approach the finding of a solution to this problem, the project was divided into four: planning, concept definition, concept exploration and detailed design. From the first two phases came unique concepts exploring remote and local sensing options, combined with a range of UAV types including hybrid, fixed-wing and rotor. Through a detailed trade-off process and sensitivity analysis, the agreed upon final solution came to be a local sensing concept that makes use of many hybrid drones. In the fourth and final phase, where we now find ourselves, the detailed concept is unpacked and designed into a marketable system that is capable of satisfying the underlying MNS. In this stage the design was split into three design groups: UAV design, ground station design, swarm design.
Bachelor thesis
(2021)
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L.P.F. de Graaf, S.D.S. Keemink, P.J. Koopdonk, B.J. Kroese, N.W.M. de Krom, M.S. Kukkola, Lars La Heij, M.N. Lengkeek, J.J. L'Ortije, C.J. Middelhoek, C.A. Dransfeld, M.T.H. Brown, S. Hamaza