KT
Kevin Tran
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A fixed-base robot arm can only reach objects within its physical workspace. When an object lies beyond this reach, the robot can use a held tool to pull the object closer before grasping it. This thesis studies how much a tool extends the reachable workspace of a Franka Panda robot arm (7 degrees of freedom).
A symbolic task and motion planning (TAMP) system, built from PDDL, a behaviour tree, and MoveIt2, plans and executes a drag action in which the robot uses a tool to pull an out-of-reach object toward itself. Two tool shapes, a hammer and a T-shape, are compared against a no-tool baseline. All trials run in MuJoCo simulation. Objects are placed on a systematic polar grid, spanning a distance of 0.35 to 0.90 m from the robot and an angle of 0 to 60 degrees, giving 1326 trials in total. The main measures are whether a plan can be found and executed, whether the object actually reaches the target, and the robot's mean manipulability (a measure of how far the arm's joints are from a singular configuration) during each trial.
The results show a clear trade-off. Without a tool, the robot succeeds almost every time at close range but fails completely beyond about 0.80 m. With a tool, success is lower at close range, because grasping and dragging add extra steps that can fail, but success increases beyond about 0.60 m, a distance the arm cannot reach directly. This crossover, where tool use becomes worthwhile, is the central finding of the thesis. A logistic regression model confirms this pattern statistically and shows that the two tool shapes extend reach by a similar amount, but differ in how much their success depends on the arm's manipulability: hammer performance is strongly tied to manipulability, while T-shape performance is not. This difference is due to how each tool interacts with the environment during the drag.
A smaller set of real-world experiments with the physical robot and the hammer tool supports the simulation results and shows that a collision-related failure mode seen with the T-shape in simulation does not appear in practice. Overall, the thesis shows that even a simple, prescribed tool-use action, planned by a symbolic planner, can meaningfully extend what a fixed-base manipulator can reach, and that the geometry of the tool shapes how reliably this works. ...
A symbolic task and motion planning (TAMP) system, built from PDDL, a behaviour tree, and MoveIt2, plans and executes a drag action in which the robot uses a tool to pull an out-of-reach object toward itself. Two tool shapes, a hammer and a T-shape, are compared against a no-tool baseline. All trials run in MuJoCo simulation. Objects are placed on a systematic polar grid, spanning a distance of 0.35 to 0.90 m from the robot and an angle of 0 to 60 degrees, giving 1326 trials in total. The main measures are whether a plan can be found and executed, whether the object actually reaches the target, and the robot's mean manipulability (a measure of how far the arm's joints are from a singular configuration) during each trial.
The results show a clear trade-off. Without a tool, the robot succeeds almost every time at close range but fails completely beyond about 0.80 m. With a tool, success is lower at close range, because grasping and dragging add extra steps that can fail, but success increases beyond about 0.60 m, a distance the arm cannot reach directly. This crossover, where tool use becomes worthwhile, is the central finding of the thesis. A logistic regression model confirms this pattern statistically and shows that the two tool shapes extend reach by a similar amount, but differ in how much their success depends on the arm's manipulability: hammer performance is strongly tied to manipulability, while T-shape performance is not. This difference is due to how each tool interacts with the environment during the drag.
A smaller set of real-world experiments with the physical robot and the hammer tool supports the simulation results and shows that a collision-related failure mode seen with the T-shape in simulation does not appear in practice. Overall, the thesis shows that even a simple, prescribed tool-use action, planned by a symbolic planner, can meaningfully extend what a fixed-base manipulator can reach, and that the geometry of the tool shapes how reliably this works. ...
A fixed-base robot arm can only reach objects within its physical workspace. When an object lies beyond this reach, the robot can use a held tool to pull the object closer before grasping it. This thesis studies how much a tool extends the reachable workspace of a Franka Panda robot arm (7 degrees of freedom).
A symbolic task and motion planning (TAMP) system, built from PDDL, a behaviour tree, and MoveIt2, plans and executes a drag action in which the robot uses a tool to pull an out-of-reach object toward itself. Two tool shapes, a hammer and a T-shape, are compared against a no-tool baseline. All trials run in MuJoCo simulation. Objects are placed on a systematic polar grid, spanning a distance of 0.35 to 0.90 m from the robot and an angle of 0 to 60 degrees, giving 1326 trials in total. The main measures are whether a plan can be found and executed, whether the object actually reaches the target, and the robot's mean manipulability (a measure of how far the arm's joints are from a singular configuration) during each trial.
The results show a clear trade-off. Without a tool, the robot succeeds almost every time at close range but fails completely beyond about 0.80 m. With a tool, success is lower at close range, because grasping and dragging add extra steps that can fail, but success increases beyond about 0.60 m, a distance the arm cannot reach directly. This crossover, where tool use becomes worthwhile, is the central finding of the thesis. A logistic regression model confirms this pattern statistically and shows that the two tool shapes extend reach by a similar amount, but differ in how much their success depends on the arm's manipulability: hammer performance is strongly tied to manipulability, while T-shape performance is not. This difference is due to how each tool interacts with the environment during the drag.
A smaller set of real-world experiments with the physical robot and the hammer tool supports the simulation results and shows that a collision-related failure mode seen with the T-shape in simulation does not appear in practice. Overall, the thesis shows that even a simple, prescribed tool-use action, planned by a symbolic planner, can meaningfully extend what a fixed-base manipulator can reach, and that the geometry of the tool shapes how reliably this works.
A symbolic task and motion planning (TAMP) system, built from PDDL, a behaviour tree, and MoveIt2, plans and executes a drag action in which the robot uses a tool to pull an out-of-reach object toward itself. Two tool shapes, a hammer and a T-shape, are compared against a no-tool baseline. All trials run in MuJoCo simulation. Objects are placed on a systematic polar grid, spanning a distance of 0.35 to 0.90 m from the robot and an angle of 0 to 60 degrees, giving 1326 trials in total. The main measures are whether a plan can be found and executed, whether the object actually reaches the target, and the robot's mean manipulability (a measure of how far the arm's joints are from a singular configuration) during each trial.
The results show a clear trade-off. Without a tool, the robot succeeds almost every time at close range but fails completely beyond about 0.80 m. With a tool, success is lower at close range, because grasping and dragging add extra steps that can fail, but success increases beyond about 0.60 m, a distance the arm cannot reach directly. This crossover, where tool use becomes worthwhile, is the central finding of the thesis. A logistic regression model confirms this pattern statistically and shows that the two tool shapes extend reach by a similar amount, but differ in how much their success depends on the arm's manipulability: hammer performance is strongly tied to manipulability, while T-shape performance is not. This difference is due to how each tool interacts with the environment during the drag.
A smaller set of real-world experiments with the physical robot and the hammer tool supports the simulation results and shows that a collision-related failure mode seen with the T-shape in simulation does not appear in practice. Overall, the thesis shows that even a simple, prescribed tool-use action, planned by a symbolic planner, can meaningfully extend what a fixed-base manipulator can reach, and that the geometry of the tool shapes how reliably this works.
Bachelor thesis
(2022)
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M.T.K. Tran, M.D. Yang, W.C.A.M. van Nieuwburg, B.J.J. van Spreuwel, M.J. Tummers, J. Westerweel, G. Mulder
A 1:5 scaled rowing boat had been designed by a previous research group to determine the performance of rowing blades with different sizes and blade angles. In this research modifications were done to allow more control of the rowing motion. The aim of this study is to assess whether an angled oar blade can reach faster speeds with the same input power. This is done by collecting data with force and position sensors. With the collected data, the input power and speed of the rowing boat can be compared between several modified oar blades. The results of the tests in the towing tank show that it is very likely that the rowing boat can go faster with the same input power by adjusting the oar blade angle.
...
A 1:5 scaled rowing boat had been designed by a previous research group to determine the performance of rowing blades with different sizes and blade angles. In this research modifications were done to allow more control of the rowing motion. The aim of this study is to assess whether an angled oar blade can reach faster speeds with the same input power. This is done by collecting data with force and position sensors. With the collected data, the input power and speed of the rowing boat can be compared between several modified oar blades. The results of the tests in the towing tank show that it is very likely that the rowing boat can go faster with the same input power by adjusting the oar blade angle.
Electrified Haber-Bosch Process
Green Ammonia Production
This report provides a design for a new process to make the ammonia-producing Haber-Bosch process more sustainable by reducing its CO2 emissions. It presents different elements of a process design with most importantly; process diagrams, mass and energy balances and a techno-economic analysis. The most important differences in this process is the use of water electrolysis and nitrogen production. This process emits approximately 50% less CO2 than regular Haber-Bosch methods. Nevertheless, the techno-economic analysis shows that this method is very expensive and that ammonia can only be sold at an acceptable price conform to the market with subsidies on electricity prices and can only become competitive if CO2 taxes are implemented for classic Haber-Bosch plants.
...
This report provides a design for a new process to make the ammonia-producing Haber-Bosch process more sustainable by reducing its CO2 emissions. It presents different elements of a process design with most importantly; process diagrams, mass and energy balances and a techno-economic analysis. The most important differences in this process is the use of water electrolysis and nitrogen production. This process emits approximately 50% less CO2 than regular Haber-Bosch methods. Nevertheless, the techno-economic analysis shows that this method is very expensive and that ammonia can only be sold at an acceptable price conform to the market with subsidies on electricity prices and can only become competitive if CO2 taxes are implemented for classic Haber-Bosch plants.