 Rational Trigonometry Applied to Robotics

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Rational Trigonometry Applied to Robotics
Content Final Year Project Rational Trigonometry Applied to Robotics João Pequito Almeida

1.1. Definition of Rational Trigonometry 1.2. Main advantages
Rational Trigonometry Applied to Robotics Content Content 1. Introduction 1.1. Definition of Rational Trigonometry 1.2. Main advantages 1.3. Application examples 2. Application to Robotics 2.1. Problems using standard approaches to kinematics modeling 2.2. Approach using Rational Trigonometry Fundamental principle Equations Application examples 2.3. Conclusions, perspectives and future work 3. Questions, demonstrations

Rational Trigonometry Applied to Robotics
1.1. Definition of Rational Trigonometry What is it? Rational Trigonometry is the study of triangles using quadrances and spreads. Quadrance = Distance² Spread = Sin²(separation angle) Or

Rational Trigonometry Applied to Robotics
1.2. Main advantages Defines separation of lines in an unambiguous way; Avoids the use of circular functions for the study of triangles; Privileges an algebraic approach instead of a functional approach; Allows the solving of complex geometrical problems using rational expressions; Simplifies the computational implementation of geometrical problems (namely avoids error propagation in series expansions).

Rational Trigonometry Applied to Robotics
1.3. Application examples A simple triangle

Rational Trigonometry Applied to Robotics
2.1. Problems using other models The simplicity in obtaining the solutions depends on the choice of reference frames (i.e. some choices make calculations easier than others); Difficulty in applying solutions obtained for one robot to another with a different kinematics structure, i.e., the structure of a solution is in general not kept for a different problem Difficulty in solving inverse problems – RT will be shown to provide a generic solution to the inverse problem as a projection map between the joint space and task space Forward kinematics for parallel manipulators are in general complex and usually obtained through numeric methods.

Rational Trigonometry Applied to Robotics
2.2. Approach using Rational Trigonometry By using Rational Trigonometry, a difficulty immediately comes up for rotation, better illustrated in the figure. In which of these situations is the point P defined only by its quadrance Q and spread s? Why not use this, then, as an advantage?

Rational Trigonometry Applied to Robotics
2.2. Approach using Rational Trigonometry By separating the sign and the value at stake we can have a clearer description of the point location and take advantage of the natural redundancy of an axis system with reflections (example [+x -y +z]): Line choice matrix Base value matrix Reference frame matrix Or, more generically We can also obtain R matrices for other representations like the Euler model for rotation (ideal for attitude representation). A new coordinate system

Rational Trigonometry Applied to Robotics
2.2. Approach using Rational Trigonometry Then, using these new coordinate system (L,R,B) allows for a description of the point as a set of values: 1. A line choice matrix; 2. Reference frame matrix; 3. Base value matrix. This notation also allows the calculation of all reflected points using just one value calculation. Now, how to combine them to obtain a description of the motion of a set of points?

Rational Trigonometry Applied to Robotics
2.2. Approach using Rational Trigonometry If we consider each solution Si as one unique tuple, then the set of all solutions can be obtained by their Cartesian Product of solutions

Rational Trigonometry Applied to Robotics
2.2. Approach using Rational Trigonometry Hypothesis: generate matrices than can be combined uniquely, similarly to the Cartesian product of sets where each Tuple is one of these coordinate points (a row of the P matrix), i.e. if we take the same row of each matrix it will be a unique combination of coordinates. For that we need to repeat and swap rows, a possible solution (using some algebra “tricks”) is the following set of equations: Coordinate selection matrix if otherwise Solutions of previous (I) and next joints (O) Counts total of combinations of coordinates from a to b The final swapped point

Rational Trigonometry Applied to Robotics

Rational Trigonometry Applied to Robotics
2.2. Approach using Rational Trigonometry If each Pi has all points of joint i, P* (the swapped solutions) can be added (provided that the previous rules are respected). By using swapped coordinates we can simply add them according to the robot’s structure. This exposes clearly the structure as a matrix revealing the nature of both the direct and the inverse kinematics problem:

Rational Trigonometry Applied to Robotics
2.2. Approach using Rational Trigonometry Series manipulator example As you could see in the previous equation, this model generalizes very well for any number of joints, so, as an example, the 2D hiper-redundant snake: Reference Frame Matrices Base Matrices Combined Reference Frame Combined Base Matrix

Rational Trigonometry Applied to Robotics
2.2. Approach using Rational Trigonometry Series manipulator example Solution with known L, (this will be the case for the most common uses as L matrices can be obtained directly from the data)

Rational Trigonometry Applied to Robotics
2.2. Approach using Rational Trigonometry

Rational Trigonometry Applied to Robotics
2.2. Approach using Rational Trigonometry Parallel manipulator example (P3 is the midpoint)

Rational Trigonometry Applied to Robotics
2.2. Approach using Rational Trigonometry The solution is similar to the series example but P3 can be obtained from P1 and P2, so, assuming P1 and P2 are determined: Again, as known functions (2D, r have the appropriate signs): Combining successive series and parallel manipulators this way is, as you can see, quite simple

Rational Trigonometry Applied to Robotics
2.3. Conclusions, Perspectives and Future Work This model provides A straightforward way to approach any robotic structures in a generic way; An algebraic representation that allows a global perspective of the problems at hand (e.g. the existence of redundant solutions). Perspectives It’s clear that using this framework the inversion of cinematic models might be simplified for many cases and current work involves optimizing this inversion in a global way; Computational versions are easy to obtain and implement. Future Work Current plans include solving the inverse model in an optimal way (hopefully globally optimal) Implement a working toolbox for MatLab/Octave and/or a library for C/C++/Java Developments available online at

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3. Questions, demonstrations ? ?

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