Week 7 Phase Test Week

Robotic interception of moving objects (continued from last week)

Among the most efficient approaches is APPE (Active Prediction, Planning and Execution) system for robotic
interception of moving objects. The key feature of the system is the ability of a robot to perform a task autonomously without complete a priori information.

Kalman filtering has proved very useful in autonomous and assisted navigation and guidance systems, radar tracking of moving objects, etc. The Kalman filter is a set of mathematical equations that provides an efficient computational (recursive) solution of the least-squares method. The filter is very powerful in several aspects: it supports estimates of past, present, and even future states, and it can do so even when the precise nature of the modelled system is unknown. Kalman filtering is also a computationally efficient algorithm, which generates an optimal estimate from a sequence of noisy observations.

This paper discuses an implementation of a robotic interception (a shoot function in robot soccer) based on image capture/processing combined with the successful use of Kalman filtering aiming at substantial improvement in shooting accuracy both for a stationary and moving object (the ball).

That's all for this week, Phase Test is in the way but from what written here i had understand that the article state that it use Kalman filtering to solve shooting accuracy problem when the ball is moving.

Week 6 control algorithm for the interception of a mobile target

This week i will study on the control algorithm to try understand more, so i had do some Google search and found this article;

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.78.2284&rep=rep1&type=pdf

In this article it say that, because of the dynamics and high complexity of the robot soccer system as well as maneuverability and speed of its robots, the accurate path planning and prediction of moving targets have gained special importance. Several techniques have been proposed for path planning including the use of genetic algorithms and fuzzy logic.

Coupled to the path-planning problem is obstacle avoidance in a dynamic environment. The path plans must be dynamically updated to reflect the changes in the environment, which means that they have to be created in real-time.

Robots have to exhibit basic actions like positioning at a designated location, moving to the ball or blocking an opponent, turning to a desired angle, circling around the ball and shooting the ball into opponent’s goal. Among other factors, the strategy and path planning in a robot soccer game are dependent on the ball position.

The robot's main sensor system is vision, which captures and processes the image at 30 to 60 frames per second. The odd and even scan fields of the interlaced image are processed separately. For this reason a stationary object like the ball is reported at different locations in each frame due to different quantization errors. The errors are compounded by the variation of light intensity from one frame to another. These quantization errors are inherent in the system and have a significant impact on the shooting accuracy even when the ball is stationary. However, the tests carried out on a moving ball are more significant because the interception accuracy suffers when the ball is moving. This is due to the fact that control actions are initiated based on the current ‘static’ state of objects whereas action must be taken based on predicted future positions.

That's all i had read for this week because i need to process all of this information to understand it.

Week 5 Prepare Project Proposal

Development of intelligent ball control algorithm for mobile robot by using artificial intelligent


Minggu ni saya wat research pasal ball control algorithm, mcm basic ball control block diagram, mcm mn  controller nak proses input daripada mobile robot dan nk determine kan action yang perlu diambil bg setiap keadaan dan kedudukan semua mobile robot. Daripada research yang dibuat banyak contoh-contoh soccer mobile robot yang saya baca yang pernah memasuki pertandingan soccer yang sebelum ini. daripada artikel robot-robot itu ada banyak contoh block diagram for ball control algorithm yang plg simple saya jumpe adalah
                                     

the input is the vision module (camera) where it is to take picture, to extract information from the picture such as object, type direction and distance. The Decision Engine receives input from the vision module and sends move commands to the drive controller and it consists of two sub-modules: the internal model manager and the strategy planner. These sub-modules communicate with each other to formulate the best decision for the agent’s next action. As specified in the system architecture, the drive controller takes commands from the decision engine, and sends the control signals to the output whereas in this case is the motor.

Block diagram yang ni simple sebab tu saya nk gunakan block diagram yang ni sebagai basic bagi permulaan, bile dah banyak yang dau tau saya akan gunakan block diagram yang lain sebagai basic contoh block diagram




the control architecture of MRL middle-size robots is a three layered architecture where the top layer contains techniques for autonomously creating a plan of robot tasks; the middle layer is a sequencing capability to accomplish tasks and the bottom.

  the control architecture of MRL middle-size robots is a three layered architecture where the top layer contains techniques for autonomously creating a plan of robot tasks; the middle layer is a sequencing capability to accomplish tasks and the bottom.

Block diagram yang ni sangat complex sbb tu saya tak gunakan lagi, ssh nk faham.

Minggu ni juga saya wat research pasal artificial intelligent, pasal fuzzy logic

Fuzzy logic is a form of multi-valued logic derived from fuzzy set theory to deal with reasoning that is approximate rather than precise. In contrast with "crisp logic", where binary sets have binary logic, fuzzy logic variables may have a truth value that ranges between 0 and 1 and is not constrained to the two truth values of classic propositional logic.

fuzzy logic ini digunakan sebagai asas bg controller untuk tentukan action yang perlu dibuat berdasarkan daripada semua input yang diterima. dan ader satu lg yang saya belum research lg iaitu neural mungkin minggu depan baru saya wat research.

setakat tu jer utk minggu ni. sekian

Week 4 Prepare Project Proposal

 Development of intelligent ball control algorithm for mobile robot by using artificial intelligent

Minggu ini saya study sikit pasal project ni, daripada tajuk saya tahu project adalah untuk buat satu program  untuk mobile robot main bola. Project ni fully program jadi hanya bole test melalui simulation gune software.
Saya ade jumpe beberapa contoh soccer mobile robot yang pernah masuk salah satu pertandingan soccer mobile robot 

Daripada contoh-contoh robot yang saya jumpe terdapat banyak input yang boleh dipakai utk soccer mobile robot, input-input ini penting untuk saya tahu kerana utk ball control yang bagus saya perlukan input yang cekap utk detect kedudukan sume objek sekeliling.
Tapi atas nasihat supervisor saya akan gunakan input mcm gambar dibawah,

 Sekian saja utk minggu ni.