University Course Timetabling with Genetic Algorithm: a Laboratory Excercises Case Study

Abstract. This paper describes the application of a hybrid genetic algorithm to a real-world instance of the university course timetabling problem. We address the timetabling of laboratory exercises in a highly constrained environment, for which a formal definition is given. Solution representation technique appropriate to the problem is defined, along with associated genetic operators and a local search algorithm. The approach presented in the paper has been successfully used for timetabling at the authors ’ institution and it was capable of generating timetables for complex problem instances.

Inverse Kinematics Analysis and COG Trajectory Planning Algorithms for Stable Walking of a Quadruped Robot with Redundant DOFs

This paper presents a new Center of Gravity (COG) trajectory planning algorithm for a quadruped robot with redundant Degrees of Freedom (DOFs). Each leg has 7 DOFs, which allow the robot to exploit its kinematic redundancy for various locomotion and manipulation tasks. Also, the robot can suitably adapt to different environment (e.g., passing through a narrow gap) by simply changing the body posture. However, the robot has significant COG movement during the leg swinging phase due to the heavy leg weights; the weight of all the four legs takes up 80% of the robot???s total weight. To achieve stable walking in the presence of undesired COG movements, a new COG trajectory planning algorithm was proposed by using a combined Jacobian of COG and centroid of a support polygon including a foot contact constraint. Additionally, the inverse kinematics of each leg was solved by modified improved Jacobian pseudoinverse (mIJP) algorithm. The mIJP algorithm could generate desired trajectories for the joints even when the robot???s leg is in a singular posture. Owing to these proposed methods, the robot was able to perform various modes of locomotion both in simulations and experiments with improved stability