IK-FA, a new heuristic inverse kinematics solver using firefly algorithm

In this paper, a heuristic method based on Firefly Algorithm is proposed for inverse kinematics problems in articulated robotics. The proposal is called, IK-FA. Solving inverse kinematics, IK, consists in finding a set of joint-positions allowing a specific point of the system to achieve a target position. In IK-FA, the Fireflies positions are assumed to be a possible solution for joints elementary motions. For a robotic system with a known forward kinematic model, IK-Fireflies, is used to generate iteratively a set of joint motions, then the forward kinematic model of the system is used to compute the relative Cartesian positions of a specific end-segment, and to compare it to the needed target position. This is a heuristic approach for solving inverse kinematics without computing the inverse model. IK-FA tends to minimize the distance to a target position, the fitness function could be established as the distance between the obtained forward positions and the desired one, it is subject to minimization. In this paper IK-FA is tested over a 3 links articulated planar system, the evaluation is based on statistical analysis of the convergence and the solution quality for 100 tests. The impact of key FA parameters is also investigated with a focus on the impact of the number of fireflies, the impact of the maximum iteration number and also the impact of (a, ß, ¿, d) parameters. For a given set of valuable parameters, the heuristic converges to a static fitness value within a fix maximum number of iterations. IK-FA has a fair convergence time, for the tested configuration, the average was about 2.3394 × 10-3 seconds with a position error fitness around 3.116 × 10-8 for 100 tests. The algorithm showed also evidence of robustness over the target position, since for all conducted tests with a random target position IK-FA achieved a solution with a position error lower or equal to 5.4722 × 10-9.Peer ReviewedPostprint (author's final draft

CHARDA: Causal Hybrid Automata Recovery via Dynamic Analysis

We propose and evaluate a new technique for learning hybrid automata automatically by observing the runtime behavior of a dynamical system. Working from a sequence of continuous state values and predicates about the environment, CHARDA recovers the distinct dynamic modes, learns a model for each mode from a given set of templates, and postulates causal guard conditions which trigger transitions between modes. Our main contribution is the use of information-theoretic measures (1)~as a cost function for data segmentation and model selection to penalize over-fitting and (2)~to determine the likely causes of each transition. CHARDA is easily extended with different classes of model templates, fitting methods, or predicates. In our experiments on a complex videogame character, CHARDA successfully discovers a reasonable over-approximation of the character's true behaviors. Our results also compare favorably against recent work in automatically learning probabilistic timed automata in an aircraft domain: CHARDA exactly learns the modes of these simpler automata.Comment: 7 pages, 2 figures. Accepted for IJCAI 201

A comprehensive study on pathfinding techniques for robotics and video games

This survey provides an overview of popular pathfinding algorithms and techniques based on graph generation problems. We focus on recent developments and improvements in existing techniques and examine their impact on robotics and the video games industry. We have categorized pathfinding algorithms based on a 2D/3D environment search. The aim of this paper is to provide researchers with a thorough background on the progress made in the last 10 years in this field, summarize the principal techniques, and describe their results. We also give our expectations for future trends in this field and discuss the possibility of using pathfinding techniques in more extensive areas

A study on virtual reality and developing the experience in a gaming simulation

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Masters by ResearchVirtual Reality (VR) is an experience where a person is provided with the freedom of viewing and moving in a virtual world [1]. The experience is not constrained to a limited control. Here, it was triggered interactively according to the user’s physical movement [1] [2]. So the user feels as if they are seeing the real world; also, 3D technologies allow the viewer to experience the volume of the object and its prospection in the virtual world [1]. The human brain generates the depth when each eye receives the images in its point of view. For learning for and developing the project using the university’s facilities, some of the core parts of the research have been accomplished, such as designing the VR motion controller and VR HMD (Head Mount Display), using an open source microcontroller. The VR HMD with the VR controller gives an immersive feel and a complete VR system [2]. The motive was to demonstrate a working model to create a VR experience on a mobile platform. Particularly, the VR system uses a micro electro-mechanical system to track motion without a tracking camera. The VR experience has also been developed in a gaming simulation. To produce this, Maya, Unity, Motion Analysis System, MotionBuilder, Arduino and programming have been used. The lessons and codes taken or improvised from [33] [44] [25] and [45] have been studied and implemented

Prominence Driven Character Animation

This paper details the development of a fully automated system for character animation implemented in Autodesk Maya. The system uses prioritised speech events to algorithmically generate head, body, arms and leg movements alongside eyeblinks, eyebrow movements and lip-synching. In addition, gaze tracking is also generated automatically relative to the definition of focus objects- contextually important objects in the character\u27s worldview. The plugin uses an animation profile to store the relevant controllers and movements for a specific character, allowing any character to run with the system. Once a profile has been created, an audio file can be loaded and animated with a single button click. The average time to animate is between 2-3 minutes for 1 minute of speech, and the plugin can be used either as a first pass system for high quality work or as part of a batch animation workflow for larger amounts of content as exemplified in television and online dissemination channels