Detail Cantuman

Motion control of a snake robot moving between two non-parallel p lanes
Mizuki N akajimaa, Motoyasu Tanakaa , Kazuo Tanakaa and Fumitoshi Matsunob
aDepartment of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications, Chofu, Japan; bDepartment of Mechanical Engineering and Science, Kyoto University, Kyoto, Japan
ABSTRACT
A control method that makes the head of a snake robot follow an arbitrary trajectory on two nonparallel planes, including coexisting sloped and flat planes, is presented. We clarify an appropriate condition of contact between the robot and planes and design a controller for the part of the robot connecting the two planes that satisfies the contact condition. Assuming that the contact condition is satisfied, we derive a simplified model of the robot and design a controller for trajectory tracking of the robot’s head. The controller uses kinematic redundancy to avoid violating the limit of the joint angle and a collision between the robot and the edge of a plane. The effectiveness of the proposed method is demonstrated in experiments using an actual robot.
KEYWORDS
Snake robot; slope; redundancy; trajectory tracking

Gaussian mixture spline trajectory: learning from a dataset, generating
trajectories without one
T. Barbié , R. Kabutan, R. Tanaka and T. Nishida
Kyushu I nstitute of Technology, K itak yushu-shi, Fukuok a-ken, Japan
ABSTR AC T
Most optimization-based motion planners use a naive linear initialization, which does not use previous planning experience. We present an algorithm called ‘Gaussian mixture spline trajectory’ (GMST) that leverages motion datasets for generating trajectories for new planning problems. Unlike other trajectory prediction algorithms, our method does not retrieve trajectories from a dataset. Instead, it first uses a Gaussian mixture model (GMM) to modelize the likelihood of the trajectories to be inside the dataset and then uses the GMM’s parameters to generate new trajectories. As the use of the dataset is restricted only to the learning phase it can take advantage of very large datasets. Using both abstract and robot system planning problems, we show that the GMST algorithm decreases the computation time and number of iterations of optimization-based planners while increasing their success rates as compared to that obtained with linear initialization.
KEY WORDS
Trajec tor y predic tion; G au s s i a n m i x t u re mo d e l ; dataset learning

Novel approaches to c ontrol multiple tasks i n redundant manipulators: stability
analysis and performance e valuation
Abbas K aramia , Hamid Sadeghianb and Mehdi Keshmiria
aDepartment of Mechanical Engineering, Isfahan University of Technology, Isfahan, Iran; bEngineering Department, University of Isfahan, Isfahan, Iran
ABSTRACT
In this paper, two new approaches for handling multiple tasks in redundant manipulators based on predefined allocated priorities are proposed. The first approach is an integrated scheme which employs null-space base vector for handling prioritized tasks. Clear task and null-space representation, better execution of the lower priority tasks, and intuitive formulation are its basic
features. The second approach aims to improve the performance of all the prioritized tasks, especially during algorithmic singularities beside clear null-space dynamics representation. This approach can be considered as a modification and extension of the Reverse Priority (RP) algorithm in acceleration level. The commands related to each tasks are added to each other following reverse order of priorities and by suitable projectors. The projector definition is given using minimal representation of the null-space. Clear null-space dynamics in the proposed methods facilitate the stability analysis. A detailed evaluation by means of computer simulation in various cases is reported. Tasks accomplishment using the proposed approaches is compared with the classic method. The results, in general, show higher performance and accuracy of the tasks by the proposed approaches.
KEYWORDS
Redundant robot; priority allocation; multi-task control; stability