16:50―17:10
Rinaldo C. Michelini, Rezia M. Molfino, Univ. Genova, Italy, Raffaele Ghigliazza, Princeton Univ., USA, Massimo Callegari, Univ. Ancona, Italy
Eco-compatibility aims at drastic changes of people transportation means and, to grant proper mobility levels, the electrically-powered city-car concept provides effective solutions. This quite obviously leads to distributed actuation, with separately driven wheels and inconsistencies appear unless redundancy and tyre/road interaction are properly mastered. The paper deals with the dynamics of such kind of vehicle moving from the behaviour of driven wheels (with compliant tyre and varying soil interactions); then a twin powered axle or train are investigated, as basic reference to describe the dynamics of a four wheels platform, driven by redundant actuation on varying friction soils. On these premises, a city car manoeuvre stability can be stated for low speed tasks over urban roads. Results … |
Igor E. Paromtchik, Hajime Asama, RIKEN, Japan
This paper describes our research work towards the development of an optical guidance system for multiple mobile robots in an indoor environment. The guidance system operates with an environmental model, communicates with mobile robots and indicates their target positions by means of a light projection from a laser pointer onto the ground. Processing the image data from a CCD color camera mounted on the mobile robot allows it to detect the laser light beacon on the ground and estimate its relative coordinates. The robot’s control system ensures the accurate motion of the robot to the indicated target position. The guidance system subsequently indicates target positions corresponding to a desired route for a specified mobile robot in the fleet. The concept of the optical guidance system, its implementation and experimental results are discussed.
| 17:10―17:30 | Optimisation of Energy Flow Management in Hybrid Electric Vehicles via Genetic Algorithms
Antonio Piccolo, Lucio Ippolito, Vincenzo Galdi, Alfredo Vaccaro, Università di Salerno, Italy
Hybrid electric vehicles powertrain, combining electric motor with an auxiliary power unit, can offer a sensible improvement of the overall vehicle environmental impact achieving at the same time a rational energy employment. This valuable features can be magnified designing a suitable energy flow management unit whose main task is to split the instantaneous vehicle power demand between the internal combustion engine and the electric motor ensuring that the power sources are operated at high efficiency operating points and the related vehicle emissions are minimised. In the present paper after a preliminary analysis on the strategy adopted an original methodology for the tuning of the characteristic parameters … |
T1A | Robot Analysis and Planning | Neural and Fuzzy Control | T1B | SALA PLATEA | 09:30―11:10 | | Constantinos Mavroidis, USA | CHAIR | Ranjan Mukherjee, USA | Massimo Callegari, Italy | CO-CHAIR | Atsushi Konno, Japan | A New Algorithm for a Minimum Infinity-norm Solution and Its Application to Trajectory Planning of Kinematically Redundant Manipulators
Insoo Ha, Samsung Electronics, Jihong Lee, Chungnam National University, Korea
In this paper, we propose a new algorithm for finding a minimum infinity-norm solution of consistent linear equations. The proposed algorithm includes the advantages of previous works such as computational efficiency of Cadzow’s algorithm and geometric interpretation of Shim’s algorithm, and overcomes the disadvantages of them such as incompleteness of Cadzow’s algorithm and computational inefficiency of Shim’s algorithm. Also, for redundant robot trajectory planning based on minimum infinity-norm solution, an efficient approach avoiding discontinuity in trajectory is proposed by resolving the non-uniqueness problem … | 09:30―09:50 |
Shigeyasu Kawaji, Kumamoto University, Masaki Arao, Omron Corporation, Yuehui Chen, Kumamoto University, Japan
Thrust force and cutting torque are important outputs in the control of drilling systems.In this paper, a method for estimating and control the thrust force in the drilling process is proposed.Firstly,a neural network model of thrust force is on-line constructed. Secondly,based on the neuro model of thrust force,a simulated neuro controller is developed by using online trained recursive least square algorithm.Finally,the trained controller is applied to the drill machine to force the thrust force of the drilling system follow the reference thrust froce signal.The experimental results demonstrate the e .ectiveness of the proposed method.
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