Human Computer Interaction takes the attention of many researches all over the world, researches try to invent more natural and intuitive interaction ways between humans and computers. However, people depend mainly on their hands to interact with surrounding objects in everyday life. In this paper we present a novel appearance based method to recognize and track human hand in an unknown environment without any constraints on hand posture and with no need to wear any additional devices. Our method can distinguish between hand and other moving objects, by using proposed posture independent hand features. Results show that our method can recognize and track human hand at real-time with 96% accuracy rate in normal indoors light.

The paper presents an approach to detect and localize contact between deformable polyhedra, which can be convex or concave depending on the time step. Usual contact detection algorithms, defined for convex polyhedra, cannot be used efficiently, as they would imply completing the convex decomposition of the considered polyhedra at each time step, as it can change due to the deformability of these polyhedra. As the computation of this convex decomposition is very expensive (in complexity and computation time), we propose an algorithm to detect and localize the contact in linear time w.r.t. the number of vertices. This algorithm returns the direction of this contact and the value of the maximum intersection distance between the convex hulls of the two considered polyhedra. Experimental results taken from a dynamic simulation application are presented with their computation time, to complete the complexity analysis.

We describe models and algorithms designed to produce efficient and physically consistent dynamic simulations. These models and algorithms have been implemented in a unique framework, modeling both deformations and contacts through visco-elastic relations. Since this model of interaction (known as "penalty based") is much debated, we present it in detail. The "penalty" based model is said to have two major drawbacks: the difficulty in determining viscoelastic parameters, and in choosing the computation time step. We present a solution for both problems based on physical concepts. Finally, we show results comparing real data, "impulse" based simulation and "penalty" based simulation.

We describe models and algorithms designed to produce efficient and physically consistent dynamic simulations. These models and algorithms have been implemented in a unique framework modeling both deformations and contacts through visco-elastic relations. Since this model of interaction (known as "penalty based") is much debated, we present a detailed study of this model. Indeed, the "penalty" based model is supposed to have two major drawbacks: determining the visco-elastic parameters and choosing the computation time step. We present a solution for both problems based on physical concepts. Finally, we present results comparing real data, "impulse" based simulation and "penalty" based simulation.

Dynamic simulation is potentially a powerful tool for studying complex contact interactions between robots and their environments. One of the main problems of such a simulation is the determination of the model parameters which provide realistic behavior for objects. This paper describes how to identify such parameters for a masses/springs based system (the Robot/spl Phi/ system). This identification process is composed of two main steps: the first step consists in distributing the total mass of the object between the different particles while respecting the inertial properties of the object; the second one consists in finding the values of the physical parameters of the model such as elasticity, viscosity, plasticity, etc. A numerical solution of the first step is described, while a genetic algorithm based approach is proposed for the second step. This approach allows us to find the values of the parameters which provide a consistent behavior under a set of given constraints (position, velocity, volume, etc). It also allows us to minimize the computation time and to attach priorities with these constraints.

Dynamic simulation is potentially a powerful tool for studying complex contact interactions between robots and their environments. One of the main problems of such a simulation is the determination of the model parameters which provide realistic behavior for objects. This paper describes how to identify such parameters for a mass/spring based system. This identification process is composed of two main steps: 1) distributing the total mass of the object between different particles while respecting the inertial properties of the object; and 2) finding the values of the physical parameters of the model such as elasticity, viscosity, plasticity, etc. The numerical solution of the first step is described, while a genetic algorithm base approach is proposed for the second step. This approach allows us to find the values of the parameters which provide a consistent behavior under a set of given constraints (position, velocity, volume, etc.). It also allows us to minimize the computation time and to attach priorities with these constraints.

This paper presents a new approach to detect and localise contact between concave deformable polyhedra. In this case there are many contact points between two polyhedra, the proposed algorithm detects and localises the contact in linear time O(n). It returns also the direction of this contact and the value of the maximum inter-penetration between the two convex-hulls of these two polyhedra.

Dynamic simulation can be a powerful tool for the study of complex contact interactions between robots and their environments, but a large amount of computation is often required to generate numerically accurate simulations. This paper describes an adaptive time step approach to dynamical simulation based on monitoring the conservation of energy. This approach allows us to insure numerical stability and computational efficiency at a very low computational overhead, and also provides estimates of the probable range of numerical errors in position and velocity. The approach is applicable to any physical-based dynamic simulation system and can be integrated with other optimization techniques.

Complex contact interactions between the robot and its environment (contact between a dextrous hand and a grasped object, contact between an all-terrain vehicle and the terrain, ..) depend on physical properties such as mass, mass distribution, rigidity/elasticity factors, viscosity, and collision forces. Classical geometrical models (representing the spatial properties of an object) are obviously not helpful to study such interactions. So one needs another model which represents not only the form of the object but also its motion, its deformation and, its interaction with the environment. Such a model is called the "dynamic model". This paper describes the Robot/spl Phi/ system, which enables one to physically represent robots and to study their physical behaviour. This system uses a hierarchical representation of objects in order to accelerate collision detection (linear time for rigid objects). An energy based adaptative time step approach was developed in order to detect and avoid numerical divergence and to reduce computational time.

This paper presents a complete dynamic modeling system, called Robot/spl Phi/. This system allows us to create a physical model of an object and to simulate its motion, its deformations, and its interaction with the environment and with other objects. This system takes into account three types of interactions: the collision, the static and dynamic friction, and the viscosity of the environment. An hierarchical representation of objects allows us to have a linear complexity when calculating the contact between objects. An adaptative time step allows the system to avoid numerical divergence, to optimise the computational time, and to control the incurred error.

Planning robust grasping operations involving a dextrous robotics hand and an object located in a three dimensional workspace requires the combination of two main types of techniques, a geometric reasoning technique aimed at producing a grasping strategy (preshape of the hand, grasping parameters, type of motion to execute), and a physically based technique, allowing the analysis of the dynamic object/hand interactions. The paper focuses on the second type of technique which is clearly required to conclude on the feasibility of the chosen grasping strategy (stability in particular) and to determine the execution parameters of the selected grasp (type of control to apply onto the hand in particular). The purpose of the paper is to propose a new technique based upon the concept of "physical models" for solving this problem. The authors show how physical models can be constructed and used to solve the associated stability and control strategy problems. The authors also show how the related techniques have been combined with more classical geometric methods to solve the whole problem.>

Planning robust grasping operations involving a dextrous robotics hand and an object located in a 3D workspace requires the combination of two main types of techniques: a geometric based reasoning aimed at producing a grasping strategy; and a physical analysis of the object/hand interactions. The second type of technique is clearly necessary to conclude on the feasibility of the chosen grasping strategy (stability in particular) and to determine the type of control to apply onto the hand. The purpose of this paper is to propose a technique based upon the concept of "physical models" for solving this problem. We show how physical models can be constructed and used to solve the associated stability and control strategy problems. We also show how the related techniques can be combined with more classical geometric methods to solve the whole problem. We describe the modeling of the task, the connection between geometric and physical constructions, the characteristics of the motion planning process, and the experimental results which have been obtained.>