We present in this paper a first-order axiomatization of an extended theory T of finite or infinite trees, built on a signature containing an infinite set of function symbols and a relation finite(t), which enables to distinguish between finite and infinite trees. We show that T has at least one model and prove its completeness by giving not only a decision procedure, but a full first-order constraint solver that gives clear and explicit solutions for any first-order constraint satisfaction problem in T. The solver is given in the form of 16 rewriting rules that transform any first-order constraint ϕ into an equivalent disjunction φ of simple formulas such that φ is either the formula true or the formula false or a formula having at least one free variable, being equivalent neither to true nor to false and where the solutions of the free variables are expressed in a clear and explicit way. The correctness of our rules implies the completeness of T. We also describe an implementation of our algorithm in CHR (Constraint Handling Rules) and compare the performance with an implementation in C++ and that of a recent decision procedure for decomposable theories.

Given an argumentation framework AF, we introduce a mapping function that constructs a disjunctive logic program P, such that the preferred extensions of AF correspond to the stable models of P, after intersecting each stable model with the relevant atoms. The given mapping function is of polynomial size w.r.t. AF.

In particular, we identify that there is a direct relationship between the minimal models of a propositional formula and the preferred extensions of an argumentation framework by working on representing the defeated arguments. Then we show how to infer the preferred extensions of an argumentation framework by using UNSAT algorithms and disjunctive stable model solvers. The relevance of this result is that we define a direct relationship between one of the most satisfactory argumentation semantics and one of the most successful approach of nonmonotonic reasoning i.e., logic programming with the stable model semantics.

This paper is to investigate inherent oscillations problems of Potential Field Methods (PFMs) for nonholonomic robots in dynamic environments. In prior work, we proposed a modification of Newton's method to eliminate oscillations for omnidirectional robots in static environment. In this paper, we develop control laws for nonholonomic robots in dynamic environment using modifications of Newton's method. We have validated this technique in a multirobot search-and-forage task. We found that the use of the modifications of Newton's method, which applies anywhere C2 continuous navigation functions are defined, can greatly reduce oscillations and speed up robot's movement, when compared to the standard gradient approaches.

This paper presents a global ultrasonic system with selective activation algorithm for autonomous navigation of an indoor mobile robot. The global ultrasonic system consists of several ultrasonic transmitters fixed at reference positions in global coordinates and two receivers at moving coordinates of a mobile robot. By activating the ultrasonic transmitters through an radiofrequency (RF) channel, the robot is able to obtain distance information to the reference positions and localize itself in the global coordinates. Due to limitations in signal strength and beam width, the ultrasonic signals from some transmitters may not be delivered to the robot and the ultrasonic data become invalid. In order to improve the effectiveness of the global ultrasonic system, a so-called selective activation algorithm is developed. Based on the current position of the robot, the selective activation calls a proper ultrasonic transmitter and generates valid ultrasonic data at every sampling instant, resulting in faster, more accurate response for self-localization than does simple sequential activation. Path-following control experiments are conducted to verify the effectiveness of the self-localization based on the proposed selective activation algorithm with the global ultrasonic system.

This paper proposes a method for task based design of modular serial robotic arms using evolutionary algorithms (EA). We introduce a 3D kinematics and a global optimization for both topology and configuration from task specifications. The search features revolute as well as prismatic joints and any number of DOF to build up a solution without using any design knowledge. A study of the evolution dynamics gives some keys to set evolution parameters that enable artificial evolution. An adapted algorithm dealing with the topology/configuration search tradeoff is proposed, descibed, and discussed. Illustrations of the algorithms results are given and conclusions are drawn from their analysis. Perspectives of this work are given, extending its reach to control and complex system design.

The aim of this paper is to show how it is possible to obtain for the 5R planar parallel manipulator the complete workspace associated with each solution of the direct kinematic problem or assembly mode. The workspaces associated with the different inverse kinematic problem solutions or working modes are joined and the robot moves from one to another without losing the control. An exhaustive analysis of the complete workspace and singular positions of the 5R planar parallel manipulator with two active joints is presented. Furthermore, application of these principles to path planning will be explained.

This work presents a method to generate optimal trajectories for redundant mobile manipulators based on a weighted function that considers simultaneously joint torques, manipulability and preferred joint angle references. This method is applicable to a group of tasks, commonly known as push–pull tasks, in which a redundant mobile manipulator subject to non-holonomic constraints moves slowly while exerting a set of forces against the environment. In practice, this occurs when the manipulator is pulling against an object such as when opening a door or unearthing a buried object. Torque is computed in a quasi-static manner, mainly taking into consideration the effect of multiple external forces while neglecting dynamic effects. The formulation incorporates a criterion for optimizing a starting configuration, and special considerations are made to account for non-holonomic constraints. The application to an existing mobile manipulator is described.

Three-dimensional (3D) enveloping grasps for dexterous robotic hands possess several advantages over other types of grasps. This paper describes a new method for kinematic 3D enveloping grasp planning. A new idea for grading the 3D grasp search domain for a given object is proposed. The grading method analyzes the curvature pattern and effective diameter of the object, and grades object regions according to their suitability for grasping. A new approach is also proposed for modeling the fingers of the dexterous hand. The grasp planning method is demonstrated for a three-fingered, six degrees-of-freedom, dexterous hand and several 3D objects containing both convex and concave surface patches. Human-like high-quality grasps are generated in less than 20 s per object.

An algorithm for three-level hierarchical sensor fusions has beenproposed and applied to environment map building with enhanced accuracy and efficiency. The algorithm was realized through the two new types of sensor modules, which are composed of a halogen lamp-based active vision sensor and a semicircular ultrasonic (US) and infrared (IR) sensor system. In the first-level fusion, the US and IR sensor information is utilized in terms of the geometric characteristics of the sensor location. In the second-level fusion, the outputs from the US and IR sensors are combined with the sheet of halogen light through a proposed rule base. In the third-level fusion, local maps from the first- and second-level fusion are updated in a probabilistic way for a very accurate environment local map. A practical implementation has been carried out to demonstrate the efficiency and accuracy of the proposed hierarchical sensor fusion algorithm in environment map building.

This paper presents a two-level control strategy for bipedal walking mechanism that accounts for implicit control of push-off on the between-step control level and tracking of imposed holonomic constraints on kinematic variables via feedback control on within-step control level. The proposed control strategy was tested in a biologically inspired model with minimal set of segments that allows evolution of human-like push-off and power absorption. We investigated controller's stability characteristics by using Poincaré return map analysis in eight simulation cases and further evaluated the performance of the biped walking model in terms of how variations in torso position and gait velocity relate to push-off and power absorption. The results show that the proposed control strategy, with the same set of controller's gains, enables stable walking in a variety of chosen gait parameters and can accommodate to various trunk inclinations and gait velocities in a similar way as seen in humans.

In this work, the 3-RPRR, a new kinematically redundant planar parallel manipulator with six-degrees-of-freedom, is presented. First, the manipulator is introduced and its inverse displacement problem discussed. Then, all types of singularities of the 3-RPRR manipulator are analysed and demonstrated. Thereafter, the dexterous workspace is geometrically obtained and compared with the non-redundant 3-PRR planar parallel manipulator. Finally, based on a geometrical measure of proximity to singular configurations and the condition number of the manipulators' Jacobian matrices, actuation schemes for the manipulators are obtained. Different actuation schemes for a given path are obtained and the quality of their actuation schemes are compared. It is shown that the proposed manipulator is capable of following a path while avoiding the singularities.