Position estimation is a key issue for an ALV Autonomous Land Vehicle) in navigating a mountainous area. The unevenness of the terrain makes mechanical velocity sensors inaccurate (due to wheel slippage), and the lack of appropriate landmarks complicates the problem. In this paper, we present a solution method using features of the skyline. The skyline from the vision system is assumed given, and compared with a computer map, called the CAD-MAP. The algorithm is composed of: a) Identification of the peak points in the camera skyline, b) Computing the ALV position for the identified peak points, and c) Searching for the corresponding peak point in the CAD-MAP. Heuristics for computational efficiency and solution accuracy are also included in the algorithm. To test the validity and effectiveness of the algorithm, numerous simulations were performed and analyzed.

A new approach to generating smooth piecewise local trajectories for mobile robots is proposed in this paper. Given the configurations (position and direction) of two points, we search for the trajectory that minimizes the integral of acceleration (tangential and normal). The resulting trajectory should not only be smooth but also safe in order to be applicable in real-life situations. Therefore, we investigate two different obstacle-avoidance constraints that satisfy the minimization problem. Unfortunately, in this case the problem becomes more complex and not suitable for real time implementations. Therefore, we introduce two simple solutions, based on the idea of polynomial fitting, to generate safe trajectories once a collision is detected with the original smooth trajectory. Simulation results of the different algorithms are presented.

This paper addresses widely applicable methods for solving the kinematics problem of any class of serial-link robot manipulators. First, the position and orientation of the manipulator hand, the Jacobian matrix and their symbolic generation are clearly presented using recursive relations. Second, the inverse problem to such formulations is posed as an unconstrained non-linear optimization one, where numerical techniques for the overdetermined and underdetermined kinematic problems are considered separately to derive consistent arm solutions. On the basis of several proposals on step lengths involved to maintain good numerical stability, the results of computer simulation show that performance is sufficiently reliable.

This paper presents a new neural networks-based method to solve the motion planning problem, i.e. to construct a collision-free path for a moving object among fixed obstacles. Our ‘navigator’ basically consists of two neural networks: The first one is a modified feed-forward neural network, which is used to determine the configuration space; the moving object is modelled as a configuration point in the configuration space. The second neural network is a modified bidirectional associative memory, which is used to find a path for the configuration point through the configuration space while avoiding the configuration obstacles. The basic processing unit of the neural networks may be constructed using logic gates, including AND gates, OR gates, NOT gate and flip flops. Examples of efficient solutions to difficult motion planning problems using our proposed techniques are presented.

This paper presents a fibre optic proximity sensor suitable for robotic applications. The sensor characteristics are first identified through experiments. Then the signal processing problem is studied for general applications where no prior knowledge of the sensor environment is assumed. The nonlinear filtering is carried out by applying a Kalman Filter. Particular attention is paid to the target nature dependence of the sensor output. Experimental results are reported by using the sensors on a real robot system.

Traditionally, tactile sensors have been designed using compliant, rubber-like materials; when such a sensitized gripper grasps or otherwise manipulates an object, the normal strain deformation in the compliant material is sampled. The resulting information can be used to deduce simple local geometry of the contact, but the transduction process does not typically permit use of the individual strains in determining large-scale properties of the object (e.g., the inertia). Measurements of inertial parameters of grasped objects require accurate, low-hysteresis transduction that few tactile sensors currently provide.

An alternative is to work from the task specification, and determine the tactile information that is necessary to accomplish the task. Here, we consider how to sense the length and mass of a uniform object that is gripped in a gravitational field, and show the design and assessment of a new kind of tactile sensor that is based on the theory of the deformation of thin plates. Features of this design include its potentially rugged realization, and its high-accuracy measurement that is more typical of force sensors than of tactile sensors.

This paper focuses on developing a planning strategy for robotic assembly. At first, channel and junction are defined by using half-spaces, and free-space inside the female part is approximately decomposed by channels and junctions. Then, a simple and efficient algorithm to find the assembly path of the male part is developed, in which any path between the shortest and safest paths can be easily found by just changing the clearance gap between the male and female parts. Next, the robot arm is considered in the path planning, in which a feasible grasp angle region is obtained to avoid a collision between the robot arm and the female part during the assembly process. An optimum grasping angle can be found in the feasible grasping angle region by applying a proper performance index. Finally, a simple robotic assembly using the algorithm is numerically demonstrated.

In this paper a control strategy for a redundant PUMA robot is presented. This robot is obtained from the conventional PUMA robot by addition of a joint parallel to the elbow joint. For redundancy resolution the following approach is chosen. From the position and pointing direction of the end effector of the PUMA robot the position and orientation of the fourth link of a 4R-manipulato? is calculated and the redundancy is resolved for this manipulator. This is done by adding an equation to the relationship between the joint angles and position and orientation of the fourth link. By this approach a control strategy is derived that allows motions of the end effector of the PUMA robot in a large part of its work space and shows repeatable behaviour. Furthermore, the redundancy is utilized so that the fiexture of the wrist is kept small.

In this paper, a complete model of the elasto-kinematics is formulated in terms of a new kinematic notation, called the algebra of rotations. Based on this formulation, the elegant and concise expressions are derived for the displacement equation and especially the Jacobians governing the motion mapping between the manipulator tip and joint variables as well as link deflections. Introduction of the elasto-kinematics into the elasto-dynamics can directly take into consideration the nonlinear coupling between joint variables and link deflections, and thus improve the result of the elasto-dynamics.