Bayesian inference provides a probabilistic reasoning process for drawing conclusions based on imprecise and uncertain data that has been successful in many applications within robotics and information processing, but is most often considered in terms of data analysis rather than synthesis of behaviours. This paper presents the use of Bayesian inference as a means by which to perform Boolean operations in a logic programme while incorporating and propagating uncertainty information through logic operations by inference. Boolean logic operations are implemented in a Bayesian network of Bernoulli random variables with tensor-based discrete distributions to enable probabilistic hybrid logic programming of a robot. This enables Bayesian inference operations to coexist with Boolean logic in a unified system while retaining the ability to capture uncertainty by means of discrete probability distributions. Using a discrete Bayesian network with both Boolean and Bayesian elements, the proposed methodology is applied to navigate a mobile robot using hybrid Bayesian and Boolean operations to illustrate how this new approach improves robotic performance by inclusion of uncertainty without increasing the number of logic elements required. As any logical system could be programmed in this manner to integrate uncertainty into decision-making, this methodology can benefit a wide range of applications that use discrete or probabilistic logic.

ISRA is a support system for robot programming. It provides a means to automatically (guided by knowledge) convert a user's request, expressed in the natural language, into the appropriate conceptual model of the required task. This model incorporates information required for the understanding, planning and sensory-guided performance of the required robotic task.

To develop this system we applied the Natural Computation method. We considered natural information processing by humans during synthesis and interpretation of robotic programs, and then constructed an approximate conceptual model of the relevant dynamically changing real world.

Such a model has to be suitable (e.g. representable and executable) for efficient computer processing. This paper presents the results of a case study which shows that methods built by formalizing human information processing in the robotic domain may be efficiently and naturally implemented in ISRA. It provides evidence that the structure and behavior of ISRA's competence representation and algorithm are comparable with the psychological behavior of humans, as required by the Natural Computation method.

The case study illustrates the use of ISRA for the first phase of an assembly program synthesis i.e. planning of all valid assembly sequences.

The paper presents an approach to automatic synthesis of program for robots in a flexible manufacturing cell (FMC). The system of program generation consists of two layers: Task-Level Programming Layer and Program Interpretation and Verification Layer. The first layer uses robot-independent planning techniques to create a work plan for robots (set of elementary actions) and program for each elementary action. The second layer uses robot-dependent planning methods to plan robot's trajectories and calculate the robot's motion times. A simulation model of whole FMC, which is created based on a description of FMC and program for robots, makes possible evaluation of efficiency of FMC work.

Despite the numerous technical improvements, robot programming is still today a very tedious activity requiring considerable efforts for fine tuning programs written off-line. Moreover, such programs are usually not particularly portable from one robot to another, sometimes even when the robots are from the same maker.

The issue addressed in the work reported here is how to overcome these shortcomings. In this respect, the argument is to develop programs within the behaviour-based assembly paradigm which views tasks in terms of goal-achieving entities (behaviours). The focus on the description of a task more than its implementation is the key to generality and portability. As shown with the aid of two similar, but not identical, assembly benchmarks, behavioural programs are independent of any specificity of each assembly kit and can, indeed, be constructed using the very same code.

In this paper a procedure is proposed for a rational and optimum use of robots. It is the result of the lessons learned by the author in the course of his consultancy and teaching activities on manipulative tasks and programming capabilities of robots.

One of the chief aims of this paper is educational, and has been set on the basis of students' practice with robots in the Laboratory of Robotics at the University of Cassino, Italy. A second goal consists in highlighting and teaching how robots' versatility and flexibility can be easily exploited during robot's operation when a task is properly modelled through elementary actions. An elementary action, representing a small manipulative operation and consisting in one or few simple instructions given in robot language, can be easily performed by the robot's programming capability. Some examples have been reported to better illustrate the ideas and the procedure proposed, whose main advantage, also from an educational viewpoint, consist in providing a simpler method for analysing manipulations for robot design and programming.

The paper presents an object-oriented approach to the implementation of a software library (MRROC+) which contains building blocks for the construction of multi-robot system controllers tailored to meet specific demands of a task at hand. Moreover, the paper supplies a brief overview of robot programming methods.