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This paper aims to present a new learning from demonstration-based trajectory planner that generalizes and extracts relevant features of the desired motion for an industrial robot.

The proposed trajectory planner is based on the concept of human arm motion imitation by the robot end-effector. The teleoperation-based real-time control architecture is used for direct and effective imitation learning. Using this architecture, a self-sufficient trajectory planner is designed which has inbuilt mapping strategy and direct learning ability. The proposed approach is also compared with the conventional robot programming approach.

The developed planner was implemented on the 5 degrees-of-freedom industrial robot SCORBOT ER-4u for an object manipulation task. The experimental results revealed that despite morphological differences, the robot imitated the demonstrated trajectory with more than 90 per cent geometric similarity and 60 per cent of the demonstrations were successfully learned by the robot with good positioning accuracy. The proposed planner shows an upper hand over the existing approach in robustness and operational ease.

The approach assumes that the human demonstrator has the requisite expertise of the task demonstration and robot teleoperation. Moreover, the kinematic capabilities and the workspace conditions of the robot are known a priori.

The real-time implementation of the proposed methodology is possible and can be successfully used for industrial automation with very little knowledge of robot programming. The proposed approach reduces the complexities involved in robot programming by direct learning of the task from the demonstration given by the teacher.

This paper discusses a new framework blended with teleoperation and kinematic considerations of the Cartesian space, as well joint space of human and industrial robot and optimization for the robot programming by demonstration.

This paper aims to present a new offline robot programming approach for automated trajectory generation on free-form surfaces targeted toward spray painting application.

In this paper, an incremental trajectory generation approach is developed where new paint passes are generated based on paint deposited on the surface as a result of previous paint passes. The trajectory is generated on real surfaces where optimal velocity is calculated using genetic algorithm considering parameters such as surface model, spray gun model, paint distribution model and task constraints.

The developed approach was implemented on various surfaces for different paint distribution patterns, and the simulation results reveal that the approach is flexible and efficient to handle variety in part geometry and paint distribution. From experimental validation and analysis of results thus obtained, the developed approach is highly promising compared to the existing methods.

The approach assumes that the computer-aided design (CAD) model of the surface is available and is limited to surjective surfaces in a structured environment where the spray gun characteristics and process parameters are known beforehand.

The problem of programming a robot manually is overcome by automatically generating a sub-optimal trajectory which can be easily transferred to an industrial robot for spray painting the surface.

This paper discusses a new approach for automated trajectory generation from CAD model. The experimental validation of the developed approach is successfully performed on a highly curved test surface, and obtained results are in agreement with the simulation results.