In robotic applications, trajectory planning is pivotal, particularly in navigating rapidly changing and dynamic environments. This paper presents a comparison of Rapidly Exploring Random Trees (RRT) and Artificial Potential Fields (APF), and Particle swarm Optimization (PSO) based RRT-APF for collision-free trajectory planning in robotic welding scenarios. The proposed methodology addresses a multifaceted set of challenges, emphasizing critical factors such as joint acceleration, obstacle avoidance, and singularity considerations. Robotic welding scenarios pose intricate challenges, demanding a multifaceted approach to ensure both precision and safety. By merging the exploration capabilities of RRT with the obstacle avoidance prowess of APF, our approach not only mitigates the drawbacks associated with local minima problems in APF but also enhances the overall efficiency of RRT. This combination enables rapid exploration and obstacle avoidance, guided by a bias force to optimize trajectory planning for enhanced robotic performance. Furthermore, the methodology extends beyond collision-free trajectories, considering key constraints to provide a holistic solution. Addressing joint, and workspace limits, proposed PS-RRT-APF approach proves collision-free trajectories while addressing specific challenges such as obstacle avoidance, joint velocities, and motion limits. Through this innovative approach, we seek to enhance the overall performance of robotic path planning, ensuring efficiency, and safety in dynamic environments.

Recent use scenarios involving human-robot collaboration have revealed that the robots require elastic joints to safely interact with humans. It is also critical to know applied force/torque (f/t) during the interaction for control and motion planning purposes. In this article, we estimate the external f/t values without using any sensors other than low-cost encoders by exploiting the inherent elastic properties of the joint. For estimation, the following two different approaches are used: model based and model free. In the model-based approach, an extended Kalman filter (EKF) and an external force observer (EFOB) are used considering the dynamical behavior of the system to estimate the interaction force. In the model-free approach, the artificial neural network (ANN) utilizes the data gathered from mechanical systems. In comparative analysis, we have, therefore, considered three different estimation methods, two of which are model based and the remaining one is model free (i.e., data driven). Implementing these estimation algorithms experimentally on a variable stiffness joint, we performed an extensive evaluation of their performances. All methods show similar level of performance in terms of the root-mean-square (RMS) error with 0.0847, 0.0841, and 0.1082 N for the EKF, EFOB, and ANN, respectively. Model-based methods do not require continuous data stream through the experimental set up. On the other hand, the ANN does not need an explicit model of the system; therefore, it may become preferable when the detailed model derivation is not possible.