The increasing adoption of robotic manipulators in the industry has brought the issue of robotic device safety to the forefront, particularly in the context of collaborative robotics. Among the main challenges, the evaluation of impacts with operators and objects in the workspace represents a critical issue, as accurately quantifying the magnitude of collisions is a complex task. Although current regulations propose various models for impact assessment, the suggested approaches are often oversimplified and not easily applicable in complex scenarios. To overcome this limitation, this study proposes the development of an effective mass impact model that simulates the collision between two bodies as an interaction between two lumped masses. The model is developed for both two-dimensional and three-dimensional analyses. Furthermore, it is implemented in a generic form and using a lumped-parameter robot model. This formulation enables the creation of a highly versatile model capable of overcoming the lack of knowledge of inertial parameters of robots. The approach is experimentally validated by means of a SCARA robot that collides with a cart, demonstrating the model’s applicability and accuracy.

Motion planning for high-DOF multi-arm systems operating in complex environments remains a challenging problem, with many motion planning algorithms requiring evaluation of the minimum collision distance and its derivative. Because of the computational complexity of calculating the collision distance, recent methods have attempted to leverage data-driven machine learning methods to learn the collision distance. Because of the significant training dataset requirements for high-DOF robots, existing kernel-based methods, which require $O(N^2)$ memory and computation resources, where $N$ denotes the number of dataset points, often perform poorly. This paper proposes a new active learning method for learning the collision distance function that overcomes the limitations of existing methods: (i) the size of the training dataset remains fixed, with the dataset containing more points near the collision boundary as learning proceeds, and (ii) calculating collision distances in the higher-dimensional link $SE(3)^n$ configuration space – here $n$ denotes the number of links – leads to more accurate and robust collision distance function learning. Performance evaluations with high-DOF multi-arm robot systems demonstrate the advantages of the proposed active learning-based strategy vis-$\grave{\text{a}}$-vis existing learning-based methods.