In human-robot collaboration, robots must adapt their motions to dynamic environments while ensuring safety, predictability, and compliance with physical constraints. Frequent trajectory replanning can compromise motion smoothness and lead to unsafe or uncomfortable interactions. This paper introduces THOR (Trajectory receding HOrizon interpolatoR), a model predictive control algorithm in joint space that explicitly minimizes jerk during execution to generate smooth, dynamically feasible trajectories. THOR continuously adapts the trajectory in response to real-time changes, such as path replanning or safety-induced slowdowns, while respecting joint limits on position, velocity and acceleration. THOR is validated through extensive simulation and real-world experiments with a 6-DoF collaborative robotic cell. Results show that THOR significantly reduces jerk and improves motion continuity compared to standard approaches, making it particularly well-suited for responsive and safe behavior in human-robot collaboration scenarios.

This paper addresses motion replanning in human-robot collaborative scenarios, with an emphasis on reactivity and safety-compliant efficiency. While existing human-aware motion planners perform well in structured environments, they often struggle with unpredictable human behavior. This can result in safety measures that hinder the robot’s performance and overall throughput. This study combines reactive path replanning and a safety-aware cost function, enabling the robot to adapt its path to the changes in the scene in real-time. This solution reduces the execution time and trajectory slowdowns while ensuring safety. Simulations and real-world experiments show the method’s effectiveness compared to standard human-robot cooperation approaches, with efficiency enhancements of up to 60%.

Predicting human motion is vital for enhancing safety and efficiency in human-robot collaboration. Researchers have dedicated significant efforts to developing accurate human models, often involving optimization and task-specific information. However, regardless of complexity, all models come with uncertainties that robots need to recognize to make informed decisions. This paper examines the performance of two simple models using the Unscented Kalman Filter (UKF) to filter and predict future human poses. Moreover, a combined version of the models is implemented using an Interacting Multiple Model (IMM) estimator. The objective is to evaluate the algorithms' prediction accuracy and uncertainty across various human-robot interaction scenarios under different operating conditions. This analysis identifies suitable settings where the simple model can be effective and highlights situations where a more complex system might be necessary.

With the spread of robots in unstructured, dynamic environments, the topic of path replanning has gained importance in the robotics community. Although the number of replanning strategies has significantly increased, there is a lack of agreed-upon libraries and tools, making the use, development, and benchmarking of new algorithms arduous. This paper introduces OpenMORE, a new open-source ROS-based C++ library for sampling-based path replanning algorithms. The library builds a framework that allows for continuous replanning and collision checking of the traversed path during the execution of the robot trajectory. Users can solve replanning tasks exploiting the already available algorithms and can easily integrate new ones, leveraging the library to manage the entire execution.

Robots working in proximity of humans often need to change their motion to avoid collisions and interference with the operators. This paper uses a path re-planning approach to change the robot path online when the human operator is in the robot way. The method exploits a set of pre-computed paths to compute a new feasible path in case of obstruction to enhance the trajectory’s readability. Moreover, the algorithm iteratively optimizes the current solution in an anytime fashion to deal with strict computing time requirements. Experimental results show the method’s effectiveness in a collaborative cell, compared with industry best practices.