In this paper, we present a novel online global reactive motion planner that synergizes the benefits of reactive control and model predictive control (MPC). By applying circular fields, the planner significantly simplifies the problem of determining control inputs for mobile robots and manipulators, making real-time MPC feasible even in complex and dynamic three-dimensional environments. This approach utilizes the performance advantages of optimal control while maintaining reactivity to environmental changes and computational efficiency. The proposed motion planner is evaluated in various simulated scenarios, including complex dynamic environments with up to 100 moving obstacles, and is compared to different state-of-the-art approaches.

The rise of autonomous driving in everyday life makes efficient and collision-free motion planning more important than ever. However, multi robot applications in highly dynamic environments still pose hard challenges for state-of-the-art motion planners. In this paper, we present a new iteration of a reactive circular fields motion planner with the focus on simultaneous control of multiple robots in robotic soccer games, which is able to operate omnidirectional robots safely and efficiently despite high measurement delays and inaccuracies. Our extension enables the definition and effective execution of complex tasks in soccer specific problems. We extensively evaluated our planner in several complex simulation environments and experimentally verified the approach in realistic scenarios on real soccer robots. Furthermore, we demonstrated the capabilities of our motion planner during the successful participation in the RoboCup 2022 and 2023.

Future robots operating in fast-changing anthropomorphic environments need to be reactive, safe, flexible, and intuitively use both arms (comparable to humans) to handle task-space constrained manipulation scenarios. Furthermore, dynamic environments pose additional challenges for motion planning due to a continual requirement for validation and refinement of plans. This work addresses the issues with vector-field-based motion generation strategies, which are often prone to local-minima problems. We aim to bridge the gap between reactive solutions, global planning, and constrained cooperative (two-arm) manipulation in partially known surroundings. To this end, we introduce novel planning and real-time control strategies leveraging the geometry of the task space that are inherently coupled for seamless operation in dynamic scenarios. Our integrated multiagent global planning and control scheme explores controllable sets in the previously introducedcooperative dual task spaceand flexibly controls them by exploiting the redundancy of the high degree-of-freedom (DOF) system. The planning and control framework is extensively validated in complex, cluttered, and nonstationary simulation scenarios where our framework is able to complete constrained tasks in a reliable manner, whereas existing solutions fail. We also perform additional real-world experiments with a two-armed 14 DOF torque-controlled KoBo robot. Our rigorous simulation studies and real-world experiments reinforce the claim that the framework is able to run robustly within the inner loop of modern collaborative robots with vision feedback.

We discuss the concept of input-to-state stability in dynamical systems and its application to address the reachability problem in power systems, i.e., what are the bounds on all possible system trajectories given certain parameters are unknown but bounded. We illustrate the application of the concept with a single-machine infinite-bus system where the infinite bus voltage is not perfectly known except for an upper and lower bound.