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.

Autonomous driving requires decision making in dynamic and uncertain environments. The uncertainty from the prediction originates from the noisy sensor data and from the fact that the intention of human drivers cannot be directly measured. This problem is formulated as a partially observable Markov decision process (POMDP) with the intention of the other vehicles as hidden variables. The solution of the POMDP is a policy determining the optimal acceleration of the ego vehicle along a preplanned path. Therefore, the policy is optimized for the most likely future scenarios resulting from an interactive, probabilistic motion model for the other vehicles. Considering possible future measurements of the surroundings allows the autonomous car to incorporate the estimated change in future prediction accuracy in the optimal policy. A compact representation allows a low-dimensional state-space so that the problem can be solved online for varying road layouts and number of other vehicles. This is done with a point-based solver in an anytime fashion on a continuous state-space. We show the results with simulations for the crossing of complex (unsignalized) intersections. Our approach performs nearly as good as with full prior information about the intentions of the other vehicles and clearly outperforms reactive approaches.