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This paper describes a real-time capable online path planning on roads and its experimental investigation for the highly maneuverable robotic electric vehicle research platform ROboMObil. The path planning algorithm is based on an efficiently solvable and compact optimization problem and contributes to the autonomous driving of centralized controlled vehicles. The necessary development from a global offline problem formulation towards an online receding horizon method is shown, which is capable of taking environmental changes into account. The online planned path together with a generated velocity profile serves as an input to the ROboMObil's geometric path following control, allowing for automated driving. Finally, a test drive shows the results of implementing the presented algorithms on this research vehicle and investigates the energy saving capabilities of the proposed path planning methodology.

Abstract-Trajectory generation for active physical assistance to humans in cooperative haptic tasks gains increasing interest in recent literature. Planning-based approaches represent one class of trajectory synthesis methods for active robotic partners. To overcome the limitations of kinematic planning algorithms in dynamic tasks, we propose a three-step approach to the synthesis of trajectories under the principle of least action. This is motivated by neuroscientific findings on human effort minimization in motor tasks. A trajectory is generated by optimized sequencing of optimal motion primitives. The benefits of the proposed method for physical human-robot cooperation are demonstrated in human user studies in a 2D cooperative transport task in a virtual maze.

This work describes the development and experimental validation of a geometric path following control strategy with demand supervision applied to an over-actuated robotic vehicle, the ROboMObil [1]. The proposed method enables the ROboMObil to automatically follow paths while the driver is free to control the velocity along the path. Beside the longitudinal degree of freedom, two lateral degrees of freedom can be controlled relative to the path. If this demand interface were provided without supervision, the driver may potentially overwrite the path following control in a manner such that the vehicle limits are violated and the vehicle becomes unstable. To avoid such critical situations a demand supervisor is introduced into the path following framework. The work concludes by a simulative demonstration of the supervised control system and an experimental validation of the presented approach implemented in the ROboMObil.