Few-shot object detection is especially interesting for applications with mobile robots and becomes even more challenging when task-related classes are very similar. This work focuses on such a scenario: detecting different types of household and industrial tools. Such tools can be rare and specific and are usually not covered by existing large datasets, except for common ones such as screwdrivers. Additionally, the target classes might change frequently depending on the robot’s missions. Therefore, we propose DE-fine-ViT, a fine-grained few-shot object detection model that does not require fine-tuning. We build our architecture on top of the elaborate DE-ViT model, extending it with specialized components to improve the fine-grained detection capabilities. The user can construct class and part prototypes tailored to the task in an interactive preparation phase. During inference, our proposed reevaluation module leverages the multi-granularity of prototypes for fine-grained class differentiation. We evaluate our model in multiple realistic experiments, including a specifically created fine-grained dataset, demonstrating its efficacy and suitability for scenarios with little data and low inter-class variance.

Motion planning for mobile robot platforms is one of the long-established research fields in robotics. In this paper, we propose a trajectory planner for mobile holonomic robots to steer non-holonomic conventional passive wheelchairs in dynamic environments. The challenges to overcome when steering a wheelchair are to find smooth feasible trajectories, maintain a fast reactive response to dynamic obstacles and to satisfy a set of additional constraints such as limiting physical forces acting on the wheelchair occupants. Our approach is a variant of the timed-elastic-bands (TEB) planner, which includes a footprint of the wheelchair during optimization, and generates a steering angle which is then consumed by an arm controller to actuate the relative orientation between the wheelchair and the mobile platform. This is realized by posing new non-holonomic and kinodynamic constraints on the TEB planner and an implementation of a suitable real-time dual-arm controller for executing steering commands. We demonstrate our results based on a TEB baseline comparison in simulation using functional models of our robot HoLLiE and a wheelchair.