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.

Bio-inspired robots take advantage of millions of years of evolution to provide interesting and flexible solutions for issues related to motion and perception. Often, they have challenging kinematics classical robotics control mechanisms are not always able to take advantage of them. A concrete example of this is LAURON V, a six-legged robot for space exploration inspired by the stick insects. The main goals of this work is to combine classical behaviour-based control with motor primitives implemented with SNN for motion representation. We extend a previously presented bio-inspired approach to represent hand and arm motion using motor primitives, and combine it with a behaviour-based architecture to model different locomotion behaviours for a multi-legged robot. There are four main components. First, to model the individual leg motions we use two motor primitives implemented with spiking neural networks for the swing and stance phases. Second, to control the motor primitives of each leg there are local behaviours corresponding to each phase, and corresponding to each activation pattern. Third, the activation patterns are used to facilitate multi-leg coordination and generate different walking behaviours. Fourth, a high-level control interface integrates control signals from other sources and activates the patterns. We conducted five different experiments to evaluate our approach in a simulated environment using the Neurorobotics Platform (NRP). The results show that our modelling approach with motor primitives is flexible enough to represent different types of motions, and also highlight the value of the NRP for robotics development.