Planning multi-contact motions for legged robots to traverse uneven surfaces requires the resolution of complex mathematical problems. Indeed, due to perturbations that may arise in real world (e.g. environment changes), online motion planning becomes essential. To this end, Receding Horizon Planning (RHP) aims at constantly updating the motion plan for immediate execution based on the current state of the robot and its environment.

RHP requires the resolution of trajectory optimization problems, which are high-dimensional and nonlinear problems. The authors propose a RHP framework that is tackled by the efficient interior-point method of Artelys Knitro.

The proposed approach is tested on a humanoid robot walking on moderate slopes as well as large slopes where static stability cannot be achieved. Thanks to Artelys Knitro, their approach has a success rate of 98.6% for moderate slopes and 95% for large slopes against the 51.2% and 8% of the baseline method.