Force sensing can help robots reconstruct potential energy landscape and guide locomotor transitions to traverse large obstacles

Legged robots already excel at maintaining stability during upright walking and running to step over small obstacles. However, they must further traverse large obstacles comparable to body size to enable a broader range of applications like search and rescue in rubble and sample collection in rocky Martian hills. Our lab's recent research demonstrated that legged robots can traverse large obstacles if they can be destabilized to transition across various locomotor modes. When viewed on a potential energy landscape of the system, which results from physical interaction with obstacles, these locomotor transitions are strenuous barrier-crossing transitions between landscape basins. Because potential energy landscape gradients are conservative forces, we hypothesize that sensing obstacle interaction forces allows landscape reconstruction, which can guide robots to cross barriers at the saddle to make transitions more easily (analogous to crossing a mountain ridge at its saddle). Here, we created a robophysical model with custom 3-axis force sensors and surface contact sensors to measure forces during interaction with large obstacles. We found that the measured forces indeed well captured potential energy landscape gradients. Our next step is to understand how to use the locally measured gradients to sufficiently reconstruct the potential energy landscape so that saddles can be identified. This will enable robots to make locomotor transitions at the landscape saddle with the lowest resistance.