[Paper Review] Environmental force sensing helps robots traverse cluttered large obstacles using physical interaction

Qihan Xuan, Chen Li|arXiv (Cornell University)|Dec 15, 2021

Robotic Locomotion and Control4 citations

TL;DR

This paper proposes a force-feedback control strategy that enables robots to traverse cluttered, large obstacles using environmental force sensing and physical interaction. By estimating beam stiffness from sensed forces via a physics model, the robot transitions between locomotor modes (e.g., pitch to roll) to reduce energy use and increase traversal success, outperforming fixed pushing or avoidance strategies in simulation.

ABSTRACT

Many applications require robots to move through complex 3-D terrain with large obstacles, such as self-driving, search and rescue, and extraterrestrial exploration. Although robots are already excellent at avoiding sparse obstacles, they still struggle in traversing cluttered large obstacles. To make progress, we need to better understand how to use and control the physical interaction with obstacles to traverse them. Forest floor-dwelling cockroaches can use physical interaction to transition between different locomotor modes to traverse flexible, grass-like beams of a large range of stiffness. Inspired by this, here we studied whether and how environmental force sensing helps robots make active adjustments to traverse cluttered large obstacles. We developed a physics model and a simulation of a minimalistic robot capable of sensing environmental forces during traversal of beam obstacles. Then, we developed a force-feedback control strategy, which estimated beam stiffness from the sensed contact force using the physics model. Then in simulation we used the estimated stiffness to control the robot to either stay in or transition to the more favorable locomotor modes to traverse. When beams were stiff, force sensing induced the robot to transition from a more costly pitch mode to a less costly roll mode, which helped the robot traverse with a higher success rate and less energy consumed. By contrast, if the robot simply pushed forward or always avoided obstacles, it would consume more energy, become stuck in front of beams, or even flip over. When the beams were flimsy, force sensing guided the robot to simply push across the beams. In addition, we demonstrated the robustness of beam stiffness estimation against body oscillations, randomness in oscillation, and uncertainty in position sensing. We also found that a shorter sensorimotor delay reduced energy cost of traversal.

Motivation & Objective

• To understand how robots can use physical interaction with large, flexible obstacles to improve traversal in complex 3D terrain.
• To investigate whether environmental force sensing enables active adaptation during obstacle traversal.
• To develop a control strategy that uses force feedback to estimate obstacle stiffness and select optimal locomotor modes.
• To evaluate the robustness of stiffness estimation under body oscillations, sensor noise, and position uncertainty.
• To quantify the impact of sensorimotor delay on energy efficiency during traversal.

Proposed method

• A minimalistic robot model was developed with a physics-based simulation of interaction with beam-like obstacles.
• A physics model was used to relate contact force to beam stiffness during traversal.
• Force feedback was used to estimate beam stiffness in real time from sensed contact forces.
• A control strategy was implemented to switch between locomotor modes (e.g., pitch and roll) based on estimated stiffness.
• The system was tested under varying conditions, including body oscillations, random perturbations, and uncertain position sensing.
• Sensorimotor delay was varied to assess its effect on energy cost and traversal performance.

Experimental results

Research questions

• RQ1 Can environmental force sensing enable robots to actively adapt their locomotion mode when encountering large, flexible obstacles?
• RQ2 How does stiffness estimation from contact forces improve traversal success and energy efficiency?
• RQ3 What is the impact of sensorimotor delay on the energy cost of obstacle traversal?
• RQ4 How robust is the stiffness estimation method to body oscillations and sensor noise?
• RQ5 Does transitioning between locomotor modes based on stiffness estimation reduce energy consumption compared to fixed strategies?

Key findings

• When beams were stiff, force sensing enabled the robot to transition from a costly pitch mode to a more efficient roll mode, increasing traversal success rate and reducing energy use.
• For flimsy beams, force sensing allowed the robot to simply push across without complex mode transitions, maintaining high success and low energy cost.
• The stiffness estimation method remained robust under body oscillations, random oscillation patterns, and uncertainty in position sensing.
• Shorter sensorimotor delays led to lower energy costs during traversal, highlighting the importance of timely feedback.
• Fixed strategies—such as always pushing forward or always avoiding obstacles—resulted in higher energy consumption, stalling, or flipping, especially on stiff beams.