A Comparative Study of Adaptive Interlimb Coordination Mechanisms for Self-Organized Robot Locomotion

Abstract

Walking animals demonstrate impressive self-organized locomotion and adaptation to body property changes by skillfully manipulating their complicated and redundant musculoskeletal systems. Adaptive interlimb coordination plays a crucial role in this achievement. It has been identified that interlimb coordination is generated through dynamical interactions between the neural system, musculoskeletal system, and environment. Based on this principle, two classical interlimb coordination mechanisms (continuous phase modulation and phase resetting) have been proposed independently. These mechanisms use decoupled central pattern generators (CPGs) with sensory feedback, such as ground reaction forces (GRFs), to generate robot locomotion autonomously without predefining it (i.e., self-organized locomotion). A comparative study was conducted on the two mechanisms under decoupled CPG-based control implemented on a quadruped robot in simulation. Their characteristics were compared by observing their CPG phase convergence processes at different control parameter values. Additionally, the mechanisms were investigated when the robot faced various unexpected situations, such as noisy feedback, leg motor damage, and carrying a load. The comparative study reveals that the phase modulation and resetting mechanisms demonstrate satisfactory performance when they are subjected to symmetric and asymmetric GRF distributions, respectively. This work also suggests a strategy for the appropriate selection of adaptive interlimb coordination mechanisms under different conditions and for the optimal setting of their control parameter values to enhance their control performance.

Robust and reusable self-organized locomotion of legged robots under adaptive physical and neural communications

“…Similar to the setup of the proposed method, both methods also use GRFs to modulate the phase relationships of distributed (decoupled) CPGs to generate self-organized locomotion. Continuous GRFs are typically used to modulate CPG phases in the Tegotae method (also known as phase modulation Sun et al, 2021b). The PR method, on the other hand, uses discrete GRFs to periodically reset the CPG phases.…”

“…The APNC-based control method employed an adaptive feedback gain to properly accelerate the effect of the continuous phase modulation; thus, it achieved faster phase convergence than the Tegotae-based control method, which employed a fixed predefined feedback gain. The PR-based control method used discrete GRF feedback to periodically reset the CPG phases, which also led to fast phase convergence (see Sun et al, 2021b for further comparative analysis of the continuous phase modulation and PR).…”

“…However, the effectiveness of the physical communication for locomotion generation significantly depends on the uninterrupted functionality of the load-sensing feedback (i.e., the GRF). Furthermore, the feedback gain must be predefined and stable locomotion convergence cannot be guaranteed if the sensory feedback encounters disturbances or produces an unstable pattern (Sun et al, 2021b). Another drawback is the impossibility of storing the generated locomotion (i.e., no motor memory).…”

Editorial: Biological and Robotic Inter-Limb Coordination

“…In addition to the snake and biped robots, several studies in this special issue employs quadruped robots as their experimental platforms in order to investigate and develop control mechanisms for adaptive inter-limb coordination. Sun et al (2021) employed a simulated quadruped robot to investigate two classical adaptive inter-limb coordination mechanisms: continuous phase modulation (also known as Tegotae) and phase resetting. These mechanisms use decoupled neural central pattern generators (CPGs) or local neural control circuits with sensory feedback, such as GRFs, to generate self-organized robot locomotion.…”

Insect-Inspired Robots: Bridging Biological and Artificial Systems

“…(a) Bio-inspired control. The upper-inset shows distributed decentralized CPGs with force feedback[158][159][160]. The middle inset shows the Walknet-control (modified from[143,161]).…”