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Objectives. We aimed to develop a robotic interface capable of providing finely-tuned, multidirectional trunk assistance adjusted in real-time during unconstrained locomotion in rats and mice. Approach. We interfaced a large-scale robotic structure actuated in four degrees of freedom to exchangeable attachment modules exhibiting selective compliance along distinct directions. This combination allowed high-precision force and torque control in multiple directions over a large workspace. We next designed a neurorobotic platform wherein real-time kinematics and physiological signals directly adjust robotic actuation and prosthetic actions. We tested the performance of this platform in both rats and mice with spinal cord injury. Main Results. Kinematic analyses showed that the robotic interface did not impede locomotor movements of lightweight mice that walked freely along paths with changing directions and height profiles. Personalized trunk assistance instantly enabled coordinated locomotion in mice and rats with severe hindlimb motor deficits. Closed-loop control of robotic actuation based on ongoing movement features enabled real-time control of electromyographic activity in anti-gravity muscles during locomotion. Significance. This neurorobotic platform will support the study of the mechanisms underlying the therapeutic effects of locomotor prosthetics and rehabilitation using high-resolution genetic tools in rodent models.
The design of efficient locomotion controllers for arbitrary structures of reconfigurable modular robots is challenging because the morphology of the structure can change dynamically during the completion of a task. In this paper, we propose a new method to automatically generate reduced Central Pattern Generator (CPG) networks for locomotion control based on the detection of bio-inspired sub-structures, like body and limbs, and articulation joints inside the robotic structure. We demonstrate how that information, coupled with the potential symmetries in the structure, can be used to speed up the optimization of the gaits and investigate its impact on the solution quality (i.e. the velocity of the robotic structure and the potential internal collisions between robotic modules). We tested our approach on three simulated structures and observed that the reduced network topologies in the first iterations of the optimization process performed significantly better than the fully open ones.
The design of efficient locomotion gaits for robots with many degrees of freedom is challenging and time con- suming even if optimization techniques are applied. Control parameters can be found through optimization in two ways: (i) through online optimization where the performance of a robot is measured while trying different control parameters on the actual hardware and (ii) through offline optimization by simulating the robot’s behavior with the help of models of the robot and its environment. In this paper, we present a hybrid optimization method that combines the best properties of online and offline optimization to efficiently find locomotion gaits for arbitrary structures. In comparison to pure online optimization both the number of experiments using robotic hardware as well as the total time required for finding efficient locomotion gaits get highly reduced by running the major part of the optimization process in simulation using a cluster of processors. The presented example shows that even for robots with a low number of degrees of freedom the time required for optimization can be reduced by at least a factor of 2.5 to 30 depending on how extensive the search for optimized control parameters should be. Time for hardware experiments becomes minimal. More importantly gaits that can possibly damage the robotic hardware can be filtered before being tried. Yet in contrast to pure offline optimization we reach well matched behavior that allows a direct transfer of locomotion gaits from simulation to hardware. This is because through a meta-optimization we adapt not only the locomotion parameters but also the parameters for simulation models of the robot and environment allowing for a good matching of the behavior of simulated and hardware robot structures. We verify the proposed hybrid optimization method on a structure composed of two Roombots modules. Roombots are self-reconfigurable modular robots that can form arbitrary structures with many degrees of freedom through an integrated active connection mechanism.
This paper presents the results of a study on the effect of in-series compliance on the locomotion of a simulated 8-DoF Lola-OP Modular Snake Robot with added compliant elements. We explore whether there is an optimal stiffness for gait, terrain type, or several gaits and several terrains (i.e. a good “general-purpose” stiffness). Compliance was simulated using ball joints with eight different levels of stiffness. Two snake locomotion gaits (rolling and sidewinding) were tested over flat ground and three different types of rough terrains. We performed grid search and Particle Swarm Optimization to identify the locomotion parameters leading to fast locomotion and analyzed the best candidates in terms of locomotion speed and energy efficiency (cost of transport). Contrary to our expectations, we did not observe a clear trend that would favor the use of compliant elements over rigid structures. For sidewinding, compliant and stiff elements lead to comparable performances. For rolling gait, the general rule seems to be “the stiffer, the better”.