An experimental study on the role of compliant elements on the locomotion of the self-reconfigurable modular robots Roombots

This paper presents the results of a study on the exploitation of compliance in structures made of self-reconfigurable modular robots - Roombots. This research was driven by the following three hypotheses: (1) compliance can improve locomotion performance; (2) different types of compliance will result in diverse locomotion behaviors; (3) control parameters optimized for a medium level of compliance will perform better for other values of compliance than parameters optimized for extremal compliance. Two types of in-series compliant elements were tested, with five different stiffness values for each of them, on a structure made of two Roombots modules. We ran dedicated on-line locomotion parameter optimizations for six different configurations and evaluated their performance for different stiffness values. Hypothesis 1 was confirmed for both types of compliant elements, with a peak of performance for an optimal level of compliance. The variety of locomotion strategies obtained for the different structures confirms hypothesis 2. Hypothesis 3 was only partially confirmed.

Stéphane Bonardi, Auke Ijspeert, Rico Möckel, Emmanuel Senft, Massimo Vespignani

2013

Gait Optimization for Roombots Modular Robots - Matching Simulation and Reality

Stéphane Bonardi, Auke Ijspeert, Rico Möckel, Soha Pouya, Alexander Spröwitz, Massimo Vespignani

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.

2013

Gait Optimization for Roombots Modular Robots - Matching Simulation and Reality

Stéphane Bonardi, Auke Ijspeert, Soha Pouya, Jesse van den Kieboom, Massimo Vespignani

The design of efficient locomotion gaits for robots with many degrees of freedom is challenging and time consuming 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 a factor of 2.5 to 30, at least, 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 in hardware. 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 robot behavior in simulation and hardware. We validate the proposed hybrid optimization method on a structure composed of two Roombots modules with a total number of six degrees of freedom. Roombots are self-reconfigurable modular robots that can form arbitrary structures with many degrees of freedom through an integrated active connection mechanism.

Ieee2013

Gait Optimization for Roombots Modular Robots - Matching Simulation and Reality

Stéphane Bonardi, Auke Ijspeert, Rico Möckel, Soha Pouya, Alexander Spröwitz, Massimo Vespignani

2013

Gait Optimization for Roombots Modular Robots - Matching Simulation and Reality

Stéphane Bonardi, Auke Ijspeert, Soha Pouya, Jesse van den Kieboom, Massimo Vespignani

Ieee2013