Bio-inspired swing leg control for spring-mass robots running on ground with unexpected height disturbance

We proposed three swing leg control policies for spring-mass running robots, inspired by experimental data from our recent collaborative work on ground running birds. Previous investigations suggest that animals may prioritize injury avoidance and/or efficiency as their objective function during running rather than maintaining limit-cycle stability. Therefore, in this study we targeted structural capacity (maximum leg force to avoid damage) and efficiency as the main goals for our control policies, since these objective functions are crucial to reduce motor size and structure weight. Each proposed policy controls the leg angle as a function of time during flight phase such that its objective function during the subsequent stance phase is regulated. The three objective functions that are regulated in the control policies are (i) the leg peak force, (ii) the axial impulse, and (iii) the leg actuator work. It should be noted that each control policy regulates one single objective function. Surprisingly, all three swing leg control policies result in nearly identical subsequent stance phase dynamics. This implies that the implementation of any of the proposed control policies would satisfy both goals (damage avoidance and efficiency) at once. Furthermore, all three control policies require a surprisingly simple leg angle adjustment: leg retraction with constant angular acceleration

Power consumption analysis of different hexapod robot gaits.

The paper is focused on the power consumption analysis of different gaits of our constructed hexapod robot controlled by different Central Pattern Generator (CPG) models. There are a lot of gait patterns in the literature constructed either by different CPG models or using a series of oscillations with adjustable phase lag. The mentioned models, as well as those proposed in our previous paper are used and compared from the viewpoint of energy demand. In general, power consumption of the constructed hexapod robot is experimentally analyzed based on the current consumption in the applied servo motors, which drive the robot limbs. For this purpose the suitable drivers allowing a simple measurement of electric energy consumption of servo motors are used. The obtained experimental results show different energy demand for different robot gaits. Because power consumption is one of the main operational restrictions imposed on autonomous walking robots, we show that the performed energy efficiency analysis and the choice of the appropriate robot gaits depending on the actual situation can reduce the energy costs

The Role of Reflexes Versus Central Pattern Generators

Animals execute locomotor behaviors and more with ease. They have evolved these breath-taking abilities over millions of years. Cheetahs can run, dolphins can swim and flies can fly like no artificial technology can. It is often argued that if human technology could mimic nature, then biological-like performance would follow. Unfortunately, the blind copying or mimicking of a part of nature [Ritzmann et al., 2000] does not often lead to the best design for a variety of reasons [Vogel, 1998]. Evolution works on the just good enough principle. Optimal designs are not the necessary end product of evolution. Multiple satisfactory solutions can result in similar performances. Animals do bring to our attention amazing designs, but these designs carry with them the baggage of their history. Moreover, natural design is constrained by factors that may have no relationship to human engineered designs. Animals must be able to grow over time, but still function along the way. Finally, animals are complex and their parts serve multiple functions, not simply the one we happen to examine. In short, in their daunting complexity and integrated function, understanding animal behaviors remains as intractable as their capabilities are tantalizing

On the Evolutionary Co-Adaptation of Morphology and Distributed Neural Controllers in Adaptive Agents

The attempt to evolve complete embodied and situated artificial creatures in which both morphological and control characteristics are adapted during the evolutionary process has been and still represents a long term goal key for the artificial life and the evolutionary robotics community. Loosely inspired by ancient biological organisms which are not provided with a central nervous system and by simple organisms such as stick insects, this thesis proposes a new genotype encoding which allows development and evolution of mor- phology and neural controller in artificial agents provided with a distributed neural network. In order to understand if this kind of network is appropriate for the evolution of non trivial behaviours in artificial agents, two experiments (description and results will be shown in chapter 3) in which evolution was applied only to the controller’s parameters were performed. The results obtained in the first experiment demonstrated how distributed neural networks can achieve a good level of organization by synchronizing the output of oscillatory elements exploiting acceleration/deceleration mechanisms based on local interactions. In the second experiment few variants on the topology of neural architecture were introduced. Results showed how this new control system was able to coordinate the legs of a simulated hexapod robot on two different gaits on the basis of the external circumstances. After this preliminary and successful investigation, a new genotype encoding able to develop and evolve artificial agents with no fixed morphology and with a distributed neural controller was proposed. A second set of experiments was thus performed and the results obtained confirmed both the effectiveness of genotype encoding and the ability of distributed neural network to perform the given task. The results have also shown the strength of genotype both in generating a wide range of different morphological structures and in favouring a direct co-adaptation between neural controller and morphology during the evolutionary process. Furthermore the simplicity of the proposed model has showed the effective role of specific elements in evolutionary experiments. In particular it has demonstrated the importance of the environment and its complexity in evolving non-trivial behaviours and also how adding an independent component to the fitness function could help the evolutionary process exploring a larger space solutions avoiding a premature convergence towards suboptimal solutions

Spiking Neural Network that Maps from Generalized Coordinates to Cartesian Coordinates

In this thesis, I look to understand how insects compute task-level quantities by integrating range-fractionated sensory signals to create a sparse-spatial coding of Cartesian positions. I created biologically plausible 2-D and 3-D models of one species of the stick insect (Carausius morosus) leg and encoded the foot position through a spiking neural network. This model used spiking afferents from three angles of an insect leg which are integrated by one non-spiking interneuron. This model contains many dendritic compartments and one somatic compartment that encode the foot’s position relative to the body. The Functional Subnetwork Approach (FSA) was used to tune the conductances between the compartments (Szczecinski et al., 2017). Also, the Product of Exponentials (POE) was used to calculate the spatial kinematic chain of the stick insect leg (Murray et al., 1994). The system accurately encodes the foot position and depends on the width of the sensory encoding curves, or the “bell curves”. Discussion of limitations and other studies that relate to this work, as well as motivation for future work are included

Optimizing the structure and movement of a robotic bat with biological kinematic synergies

In this article, we present methods to optimize the design and flight characteristics of a biologically inspired bat-like robot. In previous, work we have designed the topological structure for the wing kinematics of this robot; here we present methods to optimize the geometry of this structure, and to compute actuator trajectories such that its wingbeat pattern closely matches biological counterparts. Our approach is motivated by recent studies on biological bat flight that have shown that the salient aspects of wing motion can be accurately represented in a low-dimensional space. Although bats have over 40 degrees of freedom (DoFs), our robot possesses several biologically meaningful morphing specializations. We use principal component analysis (PCA) to characterize the two most dominant modes of biological bat flight kinematics, and we optimize our robot’s parametric kinematics to mimic these. The method yields a robot that is reduced from five degrees of actuation (DoAs) to just three, and that actively folds its wings within a wingbeat period. As a result of mimicking synergies, the robot produces an average net lift improvesment of 89% over the same robot when its wings cannot fold

Biologically-plausible six-legged running : control and simulation

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (p. 63-66).This thesis presents a controller which produces a stable, dynamic 1.4 meter per second run in a simulated twelve degree of freedom six-legged robot. The algorithm is relatively simple; it consists of only a few hand-tuned feedback loops and is defined by a total of 13 parameters. The control utilizes no vestibular-type inputs to actively control orientation. Evidence from perturbation, robustness, motion analysis, and parameter sensitivity tests indicate a high degree of stability in the simulated gait. The control approach generates a run with an aerial phase, utilizes force information to signal aerial phase leg retraction, has a forward running velocity determined by a single parameter, and couples stance and swing legs using angular momentum information. Both the hypotheses behind the control and the resulting gait are argued to be plausible models of biological locomotion.by Matthew David Malchano.M.Eng