In-space assembly is crucial to creating large-scale space structures and enabling long term space missions. Natural limitations in the size of transportation vehicles and ISRU production facilities necessitate an additive strategy with the size of the typical structural unit being essentially fixed and inversely proportional to the final assembly size. In prior robotic and space assembly examples, reversible mechanical integration of structural modules is typically achieved with actuated alignment and fastening mechanisms onboard every structural module. Additive assembly or manufacturing planning approaches often feature a “build front” that receives new materials or parts and progresses gradually across the target geometry. The system we describe here places much of the alignment and fastener actuation systems onboard a mobile robot that can operate at a build front while companion robots (Scaling Omni-directional Lattice Locomoting Explorer, SOLL-E) provide part or material transportation. The design and evaluation of this Mobile Meta-Material Interior Co-Integrator (MMIC-I), an inchworm-style locomoting robotic assembler, is described here with an emphasis on ease of assembly and a low number of unique parts for a simple design. It is designed to assist in alignment of cuboctahedron structural unit cells with captive fasteners, defining the build front in operation. Adjacent structural unit cells are locked together with specified axial and rotational actuation of the fasteners. Hardware prototypes show that the robot is able to successfully locomote to any indexed location within a lattice structure and bolt together each set of fasteners on any interface.

This paper presents the design and development of a transport and placement robot that demonstrates autonomous assembly of structural building blocks. The robots are intended to serve as a critical component of automated structural assembly and maintenance systems. The Scaling Omni-directional Lattice Locomoting Explorer (SOLL-E) uses a 5-DoF bipedal inchworm locomotion architecture with locking foot and cargo grippers. The locomotion system employs large magnet gap diameter BLDC motors with moderate timing belt gearing for primary joints, and DC planetary gearmotors for turning. Foot and cargo grippers are identical, with servo-actuated locking mechanisms. Three modular controller boards are used to control these actuators in real-time, with command and telemetry data transferred between the server and each controller board via WiFi. Functionality and performance were evaluated in a ground demonstration.

Using a new hardware implementation of our designs for tunably compliant spine-like tensegrity robots, we show that the NASA Tensegrity Robotics Toolkit can effectively generate and predict desirable locomotion strategies for these many degree of freedom systems. Tensegrity, which provides structural integrity through a tension network, shows promise as a design strategy for more compliant robots capable of interaction with rugged environments, such as a tensegrity interplanetary probe prototype surviving multi-story drops. Due to the complexity of tensegrity structures, modeling through physics simulation and machine learning improves our ability to design and evaluate new structures and their controllers in a dynamic environment. The kinematics of our simulator, the open source NASA Tensegrity Robotics Toolkit, have been previously validated within 1.3% error on position through motion capture of the six strut robot ReCTeR. This paper provides additional validation of the dynamics through the direct comparison of the simulator to forces experienced by the latest version of the Tetraspine robot. These results give us confidence in our strategy of using tensegrity to impart future robotic systems with properties similar to biological systems such as increased flexibility, power, and mobility in extreme terrains.

This paper proposes a multi-objective evolutionary algorithm-based optimal posture controller to generate an optimal trajectory of humanoid robots against external disturbance using an iterative linear quadratic regulator (ILQR) and concurrently optimize multiple performance criteria. As the dimensionality of nonlinear system increases, it is difficult to find the weighting matrices of cost function in ILQR. In the proposed method, this problem is solved by employing a multi-objective quantum-inspired evolutionary algorithm (MQEA) to obtain nondominated solutions of the weighting matrices generating various optimal trajectories that satisfy multiple performance criteria. Among numerous nondominated solutions generated from MQEA, fuzzy measure and fuzzy integral are employed for global evaluation by integrating the partial evaluation of each of them over criteria with respect to user's degree of consideration for each criterion. The effectiveness of the proposed method is verified by computer simulations for the problem of balancing the posture of a humanoid robot against external impulse force, where the robot is modeled by a four-link inverted pendulum.

This paper proposes an optimal control method to generate a minimum-torque change trajectory of humanoid arm by using a differential dynamic programming (DDP). Since DDP is a locally optimal feedback controller, the convergence is not guaranteed unless DDP starts with a good reference trajectory for high-dimensional nonlinear dynamical systems. The reference trajectory is generated by using the minimum-jerk trajectory method, and then the corresponding torque profile is obtained by using the computed-torque method. This reference trajectory is not optimal because it is solely based on the kinematics of the system. In the proposed method, the rate of torque change is used as control input in DDP to generate the optimal trajectory, which concurrently minimizes the torque variations and considers the dynamics of the system. The effectiveness of the proposed method is verified by computer simulations for generating the optimal trajectory of a 7 degrees-of-freedom (DOF) MyBot humanoid arm in Webots simulator.

This paper proposes a method to generate an optimal trajectory of nonlinear dynamical system and concurrently optimize multiple performance criteria. As the dimensionality of system increases, it is difficult to find values of cost/reward function of conventional optimal controllers. In order to solve this problem, the proposed method employs iterative linear quadratic regulator and multi-objective quantum-inspired evolutionary algorithm to generate various optimal trajectories that satisfy multiple performance criteria. Fuzzy measure and fuzzy integral are also employed for global evaluation by integrating the partial evaluation of each solution over criteria with respect to user's degree of consideration for each criterion. Effectiveness of the proposed method is verified by computer simulation carried out for the problem of stabilizing two-link inverted pendulum model.

Accelerated Q-learning algorithm is proposed for environment having both goal and fail states. It extends Q-learning, a well-known scheme in reinforcement learning. Unlike this conventional Q-learning, the proposed algorithm keeps track of the past failure experiences as a separate fail state-action value, QF. Agent uses this value along with a goal state-action value, QN, which is calculated and updated using conventional Q-learning, to modify the exploratory behavior during learning phase. Effectiveness of the proposed accelerated Q-learning algorithm is verified in a grid world environment. The proposed algorithm significantly reduces a convergence speed to find out the optimal path from start state to goal state while maximizing its receiving rewards.

This paper proposes a novel two-layered confabulation architecture for an artificial creature to select a proper behavior considering the internally generated will and the context of the external environment consecutively. The architecture is composed of seven main modules for processing perception, internal state, context, memory, learning, behavior selection, and actuation. The two-layered confabulation in a behavior module is processed by a will-based confabulation and a context-based confabulation consecutively by referring to confabulation probabilities in a memory module. An arbiter in the behavior module chooses a proper behavior among the suggested ones from the two confabulations, which is to be put into an action. To demonstrate the effectiveness of the proposed architecture, experiments are carried out for an artificial creature, implemented in the 3-D virtual environment, which behaves as per its will considering the context in the environment.

Confabulations, where millions of items of relevant knowledge are applied in parallel in the human brain, are typically employed in thinking. This paper proposes a novel behavior selection architecture and memory-based learning method for an artificial creature based on two-layered confabulation. A behavior is selected by considering both internally generated will and the context of the external environment. Proposed behavior selection using a confabulation scheme is a parallel process in which a number of behaviors are considered simultaneously. An arbitration mechanism is employed to choose a proper behavior which is to be put into an action. Also memory-based behavior learning is proposed, w here the memory has the selection probabilities of behaviors based on will and context. The learning module updates the contents of memory according to the user-given reward or penalty signal. To demonstrate the effectiveness of the proposed scheme, an artificial creature is implemented in the 3D virtual environment such that it can interact with a human being considering its will and context.

This paper presents the development of generating dance performances of humanoid robot, HSR (HanSaRam)-VII, synchronized with Robonova, for entertainment. This heterogeneous team, RoboBees, participated in 'Robots at Play Award 2006' and nominated as one of top six teams in the world. A method of generating and combining both periodic motion (on-line pattern generator) and aperiodic motion (off-line pattern generator) is investigated along with the control architecture for HSR-VII. Detailed system descriptions of two types of humanoid robots, HSR-VII from KAIST and Robonova from miniROBOT Corp., are included with the experimental results. In addition, the time domain passivity compliance control system is introduced to guarantee the stable periodic motion by changing the initial planned trajectories. Video clip of RoboBees' performance is available at http://rit.kaist.ac.kr.

This paper proposes a genetic representation method to evolve artificial creature’s personality by using artificial genome and evolutionary generative algorithm. The genome consists of computer-coded chromosomes. Based on the internal architecture, the chromosomes are designed for the genetic representation. They are composed of the fundamental genes, internal state related genes, and behavior related genes as essential components, which represent the personality and are used for the animal-like evolution in the simulated environment. To get a desired personality (genome), a proper fitness function is designed and genetic operators are applied to the population (genomes). The artificial creature, Rity, is developed in a virtual world of PC to test the effectiveness of this representation scheme.