Locomotion Control of Legged Robots using Data-Driven Techniques: Application to a Buoyancy Assisted Biped

The field of legged robotics has made significant advancements and shown potential practicality in various applications. Although these robots are becoming more popular, they are still not widely used due to the inherent danger when malfunctioning as well as their high cost. BALLU, Buoyancy Assisted Lightweight Legged Unit, is a robot that never falls down due to the buoyancy provided by a set of helium balloons attached to its lightweight body. This platform solves many issues that hinder current robots from operating close to humans while also providing affordability. However, the advantages gained also lead to the platform's distinct difficulties caused by severe underactuation and nonlinearities due to external forces such as buoyancy and drag. This dissertation presents a motion planning approach using data-driven techniques motivated by these challenges and its application to BALLU. The paper describes the concept of the platform, the hardware design of different generations of BALLUs, the software architecture, the nonconventional characteristics of BALLU as a legged robot, and an analysis of its unique behavior. Based on the analysis, a data-driven approach is proposed to achieve non-teleoperated walking: a statistical process is proposed to form low-dimensional state vectors from the simulation data, and a deep neural network-based controller is trained. The controller is tested on both simulation and real-world hardware. Its performance is assessed by observing the robot's limit cycles and trajectories in Cartesian coordinates. The controller generates periodic walking sequences in simulation as well as on the real-world robot, even without additional transfer learning. It is also shown that the controller can deal with unseen conditions during the training phase. The resulting behavior not only shows the robustness of the controller but also implies that the proposed statistical process effectively extracts a state vector that is low-dimensional yet contains the essential information of the high-dimensional dynamics of BALLU's walking

BALLU2: A Safe and Affordable Buoyancy Assisted Biped.

This work presents the first full disclosure of BALLU, Buoyancy Assisted Lightweight Legged Unit, and describes the advantages and challenges of its concept, the hardware design of a new implementation (BALLU2), a motion analysis, and a data-driven walking controller. BALLU is a robot that never falls down due to the buoyancy provided by a set of helium balloons attached to the lightweight body, which solves many issues that hinder current robots from operating close to humans. The advantages gained also lead to the platforms distinct difficulties caused by severe nonlinearities and external forces such as buoyancy and drag. The paper describes the nonconventional characteristics of BALLU as a legged robot and then gives an analysis of its unique behavior. Based on the analysis, a data-driven approach is proposed to achieve non-teleoperated walking: a statistical process using Spearman Correlation Coefficient is proposed to form low-dimensional state vectors from the simulation data, and an artificial neural network-based controller is trained on the same data. The controller is tested both on simulation and on real-world hardware. Its performance is assessed by observing the robots limit cycles and trajectories in the Cartesian coordinate. The controller generates periodic walking sequences in simulation as well as on the real-world robot even without additional transfer learning. It is also shown that the controller can deal with unseen conditions during the training phase. The resulting behavior not only shows the robustness of the controller but also implies that the proposed statistical process effectively extracts a state vector that is low-dimensional yet contains the essential information of the high-dimensional dynamics of BALLUs walking

Modeling and Simulation Studies Analyzing the Pressure-Retarded Osmosis (PRO) and PRO-Hybridized Processes

Pressure-retarded osmosis (PRO) is viewed as a highly promising renewable energy process that generates energy without carbon emissions in the age of the climate change regime. While many experimental studies have contributed to the quest for an efficiency that would make the PRO process commercially viable, computational modeling and simulation studies have played crucial roles in investigating the efficiency of PRO, particularly the concept of hybridizing the PRO process with reverse osmosis (RO). It is crucial for researchers to understand the implications of the simulation and modeling works in order to promote the further development of PRO. To that end, the authors collected many relevant papers and reorganized their important methodologies and results. This review, first of all, presents the mathematical derivation of the fundamental modeling theories regarding PRO including water flux and concentration polarization equations. After that, those theories and thermodynamic theories are then applied to depict the limitations of a stand-alone PRO process and the effectiveness of an RO-PRO hybridized process. Lastly, the review diagnoses the challenges facing PRO-basis processes which are insufficiently resolved by conventional engineering approaches and, in response, presents alternative modeling and simulation approaches as well as novel technologies

An Online Task-Planning Framework Using Mixed Integer Programming for Multiple Cooking Tasks Using a Dual-Arm Robot

This work proposes an online task-scheduling method using mixed-integer programming for a multi-tasking problem regarding a dual-arm cooking robot in a controlled environment. Given each task’s processing time, their location in the working space, dependency, the required number of arms, and the kinematic constraints of the dual-arm robot, the proposed optimization algorithm can produce a feasible solution to scheduling the cooking order for each task and for each associated arms so that the total cooking time and the total moving distance for each arm are minimized. We use a subproblem optimization strategy in which the number of tasks to be planned is divided into several groups instead of planning all tasks at the same time. By doing so, the planning time can be significantly decreased, making the algorithm practical for online implementation. The feasibility of our optimization method and the effectiveness of the subproblem optimization strategy were verified through simulated experiments consisting of 30 to 120 tasks. The results showed that our strategy is advantageous in terms of computation time and makespan for large problems

An Online Task-Planning Framework Using Mixed Integer Programming for Multiple Cooking Tasks Using a Dual-Arm Robot

This work proposes an online task-scheduling method using mixed-integer programming for a multi-tasking problem regarding a dual-arm cooking robot in a controlled environment. Given each task’s processing time, their location in the working space, dependency, the required number of arms, and the kinematic constraints of the dual-arm robot, the proposed optimization algorithm can produce a feasible solution to scheduling the cooking order for each task and for each associated arms so that the total cooking time and the total moving distance for each arm are minimized. We use a subproblem optimization strategy in which the number of tasks to be planned is divided into several groups instead of planning all tasks at the same time. By doing so, the planning time can be significantly decreased, making the algorithm practical for online implementation. The feasibility of our optimization method and the effectiveness of the subproblem optimization strategy were verified through simulated experiments consisting of 30 to 120 tasks. The results showed that our strategy is advantageous in terms of computation time and makespan for large problems

A New era of water treatment technologies: 3D printing for membranes

The commercialization of sustainable 3D printing technology changed the face of manufacturing with its precise and uniform sustainable fabrication. Therefore, like other fields of science, research related to water treatment membranes has adopted this technology successfully, preventing the waste of huge amounts of solvents and thus reducing the high carbon emissions caused by fabrication. Currently, critical research is being conducted in relation to the membrane modules and the fabrication of the membranes themselves. The module studies focus primarily on spacer production and the membrane studies are mostly concerned with the membrane surface. The membrane surface research has successfully adapted inkjet printing for enhanced surface properties for high selectivity and fouling resistance through the printing of nano-materials on the membranes' surfaces. Recently, 3D printing of the polymer membrane support or 3D printing-based interfacial polymerization has also been introduced into water treatment technologies. Since fouling resistance, selectivity and water permeability are the critical factors, many of the parameters can be controlled by the assistance of bespoke and precise 3D printing fabrication. In this study, we examine key aspects of technology which may shed light on future studies regarding 3D printed water treatment membranes and we review the critical developments to date. (C) 2020 The Korean Society of Industrial and Engineering Chemistry. Published by Elsevier B.V. All rights reserved