Towards adaptive multi-robot systems: self-organization and self-adaptation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible

Learning to Sit: Synthesizing Human-Chair Interactions via Hierarchical Control

Recent progress on physics-based character animation has shown impressive breakthroughs on human motion synthesis, through imitating motion capture data via deep reinforcement learning. However, results have mostly been demonstrated on imitating a single distinct motion pattern, and do not generalize to interactive tasks that require flexible motion patterns due to varying human-object spatial configurations. To bridge this gap, we focus on one class of interactive tasks -- sitting onto a chair. We propose a hierarchical reinforcement learning framework which relies on a collection of subtask controllers trained to imitate simple, reusable mocap motions, and a meta controller trained to execute the subtasks properly to complete the main task. We experimentally demonstrate the strength of our approach over different non-hierarchical and hierarchical baselines. We also show that our approach can be applied to motion prediction given an image input. A supplementary video can be found at https://youtu.be/3CeN0OGz2cA.Comment: Accepted to AAAI 202

Development of a Controlled Dynamics Simulator for Reusable Launcher Descent and Precise Landing

This paper introduces a Reusable Launch Vehicle (RLV) descent dynamics simulator coupled with closed-loop guidance and control (G&C) integration. The studied vehicle's first-stage booster, evolving in the terrestrial atmosphere, is steered by a Thrust Vector Control (TVC) system and planar fins through gain-scheduled Proportional-Integral-Derivative controllers, correcting the trajectory deviations until precise landing from the reference profile computed in real time by a successive convex optimisation algorithm. Environmental and aerodynamic models that reproduce realistic atmospheric conditions are integrated into the simulator for enhanced assessment. Comparative performance results were achieved in terms of control configuration (TVC-only, fins-only, and both) for nominal conditions as well as with external disturbances such as wind gusts or multiple uncertainties through a Monte Carlo analysis to assess the G&C system. These studies demonstrated that the configuration combining TVC and steerable planar fins has sufficient control authority to provide stable flight and adequate uncertainties and disturbance rejection. The developed simulator provides a preliminary assessment of G&C techniques for the RLV descent and landing phase, along with examining the interactions that occur. In particular, it paves the way towards the development and assessment of more advanced and robust algorithms

The application of optimal control technqiues to advanced manned missions, volume 1

Two problems are presented in the area of optimal control and its application to the design of attitude control systems for advanced complex aerospace vehicles. The problems discussed are specification of performance criteria in terms of structural load minimization and/or maximum orbital payload injection requirements of the controlled vehicle; and formulation and solution of the optimization problem such that practical control systems are obtained