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Mixed-Reality Based multi-agent robotics Framework for Artificial Swarm Intelligence Experiments

IEEE ACCESS 2023年 11卷 105718-105733页

作者： Karunarathna, Dilshani Jaliyagoda, Nuwan Jayalath, Ganindu Alawatugoda, Janaka Ragel, Roshan Nawinne, Isuru Univ Peradeniya Fac Engn Dept Comp Engn Peradeniya 20400 Sri Lanka Rabdan Acad Fac Resilience Res & Innovat Ctr Div Abu Dhabi U Arab Emirates Griffith Univ Inst Integrated & Intelligent Syst Nathan Qld 4111 Australia

The term "Swarm intelligence" outlines a broad scope, which is generally defined as the collective behaviour of many individuals towards a certain task, each operating autonomously in a decentralised manner. Swarms are inspired by the type of biological behaviours of animals and this technology involves decentralised local control and communication, which ensures the problem-solving process is more efficient through modification and infusion into swarm robotic technology. Swarm robotics related experiments and applications are relatively expensive compared to other types of robotics experiments. The most common solution is to use computer based virtual simulations and accordingly, this study introduces a reliable and cost-effective hybrid solution using Mixed Reality (MR) technologies to perform swarm experiments on real robots and virtually deployed robots simultaneously. MR bridges the gap between the virtual and physical realms through Augmented Reality (AR) and Augmented Virtuality (AV) and addressing the weaknesses of simulators and providing a more accurate representation of real-world performance. This approach provides a platform for researchers to explore swarm robotics with enhanced realism and interactivity and conduct experiments that mimic real-world conditions. To validate this method we conducted experiments with both physical and virtual robots in near real-time in a mixed reality environment. In addition, an open-source, distributed, and modular mixed reality simulation framework was implemented with support libraries and applications. Our work not only demonstrates the potential of MR in swarm robotics but also emphasizes its value in advancing the understanding and application of swarm intelligence principles in practical contexts.

关键词： Mixed-reality multi-agent robotics swarm intelligence artificial intelligence virtual reality

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Valentino Braitenberg's Table: Downhill Innovation of Vehicles via Darwinian Evolution 13th

Valentino Braitenberg's Table: Downhill Innovation of Vehicl...

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13th International Conference on Computational Collective Intelligence (ICCCI)

作者： Shaghaghi, Sahand Hamid, Owais Nehaniv, Chrystopher L. Univ Waterloo Syst Design Engn Waterloo ON Canada

ISBN: (纸本)9783030881139;9783030881122

Evolution has been a topic of interest that has been explored extensively throughout recent history. Ever since the proposal of the evolutionary theory by Darwin, there have been attempts made to validate, extend, explore and exploit the different aspects of this theory. This study sets forth methods to explore elements of what has been proposed by Darwinian evolution using Braitenberg vehicles, simple machines with sensory-motor couplings in an environmental context. In this study, a simulation environment is set up based upon the descriptions of neurophysiologist Valentino Braitenberg. This simulation environment is then utilized to carry out the thought experiments envisioned by Braitenberg for the simplest nontrivial kinds of vehicles. The methodology and results for the carried out experiments are detailed in this paper. Apart from an understanding of whether the environmental setup affects evolution, specifically the number of light sources (stimuli) and location of these light sources, the experiments show an interesting trend regarding dynamic equilibrium of the evolutionary process, the ramifications of which might not have been understood well enough previously. It is concluded that ecological setup, as well as the initial genetic makeup of the vehicles, play a crucial role in the evolution of vehicles in scenarios laid out in this study. Further, the placement of stimuli (location of lights) and the number of the stimuli have a visible effect on the survivability of vehicle types (species).

关键词： Braitenberg vehicles multi-agent robotics Evolutionary robotics

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Distributed Drone Traffic Coordination Using Triggered Communication

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UNMANNED SYSTEMS 2020年第1期8卷 1-20页

作者： Zaini, Abdul Hanif Xie, Lihua Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

This paper proposes a low complexity distributed multi-agent coordination algorithm for agents to reach their target positions in dense traffic under limited communication. Each single-integrator agent is limited to communicating with only one other agent at a time in consideration of limited bandwidth. We adapt the Velocity Obstacle collision avoidance method from literature to the limited communication problem by incorporating Voronoi Cells and repulsion in our hybrid algorithm. We also introduce a priority system for distributed coordination to avoid deadlocks and livelocks by having agent pairs make mutual decisions based on each agent's conditional priority. An event trigger-based communication protocol is designed to determine when and to whom to communicate. Our method's effectiveness is demonstrated in simulations including 100 randomized scenarios of 50 agents. The simulations show that our proposed algorithm enables agents to reach their assigned target positions without deadlock and collision while requiring an average communication rate that is significantly lower than the control frequency.

关键词： multi-agent robotics collision avoidance distributed control event-triggered

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Engineering Social Learning Mechanisms for Minimalistic multi-agent Robots 3

Engineering Social Learning Mechanisms for Minimalistic Mult...

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3rd International Conference on Control and Robots (ICCR)

作者： Hamid, Owais Dautenhahn, Kerstin Nehaniv, Chrystopher L. Univ Waterloo Syst Design Engn Waterloo ON Canada Univ Waterloo Elect & Comp Engn Waterloo ON Canada

ISBN: (纸本)9781728175621

Social learning, which can include complex or simple social mechanisms, allow us to understand cooperation and communication in animals, giving them better chances to survive for longer and thrive as a society. In order to translate this understanding into socially rich behavior among multi-agent robots, this study utilizes social learning mechanisms that are simple yet quite effective. Small and simple swarm robots are utilized to understand how such social mechanisms might play a role in establishing rules for emergent group behavior and how social rules might be engineered to gain useful effects in a group of robots. The study investigates exploratory behavior without interaction (asocial) and with interaction (social) among a group of robots. The results from this exploratory study suggest that deterministic asocial exploration is best performed by Spiral exploration mechanisms. However, these asocial exploration strategies are eclipsed by certain types of social reward sharing strategies as long as sharing occurs for at least half the lifetime of the robots. Sharing locations of reward caches for all time is of course the most optimal, but comes at the cost of communicating longer and hence using more energy both on the sender and receiver's end. An analysis of a compromise strategy between completely asocial exploration and social reward location sharing is performed using strategies termed critical and conditional learning. It is found that the number of reward caches located through critical and conditional learning are intermediary to the two extremes, namely completely asocial and completely social foraging.

关键词： multi-agent robotics Social Learning Social robotics Local Enhancement

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Distributed UAV Swarm Formation Control via Object-Focused, multi-Objective SARSA

Distributed UAV Swarm Formation Control via Object-Focused, ...

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American Control Conference

作者： Speck, Christian Bucci, Donald J. Lockheed Martin Adv Technol Labs Spectrum Syst Lab Cherry Hill NJ 08002 USA

ISBN: (纸本)9781538654286

We present a novel multi-objective reinforcement learning formulation of the decentralized formation control problem for swarms of fixed-wing UAVs based on a relative state-space construction of obstacles and waypoints. Specifically, a modular state-action-reward-state-action (SARSA) approach is applied to a set of behavioral swarm rules (i.e., Reynolds flocking, target seek, and obstacle avoidance). Q-tables are trained offline for each rule using a simulated model of the UAV plant over scenarios with randomized waypoints and obstacle locations. The greatest mass approach is applied to action selection for each UAV by selecting the relative heading that maximizes the weighted sum of Q-values across all Q-tables. To restrain the dimensionality of the state space, object-focused learning is applied during policy evaluation to instantiate multiple Q-tables for each detected neighbor UAV and obstacle. In comparison to existing behavioral swarm controllers, the controller learned via object-focused greatest mass SARSA (OF-GM-SARSA) is generalizable across multiple scenarios in terms of demonstrating desired swarm behaviors while providing a negligible number of collisions.

关键词： multi-agent robotics distributed formation control modular SARSA reinforcement learning Reynolds flocking

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Novelty and Objective-based Neuroevolution of a Physical Robot Swarm 9

Novelty and Objective-based Neuroevolution of a Physical Rob...

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9th International Conference on agents and Artificial Intelligence (ICAART)

作者： Stonedahl, Forrest Stonedahl, Susa H. Cheboi, Nelly Tazyeen, Danya Devore, David Augustana Coll Math & Comp Sci Dept Rock Isl IL 61201 USA St Ambrose Univ Engn & Phys Sci Dept Davenport IA USA

ISBN: (纸本)9789897582202

This paper compares the use of novelty search and objective-based evolution to discover motion controllers for an exploration task wherein mobile robots search for immobile targets inside a bounded polygonal region and stop to mark target locations. We evolved the robots' neural-network controllers in a custom 2-D simulator, selected the best performing neurocontrollers from both novelty search and objective-based search, and compared performance relative to an unevolved (baseline) controller and a simple human-designed controller. The controllers were also transferred onto physical robots, and the real-world tests provided good empirical agreement with simulation results, showing that both novelty search and objective-based search produced controllers that were comparable or superior to the human-designed controller, and that objective-based search slightly outperformed novelty search. The best controllers had surprisingly low genotypic complexity, suggesting that this task may lack the type of deceptive fitness landscape that has previously favored novelty search over objective-based search.

关键词： Novelty Search Neuroevolution multi-agent robotics Exploration

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Haptic Interactions With multi-Robot Swarms Using Manipulability

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JOURNAL OF HUMAN-ROBOT INTERACTION 2015年第1期4卷 60-74页

作者： Setter, Tina Fouraker, Alex Egerstedt, Magnus Kawashima, Hiroaki Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Kyoto Univ Grad Sch Informat Kyoto Japan

This paper investigates how haptic interactions can be defined for enabling a single operator to control and interact with a team of mobile robots. Since there is no unique or canonical mapping from the swarm configuration to the forces experienced by the operator, a suitable mapping must be developed. To this end, multi-agent manipulability is proposed as a potentially useful mapping, whereby the forces experienced by the operator relate to how inputs, injected at precise locations in the team, translate to swarm-level motions. Small forces correspond to directions in which it is easy to move the swarm, while larger forces correspond to more costly directions. Initial experimental results support the viability of the proposed, haptic, human-swarm interaction mapping, through a user study where operators are tasked with driving a collection of robots through a series of way points.

关键词： Human-robot interaction multi-agent robotics haptic force feedback

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Unifying microscopic flocking motion models for virtual, robotic, and biological flock members

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AUTONOMOUS ROBOTS 2013年第2-3期35卷 195-219页

作者： Fine, Benjamin T. Shell, Dylan A. Texas A&M Univ Dept Comp Sci & Engn College Stn TX 77843 USA

Flocking motions have been the subject of hundreds of studies over the past six decades. The vast majority of models have nearly identical aims: bottom-up demonstration of basic emergent flocking motions. Despite a significant fraction of the literature providing algorithmic descriptions of models, incompleteness and imprecision are also readily identifiable in flocking algorithms, algorithmic input, and validation of the models. To address this issue, this meta-study introduces a data-flow template, which unifies many of the existing approaches. Additionally, there are small differences and ambiguities in the flocking scenarios being studied by different researchers;unfortunately, these differences are of considerable significance. For example, much subtlety is needed to specify sensory requirements exactly and minor modifications may critically alter a flock's exhibited motions. We introduce two taxonomies that minimize both incompleteness and imprecision, and enable us to highlight those publications that study flocking motions under comparable assumptions. Furthermore, we aggregate and translate the publications into a consolidated notation. The common notation along with the data-flow template and the two taxonomies constitute a collection of tools, that together, facilitates complete and precise flocking motion models, and enables much of the work to be unified. To conclude, we make recommendations for more diverse research directions and propose criteria for rigorous problem definitions and descriptions of future flocking motion models.

关键词： Flocking multi-agent systems multi-agent robotics Biology-inspired swarms

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