Massively multi-robot simulation in stage

Stage is a C++ software library that simulates multiple mobile robots. Stage version 2, as the simulation backend for the Player/Stage system, may be the most commonly used robot simulator in research and university teaching today. Development of Stage version 3 has focused on improving scalability, usability, and portability. This paper examines Stage’s scalability. We propose a simple benchmark for multi-robot simulator performance, and present results for Stage. Run time is shown to scale approximately linearly with population size up to 100,000 robots. For example, Stage simulates 1 simple robot at around 1,000 times faster than real time, and 1,000 simple robots at around real time. These results suggest that Stage may be useful for swarm robotics researchers who would otherwise use custom simulators, with their attendant disadvantages in terms of code reuse and transparency.     

Study on Decentralization of Spherical Amphibious Multi-robot Control System Based on Smart Contract and Blockchain

With the development of intelligent bionic robots and the improvement of military application,a single robot cannot meet the requirements of the tasks of the current era.The more complex tasks require not only that the robot be able to pass through the field barriers and the amphibious environment,but also that the robot be able to collaborate in a multi-robot system.Consequently,research on the multi-robot control system of spherical amphibious robots is very important.Pres-ently,the main research on amphibious robots is to improve the functions of a single robot,in the absence of the study of the multi-robot control system.Existing systems primarily use a centralized control methodology.Although the transfer of central node can be achieved,there is still a problem of Byzantine fault tolerance in military applications,that is,when the amphibious multi-robot system is invaded by the enemy.The central node may not only fail to accomplish the task,but also lose control of other robots,with severe consequences.To solve the above problems,this paper proposed a decentralized method of spherical amphibious multi-robot control system based on blockchain technology.First,the point-to-point informa-tion network based on long range radio technology of low power wide area network was set up,we designed the blockchain system for embedded application environment and the decentralized hardware and software architecture of multi-robot control system.On this basis,the consensus plugin,smart contract and decentralized multi-robot control algorithm were designed to achieve decentralization.The experimental results of consensus of spherical amphibious multi-robot showed the effectiveness of the decentralization.     

A fuzzy-based reliability system for knowledge sharing between robots in P2P JXTA-overlay platform

The design of an efficient collaborative multi-robot framework that ensures the autonomy and the individual requirements of the involved robots is a very challenging task. This requires designing an efficient platform for inter-robot communication. P2P is a good approach to achieve this goal. P2P aims at making the communication ubiquitous thereby crossing the communication boundary and has many attractive features to use it as a platform for collaborative multi-robot environments. In this paper, we present our implemented P2P system based on JXTA Overlay. We use JXTA Overlay as a platform for robot collaboration and knowledge sharing. We also propose a fuzzy-based peer reliability system for JXTA-Overlay platform considering three parameters: Actual Behavior Criterion (ABC), Mutually Agreed Behavior (MAB) and Reputation (R). We evaluated the knowledge sharing system by many experiments and show that this system has a good performance and can be used successfully for knowledge sharing between robots. Also, we present some simulation results, which show the fuzzy-based peer reliability system has a good behavior and can successfully select the best peer candidate.     

Cooperative object transport with a swarm of e-puck robots: robustness and scalability of evolved collective strategies

Cooperative object transport in distributed multi-robot systems requires the coordination and synchronisation of pushing/pulling forces by a group of autonomous robots in order to transport items that cannot be transported by a single agent. The results of this study show that fairly robust and scalable collective transport strategies can be generated by robots equipped with a relatively simple sensory apparatus (i.e. no force sensors and no devices for direct communication). In the experiments described in this paper, homogeneous groups of physical e-puck robots are required to coordinate and synchronise their actions in order to transport a heavy rectangular cuboid object as far as possible from its starting position to an arbitrary direction. The robots are controlled by dynamic neural networks synthesised using evolutionary computation techniques. The best evolved controller demonstrates an effective group transport strategy that is robust to variability in the physical characteristics of the object (i.e. object mass and size of the longest object’s side) and scalable to different group sizes. To run these experiments, we designed, built, and mounted on the robots a new sensor that returns the agents’ displacement on a 2D plane. The study shows that the feedback generated by the robots’ sensors relative to the object’s movement is sufficient to allow the robots to coordinate their efforts and to sustain the transports for an extended period of time. By extensively analysing successful behavioural strategies, we illustrate the nature of the operational mechanisms underpinning the coordination and synchronisation of actions during group transport.     

Distributed scalable multi-robot learning using particle swarm optimization

Designing effective behavioral controllers for mobile robots can be difficult and tedious; this process can be circumvented by using online learning techniques which allow robots to generate their own controllers online in an automated fashion. In multi-robot systems, robots operating in parallel can potentially learn at a much faster rate by sharing information amongst themselves. In this work, we use an adapted version of the Particle Swarm Optimization algorithm in order to accomplish distributed online robotic learning in groups of robots with access to only local information. The effectiveness of the learning technique on a benchmark task (generating high-performance obstacle avoidance behavior) is evaluated for robot groups of various sizes, with the maximum group size allowing each robot to individually contain and manage a single PSO particle. To increase the realism of the technique, different PSO neighborhoods based on limitations of real robotic communication are tested and compared in this scenario. We explore the effect of varying communication power for one of these communication-based PSO neighborhoods. To validate the effectiveness of these learning techniques, fully distributed online learning experiments are run using a group of 10 real robots, generating results which support the findings from our simulations.     

Effect of Flexible Spine Motion on Energy Efficiency in Quadruped Running

Energy efficiency is important in the performance of quadruped robots and mammals.Flexible spine motion generally exists in quadruped mammals.This paper mainly explores the effect of flexible spinal motion on energy efficiency.Firstly,a planar simplified model of the quadruped robot with flexible spine motion is introduced and two simulation experiments are carried out.The results of simulation experiments demonstrate that both spine motion and spinal flexibility can indeed increase energy efficiency,and the curve of energy efficiency change along with spinal stiffness is acquired.So,in order to obtain higher energy efficiency,quadruped robots should have flexible spine motion.In a certain speed,there is an optimal spinal stiffness which can make energy efficiency to be the best.Secondly,a planar quadruped robot with flexible spine motion is designed and the conclusions drawn in the two simulation experiments are verified.Lastly,the third simulation experiment is carried out to explore the relationship between the optimal spinal stiffness,speed and total mass.The optimal spinal stiffness increases with both speed and total mass,which has important guiding significance for adjusting the spinal stiffness of quadruped robots to make them reach the best energy efficiency.     

A cellular mechanism for multi-robot construction via evolutionary multi-objective optimization of a gene regulatory network

A major research challenge of multi-robot systems is to predict the emerging behaviors from the local interactions of the individual agents. Biological systems can generate robust and complex behaviors through relatively simple local interactions in a world characterized by rapid changes, high uncertainty, infinite richness, and limited availability of information. Gene Regulatory Networks (GRNs) play a central role in understanding natural evolution and development of biological organisms from cells. In this paper, inspired by biological organisms, we propose a distributed GRN-based algorithm for a multi-robot construction task. Through this algorithm, multiple robots can self-organize autonomously into different predefined shapes, and self-reorganize adaptively under dynamic environments. This developmental process is evolved using a multi-objective optimization algorithm to achieve a shorter travel distance and less convergence time. Furthermore, a theoretical proof of the system's convergence is also provided. Various case studies have been conducted in the simulation, and the results show the efficiency and convergence of the proposed method.     

Stigmergic algorithms for multiple minimalistic robots on an RFID floor

Stigmergy is a powerful principle in nature, which has been shown to have interesting applications to robotic systems. By leveraging the ability to store information in the environment, robots with minimal sensing, memory, and computational capabilities can solve complex problems like global path planning. In this paper, we discuss the use of stigmergy in minimalist multi-robot systems, in which robots do not need to use any internal model, long-range sensing, or position awareness. We illustrate our discussion with three case studies: building a globally optimal navigation map, building a gradient map of a sensed feature, and updating the above maps dynamically. All case studies have been implemented in a real environment with multiple ePuck robots, using a floor with 1,500 embedded radio frequency identification tags as the stigmergic medium. Results collected from tens of hours of real experiments and thousands of simulated runs demonstrate the effectiveness of our approach.     

A System Design Concept Based on Omni-Directional Mobility,Safety and Modularity for an Autonomous Mobile Soccer Robot

In this paper, we describe the concept, design and implementation of a series of autonomous mobile soccer robots, named Musashi robots, which are designed referring ISO safety standards and have mechatronics modular architecture. The robots are designed to participate in the RoboCup Middle Size League. Using a modular design philosophy, we show that the selection of a proper moving mechanism, a suitable vision system and a mechatronics modular architecture design can lead to the realization of a reliable, simple, and low cost robot when compared with most car-like robots that include many kinds of sensors and have a complex design structure.     

Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots

Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm. We conclude with a proof-of-concept experiment in which the swarm performs a complete environmental monitoring task by combining multiple evolved controllers.     

Energy-efficient indoor search by swarms of simulated flying robots without global information

Swarms of flying robots are a promising alternative to ground-based robots for search in indoor environments with advantages such as increased speed and the ability to fly above obstacles. However, there are numerous problems that must be surmounted including limitations in available sensory and on-board processing capabilities, and low flight endurance. This paper introduces a novel strategy to coordinate a swarm of flying robots for indoor exploration that significantly increases energy efficiency. The presented algorithm is fully distributed and scalable. It relies solely on local sensing and low-bandwidth communication, and does not require absolute positioning, localisation, or explicit world-models. It assumes that flying robots can temporarily attach to the ceiling, or land on the ground for efficient surveillance over extended periods of time. To further reduce energy consumption, the swarm is incrementally deployed by launching one robot at a time. Extensive simulation experiments demonstrate that increasing the time between consecutive robot launches significantly lowers energy consumption by reducing total swarm flight time, while also decreasing collision probability. As a trade-off, however, the search time increases with increased inter-launch periods. These effects are stronger in more complex environments. The proposed localisation-free strategy provides an energy efficient search behaviour adaptable to different environments or timing constraints.     

Large Neural Network Simulations on Multiple Hardware Platforms

To efficiently simulate very large networks of interconnected neurons, particular consideration has to be given to the computer architecture being used. This article presents techniques for implementing simulators for large neural networks on a number of different computer architectures. The neuronal simulation task and the computer architectures of interest are first characterized, and the potential bottlenecks are highlighted. Then we describe the experience gained from adapting an existing simulator, SWIM, to two very different architectures–vector computers and multiprocessor workstations. This work lead to the implementation of a new simulation library, SPLIT, designed to allow efficient simulation of large networks on several architectures. Different computer architectures put different demands on the organization of both data structures and computations. Strict separation of such architecture considerations from the neuronal models and other simulation aspects makes it possible to construct both portable and extendible code.     

Distinct rhythmic locomotor patterns can be generated by a simple adaptive neural circuit: Biology,simulation, and VLSI implementation

Rhythmic motor patterns can be induced in leg motor neurons of isolated locust thoracic ganglia by bath application of pilocarpine. We observed that the relative phases of levators and depressors differed in the three thoracic ganglia. Assuming that the central pattern generating circuits underlying these three segmental rhythms are probably very similar, we developed a simple model circuit that can produce any one of the three activity patterns and characteristic phase relationships by modifying a single synaptic weight. We show results of a computer simulation of this circuit using the neuronal simulator NeuraLOG/Spike. We built and tested an analog VLSI circuit implementation of this model circuit that exhibits the same range of behaviors as the computer simulation. This multidisciplinary strategy will be useful to explore the dynamics of central pattern generating networks coupled to physical actuators, and ultimately should allow the design of biologically realistic walking robots.     

Designing pheromone communication in swarm robotics: Group foraging behavior mediated by chemical substance

In swarm robotics, communication among the robots is essential. Inspired by biological swarms using pheromones, we propose the use of chemical compounds to realize group foraging behavior in robot swarms. We designed a fully autonomous robot, and then created a swarm using ethanol as the trail pheromone allowing the robots to communicate with one another indirectly via pheromone trails. Our group recruitment and cooperative transport algorithms provide the robots with the required swarm behavior. We conducted both simulations and experiments with real robot swarms, and analyzed the data statistically to investigate any changes caused by pheromone communication in the performance of the swarm in solving foraging recruitment and cooperative transport tasks. The results show that the robots can communicate using pheromone trails, and that the improvement due to pheromone communication may be non-linear, depending on the size of the robot swarm.     

A human motor control perspective to multiple manipulator modelling

This paper describes the aspects involved in modelling a multi-robot system from a human motor control perspective. The human motor control system has a hierarchical and decentralised structure, and building a control system for a multi-robot system that attains human features would require a decomposable model. Decomposition of a complex robotic system is difficult due to the interactions between the subsystems, so these have to be first separated before the system is modelled. The proposed method of separating the interconnections is applied with the aid of fuzzy modelling to derive a fully decomposable model of two manipulator robots handling a common object.     

Radiofrequency ablation of hepatic tumors: simulation, planning, and contribution of virtual reality and haptics

For radiofrequency ablation (RFA) of liver tumors, evaluation of vascular architecture, post-RFA necrosis prediction, and the choice of a suitable needle placement strategy using conventional radiological techniques remain difficult. In an attempt to enhance the safety of RFA, a 3D simulator, treatment planning, and training tool, that simulates the insertion of the needle, the necrosis of the treated area, and proposes an optimal needle placement, has been developed. The 3D scenes are automatically reconstructed from enhanced spiral CT scans. The simulator takes into account the cooling effect of local vessels greater than 3 mm in diameter, making necrosis shapes more realistic. Optimal needle positioning can be automatically generated by the software to produce complete destruction of the tumor, with maximum respect of the healthy liver and of all major structures to avoid. We also studied how the use of virtual reality and haptic devices are valuable to make simulation and training realistic and effective.     

Computational wear prediction of artificial knee joints based on a new wear law and formulation

Laboratory joint wear simulator testing has become the standard means for preclinical evaluation of wear resistance of artificial knee joints. Recent simulator designs have been advanced and become successful at reproducing the wear patterns observed in clinical retrievals. However, a single simulator test can be very expensive and take a long time to run. On the other hand computational wear modelling is an alternative attractive solution to these limitations. Computational models have been used extensively for wear prediction and optimisation of artificial knee designs. However, all these models have adopted the classical Archard's wear law, which was developed for metallic materials, and have selected wear factors arbitrarily. It is known that such an approach is not generally true for polymeric bearing materials and is difficult to implement due to the high dependence of the wear factor on the contact pressure. Therefore, these studies are generally not independent and lack general predictability. The objective of the present study was to develop a new computational wear model for the knee implants, based on the contact area and an independent experimentally determined non-dimensional wear coefficient. The effects of cross-shear and creep on wear predictions were also considered. The predicted wear volume was compared with the laboratory simulation measurements. The model was run under two different kinematic inputs and two different insert designs with curved and custom designed flat bearing surfaces. The new wear model was shown to be capable of predicting the difference of the wear volume and wear pattern between the two kinematic inputs and the two tibial insert designs. Conversely, the wear factor based approach did not predict such differences. The good agreement found between the computational and experimental results, on both the wear scar areas and volumetric wear rates, suggests that the computational wear modelling based on the new wear law and the experimentally calculated non-dimensional wear coefficient should be more reliable and therefore provide a more robust virtual modelling platform.