Modeling multi-robot task allocation with limited information as global game

Continuous response threshold functions to coordinate collaborative tasks in multi-agent systems are commonly employed models in a number of fields including ethology, economics, and swarm robotics. Although empirical evidence exists for the response threshold model in predicting and matching swarm behavior for social insects, there has been no formal argument as to why natural swarms use this approach and why it should be used for engineering artificial ones. In this paper, we show, by formulating task allocation as a global game, that continuous response threshold functions used for communication-free task assignment result in system level Bayesian Nash equilibria. Building up on these results, we show that individual agents not only do not need to communicate with each other, but also do not need to model each other’s behavior, which makes this coordination mechanism accessible to very simple agents, suggesting a reason for their prevalence in nature and motivating their use in an engineering context.     

Balancing exploitation of renewable resources by a robot swarm

Renewable resources like fish stock or forests should be exploited at a rate that supports regeneration and sustainability—a complex problem that requires adaptive approaches to maintain a sufficiently high exploitation while avoiding depletion. In the presence of oblivious agents that cannot keep track of all available resources—a frequent condition in swarm robotics—ensuring that the exploitation effort is correctly balanced is particularly challenging. Additionally, the possibility to exploit resources by multiple robots opens the way to focusing the effort either on a single or on multiple resources in parallel. This means that the swarm needs to collectively decide whether to remain cohesive or split among multiple resources, as a function of the ability of the available resources to replenish after exploitation. In this paper, we propose a decentralised strategy for a swarm of robots that adapts to the available resources and balances the effort among them, hence allowing to maximise the exploitation rate while avoiding to completely deplete the resources. A detailed analysis of the strategy parameters provides insights into the working principles and expected performance of the robot swarm.     

Information flow principles for plasticity in foraging robot swarms

An important characteristic of a robot swarm that must operate in the real world is the ability to cope with changeable environments by exhibiting behavioural plasticity at the collective level. For example, a swarm of foraging robots should be able to repeatedly reorganise in order to exploit resource deposits that appear intermittently in different locations throughout their environment. In this paper, we report on simulation experiments with homogeneous foraging robot teams and show that analysing swarm behaviour in terms of information flow can help us to identify whether a particular behavioural strategy is likely to exhibit useful swarm plasticity in response to dynamic environments. While it is beneficial to maximise the rate at which robots share information when they make collective decisions in a static environment, plastic swarm behaviour in changeable environments requires regulated information transfer in order to achieve a balance between the exploitation of existing information and exploration leading to acquisition of new information. We give examples of how information flow analysis can help designers to decide on robot control strategies with relevance to a number of applications explored in the swarm robotics literature.     

Wind power prediction based on neural network with optimization of adaptive multi-group salp swarm algorithm

Salp swarm algorithm (SSA) is a swarm intelligence algorithm inspired by the swarm behavior of salps in oceans. In this paper, a adaptive multi-group salp swarm algorithm (AMSSA) with three new communication strategies is presented. Adaptive multi-group mechanism is to evenly divide the initial population into several subgroups, and then exchange information among subgroups after each adaptive iteration. Communication strategy is also an important part of adaptive multi-group mechanism. This paper proposes three new communication strategies and focuses on promoting the performance of SSA. These measures significantly improve the cooperative ability of SSA, accelerate convergence speed, and avoid easily falling into local optimum. And the benchmark functions confirm that AMSSA is better than the original SSA in exploration and exploitation. In addition, AMSSA is combined with prediction of wind power based on back propagation (AMSSA-BP) neural network. The simulation results show that the AMSSA-BP neural network prediction model can achieve a better prediction effect of wind power.

     

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.     

Modelling a wireless connected swarm of mobile robots

It is a characteristic of swarm robotics that modelling the overall swarm behaviour in terms of the low-level behaviours of individual robots is very difficult. Yet if swarm robotics is to make the transition from the laboratory to real-world engineering realisation such models would be critical for both overall validation of algorithm correctness and detailed parameter optimisation. We seek models with predictive power: models that allow us to determine the effect of modifying parameters in individual robots on the overall swarm behaviour. This paper presents results from a study to apply the probabilistic modelling approach to a class of wireless connected swarms operating in unbounded environments. The paper proposes a probabilistic finite state machine (PFSM) that describes the network connectivity and overall macroscopic behaviour of the swarm, then develops a novel robot-centric approach to the estimation of the state transition probabilities within the PFSM. Using measured data from simulation the paper then carefully validates the PFSM model step by step, allowing us to assess the accuracy and hence the utility of the model.     

A Hybrid Search Algorithm for Swarm Robots Searching in an Unknown Environment

This paper proposes a novel method to improve the efficiency of a swarm of robots searching in an unknown environment. The approach focuses on the process of feeding and individual coordination characteristics inspired by the foraging behavior in nature. A predatory strategy was used for searching; hence, this hybrid approach integrated a random search technique with a dynamic particle swarm optimization (DPSO) search algorithm. If a search robot could not find any target information, it used a random search algorithm for a global search. If the robot found any target information in a region, the DPSO search algorithm was used for a local search. This particle swarm optimization search algorithm is dynamic as all the parameters in the algorithm are refreshed synchronously through a communication mechanism until the robots find the target position, after which, the robots fall back to a random searching mode. Thus, in this searching strategy, the robots alternated between two searching algorithms until the whole area was covered. During the searching process, the robots used a local communication mechanism to share map information and DPSO parameters to reduce the communication burden and overcome hardware limitations. If the search area is very large, search efficiency may be greatly reduced if only one robot searches an entire region given the limited resources available and time constraints. In this research we divided the entire search area into several subregions, selected a target utility function to determine which subregion should be initially searched and thereby reduced the residence time of the target to improve search efficiency.     

Artificial pheromone for path selection by a foraging swarm of robots

Foraging robots involved in a search and retrieval task may create paths to navigate faster in their environment. In this context, a swarm of robots that has found several resources and created different paths may benefit strongly from path selection. Path selection enhances the foraging behavior by allowing the swarm to focus on the most profitable resource with the possibility for unused robots to stop participating in the path maintenance and to switch to another task. In order to achieve path selection, we implement virtual ants that lay artificial pheromone inside a network of robots. Virtual ants are local messages transmitted by robots; they travel along chains of robots and deposit artificial pheromone on the robots that are literally forming the chain and indicating the path. The concentration of artificial pheromone on the robots allows them to decide whether they are part of a selected path. We parameterize the mechanism with a mathematical model and provide an experimental validation using a swarm of 20 real robots. We show that our mechanism favors the selection of the closest resource is able to select a new path if a selected resource becomes unavailable and selects a newly detected and better resource when possible. As robots use very simple messages and behaviors, the system would be particularly well suited for swarms of microrobots with minimal abilities.     

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.     

Sensory feedback in CNN-based central pattern generators

Central Pattern Generators (CPGs) are a suitable paradigm to solve the problem of locomotion control in walking robots. CPGs are able to generate feed-forward signals to achieve a proper coordination among the robot legs. In literature they are often modelled as networks of coupled nonlinear systems. However the topic of feedback in these systems is rarely addressed. On the other hand feedback is essential for locomotion. In this paper the CPG for a hexapod robot is implemented through Cellular Neural Networks (CNNs). Feedback is included in the CPG controller by exploiting the dynamic properties of the CPG motor-neurons, such as synchronization issue and local bifurcations. These universal paradigms provide the essential issues to include sensory feedback in CPG architectures based on coupled nonlinear systems. Experiments on a dynamic model of a hexapod robot are presented to validate the approach introduced.     

Task partitioning in swarms of robots: an adaptive method for strategy selection

Task partitioning is the decomposition of a task into two or more sub-tasks that can be tackled separately. Task partitioning can be observed in many species of social insects, as it is often an advantageous way of organizing the work of a group of individuals. Potential advantages of task partitioning are, among others: reduction of interference between workers, exploitation of individuals?? skills and specializations, energy efficiency, and higher parallelism. Even though swarms of robots can benefit from task partitioning in the same way as social insects do, only few works in swarm robotics are dedicated to this subject. In this paper, we study the case in which a swarm of robots has to tackle a task that can be partitioned into a sequence of two sub-tasks. We propose a method that allows the individual robots in the swarm to decide whether to partition the given task or not. The method is self-organized, relies on the experience of each individual, and does not require explicit communication between robots. We evaluate the method in simulation experiments, using foraging as testbed. We study cases in which task partitioning is preferable and cases in which it is not. We show that the proposed method leads to good performance of the swarm in both cases, by employing task partitioning only when it is advantageous. We also show that the swarm is able to react to changes in the environmental conditions by adapting the behavior on-line. Scalability experiments show that the proposed method performs well across all the tested group sizes.     

Self-organised path formation in a swarm of robots

In this paper, we study the problem of exploration and navigation in an unknown environment from an evolutionary swarm robotics perspective. In other words, we search for an efficient exploration and navigation strategy for a swarm of robots, which exploits cooperation and self-organisation to cope with the limited abilities of the individual robots. The task faced by the robots consists in the exploration of an unknown environment in order to find a path between two distant target areas. The collective strategy is synthesised through evolutionary robotics techniques, and is based on the emergence of a dynamic structure formed by the robots moving back and forth between the two target areas. Due to this structure, each robot is able to maintain the right heading and to efficiently navigate between the two areas. The evolved behaviour proved to be effective in finding the shortest path, adaptable to new environmental conditions, scalable to larger groups and larger environment size, and robust to individual failures.     

Cooperative navigation in robotic swarms

We study cooperative navigation for robotic swarms in the context of a general event-servicing scenario. In the scenario, one or more events need to be serviced at specific locations by robots with the required skills. We focus on the question of how the swarm can inform its members about events, and guide robots to event locations. We propose a solution based on delay-tolerant wireless communications: by forwarding navigation information between them, robots cooperatively guide each other towards event locations. Such a collaborative approach leverages on the swarm’s intrinsic redundancy, distribution, and mobility. At the same time, the forwarding of navigation messages is the only form of cooperation that is required. This means that the robots are free in terms of their movement and location, and they can be involved in other tasks, unrelated to the navigation of the searching robot. This gives the system a high level of flexibility in terms of application scenarios, and a high degree of robustness with respect to robot failures or unexpected events. We study the algorithm in two different scenarios, both in simulation and on real robots. In the first scenario, a single searching robot needs to find a single target, while all other robots are involved in tasks of their own. In the second scenario, we study collective navigation: all robots of the swarm navigate back and forth between two targets, which is a typical scenario in swarm robotics. We show that in this case, the proposed algorithm gives rise to synergies in robot navigation, and it lets the swarm self-organize into a robust dynamic structure. The emergence of this structure improves navigation efficiency and lets the swarm find shortest paths.     

Self-organized flocking in mobile robot swarms

In this paper, we study self-organized flocking in a swarm of mobile robots. We present Kobot, a mobile robot platform developed specifically for swarm robotic studies. We describe its infrared-based short range sensing system, capable of measuring the distance from obstacles and detecting kin robots, and a novel sensing system called the virtual heading system (VHS) which uses a digital compass and a wireless communication module for sensing the relative headings of neighboring robots. We propose a behavior based on heading alignment and proximal control that is capable of generating self-organized flocking in a swarm of Kobots. By self-organized flocking we mean that a swarm of mobile robots, initially connected via proximal sensing, is able to wander in an environment by moving as a coherent group in open space and to avoid obstacles as if it were a “super-organism”. We propose a number of metrics to evaluate the quality of flocking. We use a default set of behavioral parameter values that can generate acceptable flocking in robots, and analyze the sensitivity of the flocking behavior against changes in each of the parameters using the metrics that were proposed. We show that the proposed behavior can generate flocking in a small group of physical robots in a closed arena as well as in a swarm of 1000 simulated robots in open space. We vary the three main characteristics of the VHS, namely: (1) the amount and nature of noise in the measurement of heading, (2) the number of VHS neighbors, and (3) the range of wireless communication. Our experiments show that the range of communication is the main factor that determines the maximum number of robots that can flock together and that the behavior is highly robust against the other two VHS characteristics. We conclude by discussing this result in the light of related theoretical studies in statistical physics.     

Majority-rule opinion dynamics with differential latency: a mechanism for self-organized collective decision-making

Collective decision-making is a process whereby the members of a group decide on a course of action by consensus. In this paper, we propose a collective decision-making mechanism for robot swarms deployed in scenarios in which robots can choose between two actions that have the same effects but that have different execution times. The proposed mechanism allows a swarm composed of robots with no explicit knowledge about the difference in execution times between the two actions to choose the one with the shorter execution time. We use an opinion formation model that captures important elements of the scenarios in which the proposed mechanism can be used in order to predict the system??s behavior. The model predicts that when the two actions have different average execution times, the swarm chooses with high probability the action with the shorter average execution time. We validate the model??s predictions through a swarm robotics experiment in which robot teams must choose one of two paths of different length that connect two locations. Thanks to the proposed mechanism, a swarm made of robot teams that do not measure time or distance is able to choose the shorter path.     

Self-organized cooperation between robotic swarms

We study self-organized cooperation between heterogeneous robotic swarms. The robots of each swarm play distinct roles based on their different characteristics. We investigate how the use of simple local interactions between the robots of the different swarms can let the swarms cooperate in order to solve complex tasks. We focus on an indoor navigation task, in which we use a swarm of wheeled robots, called foot-bots, and a swarm of flying robots that can attach to the ceiling, called eye-bots. The task of the foot-bots is to move back and forth between a source and a target location. The role of the eye-bots is to guide foot-bots: they choose positions at the ceiling and from there give local directional instructions to foot-bots passing by. To obtain efficient paths for foot-bot navigation, eye-bots need on the one hand to choose good positions and on the other hand learn the right instructions to give. We investigate each of these aspects. Our solution is based on a process of mutual adaptation, in which foot-bots execute instructions given by eye-bots, and eye-bots observe the behavior of foot-bots to adapt their position and the instructions they give. Our approach is inspired by pheromone mediated navigation of ants, as eye-bots serve as stigmergic markers for foot-bot navigation. Through simulation, we show how this system is able to find efficient paths in complex environments, and to display different kinds of complex and scalable self-organized behaviors, such as shortest path finding and automatic traffic spreading.     

Swarm robotics: a review from the swarm engineering perspective

Swarm robotics is an approach to collective robotics that takes inspiration from the self-organized behaviors of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of robots. In this paper, we analyze the literature from the point of view of swarm engineering: we focus mainly on ideas and concepts that contribute to the advancement of swarm robotics as an engineering field and that could be relevant to tackle real-world applications. Swarm engineering is an emerging discipline that aims at defining systematic and well founded procedures for modeling, designing, realizing, verifying, validating, operating, and maintaining a swarm robotics system. We propose two taxonomies: in the first taxonomy, we classify works that deal with design and analysis methods; in the second taxonomy, we classify works according to the collective behavior studied. We conclude with a discussion of the current limits of swarm robotics as an engineering discipline and with suggestions for future research directions.     

SVM-based learning control of space robots in capturing operation

In this paper, we presents a novel approach for tracking and catching operation of space robots using learning and transferring human control strategies (HCS). We firstly use an efficient support vector machine (SVM) to parametrize the model of HCS. Then we develop a new SVM-based learning structure to better implement human control strategy learning in tracking and capturing control. The approach is fundamentally valuable in dealing with some problems such as small sample data and local minima, and so on. Therefore this approach is efficient in modeling, understanding and transferring its learning process. The simulation results attest that this approach is useful and feasible in generating tracking trajectory and catching objects autonomously.     

From embodied mind to embodied robotics: Humanities and system theoretical aspects

After an introduction (1) the article analyzes the evolution of the embodied mind (2), the innovation of embodied robotics (3), and finally discusses conclusions of embodied robotics for human responsibility (4). Considering the evolution of the embodied mind (2), we start with an introduction of complex systems and nonlinear dynamics (2.1), apply this approach to neural self-organization (2.2), distinguish degrees of complexity of the brain (2.3), explain the emergence of cognitive states by complex systems dynamics (2.4), and discuss criteria for modeling the brain as complex nonlinear system (2.5). The innovation of embodied robotics (3) is a challenge of future technology. We start with the distinction of symbolic and embodied AI (3.1) and explain embodied robots as dynamical systems (3.2). Self-organization needs self-control of technical systems (3.3). Cellular neural networks (CNN) are an example of self-organizing technical systems offering new avenues for neurobionics (3.4). In general, technical neural networks support different kinds of learning robots (3.5). Finally, embodied robotics aim at the development of cognitive and conscious robots (3.6).