Motion in ambiguity: Coordinated active global localization for multiple robots

The task of the robot in localization is to find out where it is, through sensing and motion. In environments which possess relatively few features that enable a robot to unambiguously determine its location, global localization algorithms can result in ‘multiple hypotheses’ locations of a robot. This is inevitable with global localization algorithms, as the local environment seen by a robot repeats at other parts of the map. Thus, for effective localization, the robot has to be actively guided to those locations where there is a maximum chance of eliminating most of the ambiguous states — which is often referred to as ‘active localization’. When extended to multi-robotic scenarios where all robots possess more than one hypothesis of their position, there is an opportunity to do better by using robots, apart from obstacles, as ‘hypotheses resolving agents’. The paper presents a unified framework which accounts for the map structure as well as measurement amongst robots, while guiding a set of robots to locations where they can singularize to a unique state. The strategy shepherds the robots to places where the probability of obtaining a unique hypothesis for a set of multiple robots is a maximum. Another aspect of framework demonstrates the idea of dispatching localized robots to locations where they can assist a maximum of the remaining unlocalized robots to overcome their ambiguity, named as ‘coordinated localization’. The appropriateness of our approach is demonstrated empirically in both simulation & real-time (on Amigo-bots) and its efficacy verified. Extensive comparative analysis portrays the advantage of the current method over others that do not perform active localization in a multi-robotic sense. It also portrays the performance gain by considering map structure and robot placement to actively localize over methods that consider only one of them or neither. Theoretical backing stems from the proven completeness of the method for a large category of diverse environments.     

COBOS: Cooperative backoff adaptive scheme for multirobot task allocation

In this paper, the cooperative backoff adaptive scheme (COBOS) is proposed for task allocation amongst a team of heterogeneous robots. The COBOS operates in regions with limited communication ranges, and is robust against robot malfunctions and uncertain task specifications, with each task potentially requiring multiple robots. The portability of tasks across teams (or when team demography changes) is improved by specifying tasks using basis tasks in a matrix framework. The adaptive feature of COBOS further increases the flexibility of robot teams, allowing robots to adjust their actions based on past experience. In addition, we study the properties of COBOS: operation domain; communication requirements; computational complexity; and solution quality; and compare the scheme with other task-allocation mechanisms. Realistic simulations are carried out to verify the effectiveness of the proposed scheme.     

Multiple robot programming using a concurrent logic language

Advances in robotics has led to the cooperation of multiple robots among themselves and with their industrial automation environment. Efficient interaction with industrial robots thus becomes one of the key factors in the successful utilization of this modern equipment. When multiple manipulators have to be coordinated, there is a need for a new programming approach that facilitates and encompasses the needs of concurrency, synchronization, timing, and communication. Most robot languages have been developed with little attention being given to the integration of the robot with its environment. Currently, there is a gap between the robot capabilities, the task definition environment, and language facilities supplied to use robots.This paper analyzes the needs and then establishes that a concurrent logic programming approach is a step towards achieving a multi-robot knowledgeable task programming. In particular, the FCP dialect of concurrent Prolog is demonstrated, and analyzed.This research is partially supported by the Paul Ivanier Center for research in robots and production management.     

Distributed multi-robot coordination in area exploration

This paper proposes a reliable and efficient multi-robot coordination algorithm to accomplish an area exploration task given that the communication range of each robot is limited. This algorithm is based on a distributed bidding model to coordinate the movement of multiple robots. Two measures are developed to accommodate the limited-range communications. First, the distances between robots are considered in the bidding algorithm so that the robots tend to stay close to each other. Second, a map synchronization mechanism, based on a novel sequence number-based map representation and an effective robot map update tracking, is proposed to reduce the exchanged data volume when robot subnetworks merge. Simulation results show the effectiveness of the use of nearness measure, as well as the map synchronization mechanism. By handling the limited communication range we can make the coordination algorithms more realistic in multi-robot applications.     

Cooperative multi-robot systems:: A study of vision-based 3-D mapping using information theory

Building cooperatively 3-D maps of unknown environments is one of the application fields of multi-robot systems. This article addresses that problem through a probabilistic approach based on information theory. A distributed cooperative architecture model is formulated whereby robots exhibit cooperation through efficient information sharing. A probabilistic model of a 3-D map and a statistical sensor model are used to update the map upon range measurements, with an explicit representation of uncertainty through the definition of the map’s entropy. Each robot is able to build a 3-D map upon measurements from its own range sensor and is committed to cooperate with other robots by sharing useful measurements. An entropy-based measure of information utility is used to define a cooperation strategy for sharing useful information, without overwhelming communication resources with redundant or unnecessary information. Each robot reduces the map’s uncertainty by exploring maximum information viewpoints, by using its current map to drive its sensor to frontier regions having maximum entropy gradient. The proposed framework is validated through experiments with mobile robots equipped with stereo-vision sensors.     

Guiding robots’ behaviors using pheromone communication

This paper describes an ongoing project to investigate the uses of pheromones as a means of communication in robotics. The particular example of pheromone communication considered here was inspired by queen bee pheromones that have a number of crucial functions in a bee colony, such as keeping together and stabilizing the colony. In the context of a robotic system, one of the proposed applications for robot pheromones is to allow a group of robots to be guided by a robot leader. The robot leader could release different chemicals to elicit a range of behaviors from other members of the group. A change of the operating temperature of tin oxide gas sensors has been implemented in order to differentiate different chemicals. This paper provides details of the robots used in the project and their behaviors. The sensors, especially the method of using the tin oxide gas sensors, the robot control algorithms and experimental results are presented. In this project, pheromones were used to trigger congregating behavior and light seeking in a group of robots.

Heterogeneous Teams of Modular Robots for Mapping and Exploration

In this article, we present the design of a team of heterogeneous, centimeter-scale robots that collaborate to map and explore unknown environments. The robots, called Millibots, are configured from modular components that include sonar and IR sensors, camera, communication, computation, and mobility modules. Robots with different configurations use their special capabilities collaboratively to accomplish a given task. For mapping and exploration with multiple robots, it is critical to know the relative positions of each robot with respect to the others. We have developed a novel localization system that uses sonar-based distance measurements to determine the positions of all the robots in the group. With their positions known, we use an occupancy grid Bayesian mapping algorithm to combine the sensor data from multiple robots with different sensing modalities. Finally, we present the results of several mapping experiments conducted by a user-guided team of five robots operating in a room containing multiple obstacles.     

Gradual spatial pattern formation of homogeneous robot group

This paper proposes a gradual formation of a spatial pattern for a homogeneous robot group. The autonomous formation of spatial pattern is one of key technologies for the advancement of cooperative robotic systems because a pattern formation can be regarded as function differentiation of a multi-agent system. When multiple autonomous robots without a given local task cooperatively work for a global objective, the function differentiation is the first and indispensable step. For example, each member of cooperative insects or animals can autonomously recognize own local tasks through mutual communication with local members. There were a lot of papers that reported a spatial pattern formation of multiple robots, but the global information was supposed to be available in their approaches. It is however almost impractical assumption for a small robot to be equipped with an advanced sensing system for global localization due to robot’s scale and sensor size. The local information-based algorithm for the pattern formation is desired even if each robot is not equipped with a global localization sensor.We therefore propose a gradual pattern formation algorithm, i.e., a group of robots improves complexity of their pattern from to a simple pattern to a goal pattern like a polygon. In the algorithm, the Turing diffusion-driven instability theory is used so that it could differentiate roles of each robot in a group based only on local information. In experiment, we demonstrate that robots can make a few polygon patterns from a circle pattern by periodically differentiating robot’s roles into a vertex or a side. We show utilities of the proposed gradual pattern formation algorithm for multiple autonomous robots based on local information through some experiments.     

A New Optimal Force Distribution Scheme of Multiple Cooperating Robots Using Dual Method

A new optimal force distribution scheme of multiple cooperating robots is proposed, in which the duality theory of nonlinear programming (NLP) is combined with the quadratic programming (QP) approach. The optimal force distribution problem is formulated as a QP problem with both linear and quadratic constraints, and its solution is obtained by an efficient algorithm. The use of the quadratic constraints is important in that it considerably reduces the number of constraints, thus enabling the Dual method of NLP to be used in the solution algorithm. Moreover, it can treat norm constraints without approximation, such as bound of the norm of the force exerted by each robot. The proposed scheme is more efficient in terms of speed than any other method. Numerical examples of two PUMA robot task using the proposed method and a well-known fast method are compared, and the results indicate the capability of real time application of our method.     

Robot Teams for Intervention in Confined and Structured Environments

Safety, security, and rescue robotics can be extremely useful in emergency scenarios such as mining accidents or tunnel collapses where robot teams can be used to carry out cooperative exploration, intervention, or logistic missions. Deploying a multirobot team in such confined environments poses multiple challenges that involve task planning, motion planning, localization and mapping, safe navigation, coordination, and communications among all the robots. To complete their mission, robots have to be able to move in the environment with full autonomy while at the same time maintaining communication among themselves and with their human operators to accomplish team collaboration. Guaranteeing connectivity enables robots to explicitly exchange information needed in the execution of collaborative tasks and allows operators to monitor and teleoperate the robots and receive information about the environment. In this work, we present a system that integrates several research aspects to achieve a real exploration exercise in a tunnel using a robot team. These aspects are as follows: deployment planning, semantic feature recognition, multirobot navigation, localization, map building, and real‐time communications. Two experimental scenarios have been used for the assessment of the system. The first is the Spanish Santa Marta mine, a large mazelike environment selected for its complexity for all the tasks involved. The second is the Spanish‐French Somport tunnel, an old railway between Spain and France through the Central Pyrenees, used to carry out the real‐world experiments. The latter is a simpler scenario, but it serves to highlight the real communication issues.     

Concurrent localization of multiple robots

A concurrent localization method for multiple robots using ultrasonic beacons is proposed. This method provides a high-accuracy solution using only low-price sensors. To measure the distance of a mobile robot from a beacon at a known position, the mobile robot alerts one beacon to send out an ultrasonic signal to measure the traveling time from the beacon to the mobile robot. When multiple robots requiring localization are moving in the same block, it is necessary to have a schedule to choose the measuring sequence in order to overcome constant ultrasonic signal interference among robots. However, the increased time delay needed to estimate the positions of multiple robots degrades the localization accuracy. To solve this problem, we propose an efficient localization algorithm for multiple robots, where the robots are in groups of one master robot and several slave robots. In this method, when a master robot calls a beacon, all the group robots simultaneously receive an identical ultrasonic signal to estimate their positions. The effectiveness of the proposed algorithm has been verified through experiments.     

Decentralized Cooperative SLAM for Sparsely-Communicating Robot Networks: A Centralized-Equivalent Approach

Communication between robots is key to performance in cooperative multi-robot systems. In practice, communication connections for information exchange between all robots are not always guaranteed, which adds difficulty in performing state estimation. This paper examines the decentralized cooperative simultaneous localization and mapping (SLAM) problem, in which each robot is required to estimate the map and all robot states under a sparsely-communicating and dynamic network. We show how the exact, centralized-equivalent estimate can be obtained by all robots in the network in a decentralized manner even when the network is never fully connected. Furthermore, a robot only needs to consider its own knowledge of the network topology in order to detect when the centralized-equivalent estimate is obtainable. Our approach is validated through more than 250 min of hardware experiments using a team of real robots. The resulting estimates are compared against accurate groundtruth data for all robot poses and landmark positions. In addition, we examined the effects of communication range limit on our algorithm’s performance.     

Path planning strategy for autonomous mobile robot navigation using Petri-GA optimisation

In this paper, a novel knowledge based genetic algorithm (GA) for path planning of multiple robots for multiple targets seeking behaviour in presence of obstacles is proposed. GA technique has been incorporated in Petri-Net model to make an integrated navigational controller. The proposed algorithm is based upon an iterative non-linear search, which utilises matches between observed geometry of the environment and a priori map of position locations, to estimate a suitable heading angle, there by correcting the position and orientation of the robots to find targets. This knowledge based GA is capable of finding an optimal or near optimal robot path in complex environments. The Petri-GA model can handle inter robot collision avoidance more effectively than the stand alone GA. The resulting navigation algorithm has been implemented on real mobile robots and tested in various environments to validate the developed control scheme.     

Smooth task switching through behaviour competition

Navigation in large-scale environments is composed of different local tasks. To achieve smooth switching between these tasks and thus a continuous control signal, usually a precise map of the environment and an exact pose estimate of the robot are needed. Both are hard to fulfil for experiments in real-world settings. We present a system that shows how one can relax the need for accurate metric models of the environment while at the same time achieving smooth task switching. To facilitate this scheme the dynamical systems approach is used, which incorporates behaviour coordination through competition in a dynamic framework. Feature detectors use sonar data to provide means for local navigation. This ability combined with a simple topological map constitutes a complete navigation system for large-scale office environments. Experiments showed that a Scout robot using this scheme is able to successfully navigate through our whole institute. Through the use of the dynamic behaviour coordination, switching between the navigational tasks occurs in a smooth manner leading to continuous control of the platform.     

异构多机器人协作系统研究进展

异构多机器人系统可以发挥单一结构机器人在某个领域的优点而达到整体的最优配置,机器人的功能和接口协议对协作系统影响很大.IGRS协议是我国在信息设备协作领域中惟一的国际标准,为异构多机器人协作提供了有效的支持.对国内外相关研究进行系统地归纳和总结,找出需要解决的问题,并在课题组研制的多种机器人平台上,从3个方面阐述了基于IGRS协议的异构多机器人协作系统:异构机器人的定位、通信以及感知方案;异构机器人协商策略和分组方案;机器人的功能分类和规划,提出了细粒度可控的任务委托分配方案.     

Appearance-based mapping and localization for mobile robots using a feature stability histogram

The strength of appearance-based mapping models for mobile robots lies in their ability to represent the environment through high-level image features and to provide human-readable information. However, developing a mapping and a localization method using these kinds of models is very challenging, especially if robots must deal with long-term mapping, localization, navigation, occlusions, and dynamic environments. In other words, the mobile robot has to deal with environmental appearance change, which modifies its representation of the environment. This paper proposes an indoor appearance-based mapping and a localization method for mobile robots based on the human memory model, which was used to build a Feature Stability Histogram (FSH) at each node in the robot topological map. This FSH registers local feature stability over time through a voting scheme, and the most stable features were considered for mapping, for Bayesian localization and for incrementally updating the current appearance reference view in the topological map. The experimental results are presented using an omnidirectional images dataset acquired over the long-term and considering: illumination changes (time of day, different seasons), occlusions, random removal of features, and perceptual aliasing. The results include a comparison with the approach proposed by Dayoub and Duckett (2008) [19] and the popular Bag-of-Words (Bazeille and Filliat, 2010) [35] approach. The obtained results confirm the viability of our method and indicate that it can adapt the internal map representation over time to localize the robot both globally and locally.     

通信距离约束下异构水下爬游机器人任务分配

为了保证执行任务的水下爬游机器人之间时刻保持信息交互,提出了一种带通信距离约束的异构水下爬游机器人集群任务分配方法;首先,建立了异构水下爬游机器人集群的任务分配数学模型;其次,分析了多水下爬游机器人通信距离、航程等约束条件;最后,采用蚁群优化算法对异构水下爬游机器人集群的任务分配问题进行求解,在满足约束条件情况下实现了多爬游机器人总航行距离最短;仿真验证了该方法在通信距离约束下实现多水下爬游机器人任务分配的有效性.     

An improved model for swarm robotics in surveillance based on cellular automata and repulsive pheromone with discrete diffusion

ABSTRACT

An inverted ant cellular automata model called IACA-DI is proposed for the coordination of a swarm of robots performing the surveillance task. The swarm communicate indirectly through the repulsive pheromone, which is available as neighborhood information. The pheromone is deposited at each time step by each robot over its neighborhood. The new model started from a previous one named IACA. However, a discrete modeling of the pheromone diffusion is used in IACA-DI returning a sparser distribution of the robots over the environment. Next movement decisions are based on stochastic cellular automata rules that use the pheromone levels in the neighborhood to perform a probabilistic draw. While in IACA all the neighborhood cells participate in this draw, just a subgroup of them participate in the IACA-DI. It is formed by elite cells ? those with the lowest pheromone levels - and some random selected ones. Besides, the cell that keeps the current robot’s direction receives an increment in its probability to be chosen, giving an inertial tendency to the robot motion. Simple simulations were performed enabling to refine parameters and to choose the better strategies. After this refinement, the resultant model was implemented in the simulation platform Webots? aiming to evaluate IACA-DI with real-world robotic architecture in more realistic scenarios.

IACA-DI is a new model for the coordination of robot swarms performing the surveillance task. It is based on cellular automata modeling and the swarm communicate indirectly through the repulsive pheromone deposited by the robots in the environment cells. Letters (a) and (b) show two snapshots from a simulation of a 3-robots swarm performing the surveillance task. The robots start at random positions in an environment composed by 7 rooms in (a). Thus, based on the IACA-DI decisions, they start to make steps to explore the environment aiming to cover all the rooms in a short interval of time. The trace of each robot after 100 time steps is shown in (b) by representing each individual trajectory with a different color. The behavior of each robot is managed by the IACA-DI model, which can be represented by the FSM with 4 states in (c). Different strategies and formulations were investigated for the two major states ‘next position decision’ and ‘pheromone deposition’. The resultant IACA-DI model is analyzed here using simulations performed in Webots? platform -as the snapshots shown in (a) and (b) -with real-world robotic architectures.     

An algorithm for cooperative task allocation in scalable,constrained multiple robot systems

The current trends in the robotics field have led to the development of large-scale multiple robot systems, and they are deployed for complex missions. The robots in the system can communicate and interact with each other for resource sharing and task processing. Many of such systems fail despite the availability of necessary resources. The major reason for this is their poor coordination mechanism. Task planning, which involves task decomposition and task allocation, is paramount in the design of coordination and cooperation strategies of multiple robot systems. Task allocation mechanism allocates the task in a mission to the robots by maximizing the overall expected performance, and thereby reducing the total allocation cost for the team. In this paper, we formulate a heuristic search-based task allocation algorithm for the task processing in heterogeneous multiple robot system, by maximizing the efficiency in terms of both communication and processing cost. We assume a set of decomposed tasks of a mission, which needs to be allocated to the robots. The near-optimal allocation schemes are found using the proposed peer structure algorithm for the given problem, where the number of the tasks is more than the robots present in the system. The cost function is the summation of static overhead cost of robots, assignment cost, and the communication cost between the dependent tasks, if they are assigned to different robots. Experiments are performed to verify the effectiveness of the algorithm by comparing it with the existing methods in terms of computational time and quality of solution. The experimental results show that the proposed algorithm performs the best under different problem scales. This proves that the algorithm can be scaled for larger system and it can work for dynamic multiple robot system.     

Hierarchical multi-robot navigation and formation in unknown environments via deep reinforcement learning and distributed optimization

Compared with a single robot, Multi-robot Systems (MRSs) can undertake more challenging tasks in complex scenarios benefiting from the increased transportation capacity and fault tolerance. This paper presents a hierarchical framework for multi-robot navigation and formation in unknown environments with static and dynamic obstacles, where the robots compute and maintain the optimized formation while making progress to the target together. In the proposed framework, each single robot is capable of navigating to the global target in unknown environments based on its local perception, and only limited communication among robots is required to obtain the optimal formation. Accordingly, three modules are included in this framework. Firstly, we design a learning network based on Deep Deterministic Policy Gradient (DDPG) to address the global navigation task for single robot, which derives end-to-end policies that map the robot’s local perception into its velocity commands. To handle complex obstacle distributions (e.g. narrow/zigzag passage and local minimum) and stabilize the training process, strategies of Curriculum Learning (CL) and Reward Shaping (RS) are combined. Secondly, for an expected formation, its real-time configuration is optimized by a distributed optimization. This configuration considers surrounding obstacles and current formation status, and provides each robot with its formation target. Finally, a velocity adjustment method considering the robot kinematics is designed which adjusts the navigation velocity of each robot according to its formation target, making all the robots navigate to their targets while maintaining the expected formation. This framework allows for formation online reconfiguration and is scalable with the number of robots. Extensive simulations and 3-D evaluations verify that our method can navigate the MRS in unknown environments while maintaining the optimal formation.