Towards cooperation of heterogeneous,autonomous robots: A case study of humanoid and wheeled robots

In this paper a case study of the cooperation of a strongly heterogeneous autonomous robot team, composed of a highly articulated humanoid robot and a wheeled robot with largely complementing and some redundant abilities is presented. By combining strongly heterogeneous robots the diversity of achievable tasks increases as the variety of sensing and motion abilities of the robot system is extended, compared to a usually considered team of homogeneous robots. A number of methodologies and technologies required in order to achieve the long-term goal of cooperation of heterogeneous autonomous robots are discussed, including modeling tasks and robot abilities, task assignment and redistribution, robot behavior modeling and programming, robot middleware and robot simulation. Example solutions and their application to the cooperation of autonomous wheeled and humanoid robots are presented in this case study. The scenario describes a tightly coupled cooperative task, where the humanoid robot and the wheeled robot track a moving ball, which is to be approached and kicked by the humanoid robot into a goal. The task can be fulfilled successfully by combining the abilities of both robots.     

Cooperative embodied communication emerged by interactive humanoid robots

Research on humanoid robots has produced various uses for their body properties in communication. In particular, mutual relationships of body movements between a robot and a human are considered to be important for smooth and natural communication, as they are in human–human communication. We have developed a semi-autonomous humanoid robot system that is capable of cooperative body movements with humans using environment-based sensors and switching communicative units. Concretely, this system realizes natural communication by using typical behaviors such as: “nodding,” “eye-contact,” “face-to-face,” etc. It is important to note that the robot parts are NOT operated directly; only the communicative units in the robot system are switched. We conducted an experiment using the mentioned robot system and verified the importance of cooperative behaviors in a route-guidance situation where a human gives directions to the robot. The task requires a human participant (called the “speaker”) to teach a route to a “hearer” that is (1) a human, (2) a developed robot that performs cooperative movements, and (3) a robot that does not move at all. This experiment is subjectively evaluated through a questionnaire and an analysis of body movements using three-dimensional data from a motion capture system. The results indicate that the cooperative body movements greatly enhance the emotional impressions of human speakers in a route-guidance situation. We believe these results will allow us to develop interactive humanoid robots that sociably communicate with humans.     

复杂环境下群机器人自组织协同多目标围捕

针对动态多目标围捕,提出了一种复杂环境下协同自组织多目标围捕方法.首先设计了多目标在复杂环境下的运动模型,然后通过对生物群体围捕行为的研究,构建了多目标简化虚拟受力模型.基于此受力模型和提出的动态多目标自组织任务分配算法,提出了群机器人协同自组织动态多目标围捕算法,这两个算法只需多目标和个体两最近邻位置信息以及个体面向多目标中心方向的两最近邻任务信息,计算简单高效,易于实现.接着获得了系统稳定时参数的设置范围.由仿真可知,所提的方法具有较好的灵活性、可扩展性和鲁棒性.最后给出了所提方法相较于其它方法的优势.     

A Neural Network Approach to Dynamic Task Assignment of Multirobots

In this paper, a neural network approach to task assignment, based on a self-organizing map (SOM), is proposed for a multirobot system in dynamic environments subject to uncertainties. It is capable of dynamically controlling a group of mobile robots to achieve multiple tasks at different locations, so that the desired number of robots will arrive at every target location from arbitrary initial locations. In the proposed approach, the robot motion planning is integrated with the task assignment, thus the robots start to move once the overall task is given. The robot navigation can be dynamically adjusted to guarantee that each target location has the desired number of robots, even under uncertainties such as when some robots break down. The proposed approach is capable of dealing with changing environments. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies.     

Solving function distribution and behavior design problem for cooperative object handling by multiple mobile robots

This paper addresses the function distribution and behavior design problem for a multirobot system which incorporates a behavior-based dynamic cooperation strategy for object handling. The proposed multiple robot system is composed of a managing robot and homogeneous behavior-based robots. The cooperation strategy in this system is realized in two steps: designing the distributed robot's cooperative behavioral attributes according to the robot's abilities, and organizing these behavioral attributes so that team cooperation is realized. For indicating an incremental style of local behavior construction, an advanced design of cooperative behavior for coping with unknown disturbance is addressed. Additionally, two extended cooperation strategies designed for a path tracking task are described. These three strategies are based on the same concept on performing manipulation in coordination. Therefore, by considering the function distribution among the managing robot and worker robots, and considering behavior design of each worker robot, the proposed system is able to achieve the object handling task with different performances according to the task requirement, such as with or without path tracking and with or without contact with the environment. Experimental results demonstrate the applicability of the proposed system.     

Efficient Boustrophedon Multi-Robot Coverage: an algorithmic approach

This paper presents algorithmic solutions for the complete coverage path planning problem using a team of mobile robots. Multiple robots decrease the time to complete the coverage, but maximal efficiency is only achieved if the number of regions covered multiple times is minimized. A set of multi-robot coverage algorithms is presented that minimize repeat coverage. The algorithms use the same planar cell-based decomposition as the Boustrophedon single robot coverage algorithm, but provide extensions to handle how robots cover a single cell, and how robots are allocated among cells. Specifically, for the coverage task our choice of multi-robot policy strongly depends on the type of communication that exists between the robots. When the robots operate under the line-of-sight communication restriction, keeping them as a team helps to minimize repeat coverage. When communication between the robots is available without any restrictions, the robots are initially distributed through space, and each one is allocated a virtually-bounded area to cover. A greedy auction mechanism is used for task/cell allocation among the robots. Experimental results from different simulated and real environments that illustrate our approach for different communication conditions are presented.     

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.     

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.     

Efficient exploration of unknown indoor environments using a team of mobile robots

Whenever multiple robots have to solve a common task, they need to coordinate their actions to carry out the task efficiently and to avoid interferences between individual robots. This is especially the case when considering the problem of exploring an unknown environment with a team of mobile robots. To achieve efficient terrain coverage with the sensors of the robots, one first needs to identify unknown areas in the environment. Second, one has to assign target locations to the individual robots so that they gather new and relevant information about the environment with their sensors. This assignment should lead to a distribution of the robots over the environment in a way that they avoid redundant work and do not interfere with each other by, for example, blocking their paths. In this paper, we address the problem of efficiently coordinating a large team of mobile robots. To better distribute the robots over the environment and to avoid redundant work, we take into account the type of place a potential target is located in (e.g., a corridor or a room). This knowledge allows us to improve the distribution of robots over the environment compared to approaches lacking this capability. To autonomously determine the type of a place, we apply a classifier learned using the AdaBoost algorithm. The resulting classifier takes laser range data as input and is able to classify the current location with high accuracy. We additionally use a hidden Markov model to consider the spatial dependencies between nearby locations. Our approach to incorporate the information about the type of places in the assignment process has been implemented and tested in different environments. The experiments illustrate that our system effectively distributes the robots over the environment and allows them to accomplish their mission faster compared to approaches that ignore the place labels.     

基于相对方位的多机器人合作定位算法

研究了在未知环境中,利用扩展卡尔曼滤波方法融合内部传感器信息与机器人之间的相对方位观测量,同时定位队列中每个机器人的问题.通过机器人队列共享相对方位观测量,融合不同平台感知的信息,可有效地提高整个队列的定位精度.分析了该方法与机器人分布和运动的关系及存在的缺陷,针对这一问题,提出了改进措施,从而使该方法的可靠性和实用性得到增强.仿真实验验证了改进方法的有效性.     

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.     

Distributed Coordination in Heterogeneous Multi-Robot Systems

Coordination in multi-robot systems is a very active research field in Artificial Intelligence and Robotics, since through coordination one can achieve a more effective execution of the robots' tasks. In this paper we present an approach to distributed coordination of a multi-robot system that is based on dynamic role assignment. The approach relies on the broadcast communication of utility functions that define the capability for every robot to perform a task and on the execution of a coordination protocol for dynamic role assignment. The presented method is robust to communication failures and suitable for application in dynamic environments. In addition to experimental results showing the effectiveness of our approach, the method has been successfully implemented within the team of heterogeneous robots Azzurra Robot Team in a very dynamic hostile environment provided by the RoboCup robotic soccer competitions.     

VARIABLE PATROL PLANNING OF MULTI-ROBOT SYSTEMS BY A COOPERATIVE AUCTION SYSTEM

A cooperative auction system (CAS) is proposed to solve the large-scale multi-robot patrol planning problem. Each robot picks its own patrol points via the cooperative auction system and the system continuously re-auctions, based on the team work performance. The proposed method not only works in static environments but also considers variable path planning when the number of mobile robots increases or decreases during patrol. From the results of the simulation, the proposed approach demonstrates decreased time complexity, a lower routing path cost, improved balance of workload among robots, and the potential to scale to a large number of robots and is adaptive to environmental perturbations when the number of robots changes during patrol.     

A Paradigm for Dynamic Coordination of Multiple Robots

In this paper, we present a paradigm for coordinating multiple robots in the execution of cooperative tasks. The basic idea in the paper is to assign to each robot in the team, a role that determines its actions during the cooperation. The robots dynamically assume and exchange roles in a synchronized manner in order to perform the task successfully, adapting to unexpected events in the environment. We model this mechanism using a hybrid systems framework and apply it in different cooperative tasks: cooperative manipulation and cooperative search and transportation. Simulations and real experiments demonstrating the effectiveness of the proposed paradigm are presented.     

Distributed cooperative load redistribution for fault tolerance in a team of four object-lifting robots

Fault tolerance in a team of cooperative and distributed object-lifting robots is dealt with. It is assumed that one of four object lifting robots misses a portion of its lifting power and the robots must redistribute the load among themselves to perform their task. Two distributed and cooperative methods for load reallocation among the position-controlled robots without requiring them to change their grasp positions are introduced. The first method benefits from the existing redundancy in the number and lifting power of robots. In the second method, the object is tilted in order to move the zero moment point (ZMP) away from the faulty robot and, consequently, redistributing the load. Difficulties in controlling ZMP movements are pointed out. Therefore, the second fault-clearing procedure is designed such that the ZMP position is controlled without resorting to sophisticated or centralized control algorithms. Stability of the proposed methods is mathematically proven and the deadlocks are investigated. It is also noted that the required sensory system and robot behavior in the proposed strategies are exactly the same as those used in the object-lifting task. Consequently, no additional complexity is imposed on the system. The basic idea in ALLIANCE is used for developing a mechanism for each robot to process help requests and select proper actions in a distributed fashion without negotiating with its teammates. Simulation results are given to support the developed methods.     

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.     

Social-welfare based task allocation for multi-robot systems with resource constraints

This paper aims to propose a distributed task allocation algorithm for a team of robots that have constraints on energy resources and operate in an unknown dynamic environment. The objective of the allocation is to maximize task completion ratio while minimizing resource usage. The approach we propose is inspired by the social welfare in economics that helps extend the combined operational lifetime of the team by balancing resource consumptions among robots. This social welfare based task allocation method positions a robot team appropriately in preparedness for dynamic future events and enables to achieve the objectives of the system flexibly depending on the application context. Our simulation-based experiments show that the proposed algorithm outperforms a typical market-based approach in various scenarios.     

Dynamic multi-robot team reconfiguration using weighted voting games

We consider the problem of dynamic reconfiguration of robot teams when they encounter obstacles while navigating in formation, in an initially unknown environment. We have used a framework from coalition game theory called weighted voting games to analyse this problem and proposed two heuristics that can appropriately partition a robot team into sub-teams. We have experimentally verified our technique on teams of e-puck robots of different sizes and with different obstacle geometries, both on the Webots simulator and on physical robots. We have also shown that our technique performs faster and generates considerably fewer partitions than an existing robot coalition formation algorithm.     

Frontier Based Goal Seeking for Robots in Unknown Environments

In this paper, the problem of goal seeking by robots in unknown environments is considered. A frontier based algorithm is proposed for finding a route to a goal in a fully unknown environment, where only the information about the goal region (GR), that is the region where the goal is most likely to be located, is available. The paper uses the concept of frontier cells, which are on the boundary between explored space and unexplored space. A “goal seeking index” is defined for each frontier cell and used to choose the best among them. Modification of the algorithm is proposed with altered choice of frontier cells when wall like obstacles are encountered or when the robot falls in a “trap” situation, to further reduce the number of moves toward the goal. The algorithm is tested extensively in computer simulations as well as in experiments and the results demonstrate that the algorithm effectively directs the robot to the goal and completes the search task in minimal number of moves. The solution to the problem of local minimum is also addressed, which helps in easy escape from a dead-end or dead-lock situation. It is shown that the proposed algorithm performs better than the state of the art agent centered search algorithm RTAA*.     

A PATH PLANNING METHOD BASED ON CELLULAR AUTOMATA FOR COOPERATIVE ROBOTS

A Cellular Automaton-based technique suitable for solving the path planning problem in a distributed robot team is outlined. Real-time path planning is a challenging task that has many applications in the fields of artificial intelligence, moving robots, virtual reality, and agent behavior simulation. The problem refers to finding a collision-free path for autonomous robots between two specified positions in a configuration area. The complexity of the problem increases in systems of multiple robots. More specifically, some distance should be covered by each robot in an unknown environment, avoiding obstacles found on its route to the destination. On the other hand, all robots must adjust their actions in order to keep their initial team formation immutable. Two different formations were tested in order to study the efficiency and the flexibility of the proposed method. Using different formations, the proposed technique could find applications to image processing tasks, swarm intelligence, etc. Furthermore, the presented Cellular Automaton (CA) method was implemented and tested in a real system using three autonomous mobile minirobots called E-pucks. Experimental results indicate that accurate collision-free paths could be created with low computational cost. Additionally, cooperation tasks could be achieved using minimal hardware resources, even in systems with low-cost robots.