Simulation and control of distributed robot search teams

This article describes the simulation of distributed autonomous robots for search and rescue operations. The simulation system is utilized to perform experiments with various control strategies for the robot team and team organizations, evaluating the comparative performance of the strategies and organizations. The objective of the robot team is to, once deployed in an environment (floor-plan) with multiple rooms, cover as many rooms as possible. The simulated robots are capable of navigation through the environment, and can communicate using simple messages. The simulator maintains the world, provides each robot with sensory information, and carries out the actions of the robots. The simulator keeps track of the rooms visited by robots and the elapsed time, in order to evaluate the performance of the robot teams. The robot teams are composed of homogenous robots, i.e., identical control strategies are used to generate the behavior of each robot in the team. The ability to deploy autonomous robots, as opposed to humans, in hazardous search and rescue missions could provide immeasurable benefits.     

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.     

Multiagent-Based Multi-team Formation Control for Mobile Robots

In the field of formation control, researchers generally control multiple robots in only one team, and little research focuses on multi-team formation control. In this paper, we propose an architecture, called Virtual Operator MultiAgent System (VOMAS), to perform formation control for multiple teams of mobile robots with the capabilities and advantages of scalability and autonomy. VOMAS is a hybrid architecture with two main agents. The virtual operator agent handles high level missions and team control, and the robot agent deals with low level formation control. The virtual operator uses four basic services including join, remove, split, and merge requests to perform multi-team control. A new robot can be easily added to a team by cloning a new virtual operator to control it. The robot agent uses a simple formation representation method to show formation to a large number of robots, and it uses the concept of potential field and behavior-based control to perform kinematic control to keep formation both in holonomic and nonholonomic mobile robots. In addition, we also test the stability, robustness, and uncertainty in the simulation. This research was supported by the National Science Council under grant NSC 91-2213-E-194-003.     

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.     

Decentralized planning and control for UAV–UGV cooperative teams

In this paper we study a symbiotic aerial vehicle-ground vehicle robotic team where unmanned aerial vehicles (UAVs) are used for aerial manipulation tasks, while unmanned ground vehicles (UGVs) aid and assist them. UGV can provide a UAV with a safe landing area and transport it across large distances, while UAV can provide an additional degree of freedom for the UGV, enabling it to negotiate obstacles. We propose an overall system control framework that includes high-accuracy motion planning for each individual robot and ad-hoc decentralized mission planning for complex missions. Experimental results obtained in a mockup arena for parcel transportation scenario show that the system is able to plan and execute missions in various environments and that the obtained plans result in lower energy consumption.     

Millibots

Concerns the development of a framework and algorithms for a distributed heterogeneous robot team. Team members exchange sensor information, collaborate to track and identify targets, or even assist each other to scale obstacles. As for sensing, by coordinating its members a team can exploit information derived from multiple disparate viewpoints. A single robot, even though equipped with a large array of different sensing modalities, is limited at any one time to a single viewpoint, but a team of robots can simultaneously collect information from multiple locations. This article describes the design and construction of a team of 7 /spl times/ 7 /spl times/ 7-cm robots called "millibots". We show how the team can exploit collaboration to perform missions such as mapping, exploration, surveillance, and eventually support rescue operations.     

Control sharing in human-robot team interaction

The interaction between humans and robot teams is highly relevant in many application domains, for example in collaborative manufacturing, search and rescue, and logistics. It is well-known that humans and robots have complementary capabilities: Humans are excellent in reasoning and planning in unstructured environments, while robots are very good in performing tasks repetitively and precisely. In consequence, one of the key research questions is how to combine human and robot team decision making and task execution capabilities in order to exploit their complementary skills. From a controls perspective this question boils down to how control should be shared among them. This article surveys advances in human-robot team interaction with special attention devoted to control sharing methodologies. Additionally, aspects affecting the control sharing design, such as human behavior modeling, level of autonomy and human-machine interfaces are identified. Open problems and future research directions towards joint decision making and task execution in human-robot teams are discussed.     

Navigating Multiple Mobile Robots without Direct Communication

A bulk of research is being done for the autonomous navigation of a mobile robot. Multirobot motion planning techniques often assume a direct communication among the robots, which makes them practically unusable. Similarly, approaches assuming the robot moving amid humans assume cooperation of humans, which may not be the case if the human is replaced by a robot. In this paper, a deliberative planning at the higher level with a new cell decomposition technique is presented, along with a reactive planning technique at the finer level, which uses fuzzy logic. Coordination among the robots in the absence of direct communication and knowledge of other robot's intent is a complex research question, which is solved using a simple fuzzy‐based modeling. Experimental results show that the multiple robots maintain comfortable distances from the obstacles, navigate by near optimal paths, can easily escape previously unseen obstacles, and coordinate with each other to avoid collision as well as maintain a large separation. This work displays a simple and easy to interpret system for solving complex coordination problem in multirobotics.     

Operator Performance in Exploration Robotics

Mobile robots can accomplish high-risk tasks without exposing humans to danger: robots go where humans fear to tread. Until the time in which completely autonomous robots are fully deployed, remote operators will be required in order to fulfill desired missions. Remotely controlling a robot requires that the operator receives the information about the robot??s surroundings, as well as its location in the scenario. Based on a set of experiments conducted with users, we evaluate the performance of operators when they are provided with a hand-held-based interface or a desktop-based interface. Results show how performance depends on the task asked of the operator and the scenario in which the robot is moving. The conclusions prove that the operator??s intra-scenario mobility when carrying a hand-held device can counterbalance the limitations of the device. By contrast, the experiments show that if the operator cannot move inside of the scenario, his performance is significantly better when using a desktop-based interface. These results set the basis for a transfer of control policy in missions involving a team of operators, some equipped with hand-held devices and others working remotely with desktop-based computers.     

安全平滑的改进时间弹性带轨迹规划算法

传统TEB(time elastic band)算法在杂乱场景下规划易出现倒退、大转向等异常行为,造成加速度跳变,控制指令不平滑,机器人受到大冲击,不利于移动机器人轨迹跟踪.鉴于此,提出一种改进TEB算法,通过增加危险惩罚因子约束规划更安全的运动轨迹,增加加速度跳变以抑制约束减小运动中的最大冲击,增加末端平滑约束以减小末端冲击,实现目标点平滑、准确到达.构建图优化问题,以机器人的位姿和时间间隔为节点、目标函数和约束函数为边,利用问题的稀疏性快速获得相应时刻点的控制量.最后,通过基于机器人操作系统的大量对比仿真测试以及真实差速机器人上的物理实验对提出的改进TEB算法进行性能验证.结果表明,改进TEB算法在复杂环境中能够规划出更安全、平滑的轨迹,减小机器人所受冲击,实现移动机器人更合理地运动.     

Controlling Ensembles of Robots via a Supervisory Aerial Robot

We consider the task of controlling a large team of non-holonomic ground robots with an unmanned aerial vehicle in a decentralized manner that is invariant to the number of ground robots. The central idea is the development of an abstraction for the team of ground robots that allows the aerial platform to control the team without any knowledge of the specificity of individual vehicles. This happens in much the same way as a human operator can control a single robot vehicle by simply commanding the forward and turning velocities without a detailed knowledge of the specifics of the robot. The abstraction includes a gross model of the shape of the formation of the team, and information about the position and orientation of the team in the plane. We derive controllers that allow the team of robots to move in formation while avoiding collisions and respecting the abstraction commanded by the aerial platform. We propose strategies for controlling the physical spread of the ensemble of robots by splitting and merging the team based on distributed techniques. We provide simulation and experimental results using a team of indoor mobile robots and a three-dimensional, cable-controlled, parallel robot which serves as our indoor unmanned aerial platform.     

Scalable formation control in stealth with limited sensing range

In this paper, a protocol and a control law are designed for a single robot so that a team of such robots can interact and cooperate to reach the displacements from an eligible reference formation. Each robot is equipped with displacement sensors of limited sensing ranges. Communication channels are assumed to be unavailable to the team, and each robot works in stealth mode. The team is scalable such that new robots can be recruited, and existing robots can be dismissed. In order for the team size to be scalable, the extended formation based on relative displacement is established as the reference formation. Thus, using the extended formation as a reference, the control law and the protocol could be flexible. As potential conflicts deflect the robot team from the desired formation, the control law is designed to expose the conflicts to the involved neighboring robots such that the protocol can resolve them. A numerical example is given to illustrate how an extended formation is designed, and a simulation example is conducted to demonstrate the performance and merits of the proposed techniques. Copyright © 2016 John Wiley & Sons, Ltd.     

Multi-robot exploration under the constraints of wireless networking

A multi-robot system can be highly beneficial for exploration, which is a core robotics task. Application domains include, for example, surveillance, reconnaissance, planetary exploration or rescue missions. When using a team of robots, the overall performance can be much faster and more robust. In this article, an approach to multi-robot exploration is presented that takes the constraints of wireless networking into account. An algorithm is introduced based on a population that samples the possible moves of all robots and a utility to select the best one in each time step. Results from two scenarios are presented. In the first one, a team of robots explores its environment while permanently maintaining an ad hoc network structure with each other as well as a base station at a fixed location. In the second one, the robots move freely as a pack while maintaining communication with each other.     

Using Virtual Pheromones and Cameras for Dispersing a Team of Multiple Miniature Robots

To safely and efficiently guide personnel of search and rescue operations in disaster areas, swift gathering of relevant information such as the locations of victims, must occur. Using the concept of ‘repellent virtual pheromones’ inspired by insect colony coordination behaviors, miniature robots can be quickly dispersed to survey a disaster site. Assisted by visual servoing, dispersion of the miniature robots can quickly cover an area. An external observer such as another robot or an overhead camera is brought into the control loop to provide each miniature robot estimations of the positions of all of the other near-by robots in the robotic team. These miniature robots can then move away from the other near-by robots on the team, resulting in the robot collective becoming swiftly distributed through the local area. The technique has been simulated with differing pheromone persistence levels and implemented using the miniature Scout robots, developed by the Center for Distributed Robotics at the University of Minnesota, which are well-suited to surveillance and reconnaissance missions.     

Evolving controllers for autonomous robot search teams

Deploying autonomous robot teams instead of humans in hazardous search and rescue missions could provide immeasurable benefits. In such operations, rescue workers often face environments where information about the physical conditions is impossible to obtain, which not only hampers the efficiency and effectiveness of the effort, but also places the rescuers in life-threatening situations. These types of risk promote the potential for using robot search teams in place of humans. This article presents the design and implementation of controllers to provide robots with appropriate behavior. The effective utilization of genetic algorithms to evolve controllers for teams of homogeneous autonomous robots for area coverage in search and rescue missions is described, along with a presentation of a robotic simulation program which was designed and developed. The main objective of this study was to contribute to efforts which attempt to implement real-world robotic solutions for search and rescue missions.     

移动机器人的时间最优编队

针对移动机器人的最速编队问题,结合路径规划和任务分解,提出一种分派问题的新解法和时间最优的编队策略。该策略充分考虑了障碍物环境约束和各机器人运动时的相互影响,通过将系统整体路径规划的复杂问题分解为独立路径规划问题和冲突协调问题来分别求解,降低了计算的复杂性,并能了快编队。     

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.     

Integration of Heterogeneity for Human-Friendly Robotic Operations

The objective of this paper is to develop an analytical scheme to integrate the heterogeneity of human and robot functions to achieve a human-friendly robotic operations. The heterogeneity of human and robot functions can be characterized by the fact that humans are intelligent while robots are fast, powerful and accurate. Humans can use their knowledge and experience to quickly respond to unexpected events, which makes it easy for humans to deal with unstructured environments. In contrast, robots can easily enhance the mechanical power of humans and the ability of humans to work remotely. Therefore, robots are capable of performing precise and repetitive tasks at high speed or in a hazardous environment. The important issue, in light of human/robot heterogeneity, is how to plan and control a robotic operation such that the human and the robot can cooperate in a complementary manner. Thus, a task which cannot be done by either human or robot alone can be performed efficiently and robustly by both. This paper introduces a new paradigm for human/robot interactive systems, heterogeneous function-based human/robot cooperation. A new perceptive action reference frame has been developed in the paper. It matches human perception and robot sensory measurement, and provides a platform for modeling the human/robot cooperative operations. The theoretical results presented in the paper have laid down a foundation for stability analysis as well as a planning and control system design of human/robot integrated systems. The implementations and experimental results have clearly demonstrated the advantages of proposed methods.     

Safe and efficient motion planning of multiple mobile robots based on artificial potential for human behavior and robot congestion

In order for robots to exist together with humans, safety for the humans has to be strictly ensured. On the other hand, safety might decrease working efficiency of robots. Namely, this is a trade-off problem between human safety and robot efficiency in a field of human–robot interaction. For this problem, we propose a novel motion planning technique of multiple mobile robots. Two artificial potentials are presented for generating repulsive force. The first potential is provided for humans. The von Mises distribution is used to consider the behavioral property of humans. The second potential is provided for the robots. The Kernel density estimation is used to consider the global robot congestion. Through simulation experiments, the effectiveness of the behavior and congestion potentials of the motion planning technique for human safety and robot efficiency is discussed. Moreover, a sensing system for humans in a real environment is developed. From experimental results, the significance of the behavior potential based on the actual humans is discussed. For the coexistence of humans and robots, it is important to evaluate a mutual influence between them. For this purpose, a virtual space is built using projection mapping. Finally, the effectiveness of the motion planning technique for the human–robot interaction is discussed from the point of view of not only robots but also humans.     

Multi-agent Rapidly-exploring Pseudo-random Tree

Real-time motion planning and control for groups of heterogeneous and under-actuated robots subject to disturbances and uncertainties in cluttered constrained environments is the key problem addressed in this paper. Here we present the Multi-agent Rapidly-exploring Pseudo-random Tree (MRPT), a novel technique based on a classical Probabilistic Road Map (PRM) algorithm for application in robot team cooperation. Our main contribution lies in the proposal of an extension of a probabilistic approach to be used as a deterministic planner in distributed complex multi-agent systems, keeping the main advantages of PRM strategies like simplicity, fast convergence, and probabilistic completeness. Our methodology is fully distributed, addressing missions with multi-robot teams represented by high nonlinear models and a great number of Degrees of Freedom (DoFs), endowing each agent with the ability of coordinating its own movement with other agents while avoiding collisions with obstacles. The inference of the entire team’s behavior at each time instant by each individual agent is the main improvement of our method. This scheme, which is behavioral in nature, also makes the system less susceptible to failures due to intensive traffic communication among robots. We evaluate the time complexity of our method and show its applicability in planning and executing search and rescue missions for a group of robots in S E3 outdoor scenarios and present both simulated and real-world results.