Balancing task allocation in multi-robot systems using K-means clustering and auction based mechanisms

This paper aims to solve the balanced multi-robot task allocation problem. Multi-robot systems are becoming more and more significant in industrial, commercial and scientific applications. Effectively allocating tasks to multi-robots i.e. utilizing all robots in a cost effective manner becomes a tedious process. The current attempts made by the researchers concentrate only on minimizing the distance between the robots and the tasks, and not much importance is given to the balancing of work loads among robots. It is also found from the literature that the multi-robot system is analogous to Multiple Travelling Salesman Problem (MTSP). This paper attempts to develop mechanism to address the above two issues with objective of minimizing the distance travelled by ‘m’ robots and balancing the work load between ‘m’ robots equally. The proposed approach has two fold, first develops a mathematical model for balanced multi-robot task allocation problem, and secondly proposes a methodology to solve the model in three stages. Stage I groups the ‘N’ tasks into ‘n’ clusters of tasks using K-means clustering technique with the objective of minimizing the distance between the tasks, stage II calculates the travel cost of robot and clusters combination, stage III allocates the robot to the clusters in order to utilise all robot in a cost effective manner.     

Mapping Simple Polygons: How Robots Benefit from Looking Back

We consider the problem of mapping an initially unknown polygon of size n with a simple robot that moves inside the polygon along straight lines between the vertices. The robot sees distant vertices in counter-clockwise order and is able to recognize the vertex among them which it came from in its last move, i.e. the robot can look back. Other than that the robot has no means of distinguishing distant vertices. We assume that an upper bound on n is known to the robot beforehand and show that it can always uniquely reconstruct the visibility graph of the polygon. Additionally, we show that multiple identical and deterministic robots can always solve the weak rendezvous problem in which the robots need to position themselves such that all of them are mutually visible to each other. Our results are tight in the sense that the strong rendezvous problem, where robots need to gather at a vertex, cannot be solved in general, and, without knowing a bound beforehand, not even n can be determined. In terms of mobile agents exploring a graph, our result implies that they can reconstruct any graph that is the visibility graph of a simple polygon. This is in contrast to the known result that the reconstruction of arbitrary graphs is impossible in general, even if n is known.     

Gathering on rings under the Look–Compute–Move model

A set of robots arbitrarily placed on different nodes of an anonymous ring have to meet at one common node and there remain. This problem is known in the literature as the gathering. Anonymous and oblivious robots operate in Look–Compute–Move cycles; in one cycle, a robot takes a snapshot of the current configuration (Look), decides whether to stay idle or to move to one of its neighbors (Compute), and in the latter case makes the computed move instantaneously (Move). Cycles are asynchronous among robots. Moreover, each robot is empowered by the so called multiplicity detection capability, that is, it is able to detect during its Look operation whether a node is empty, or occupied by one robot, or occupied by an undefined number of robots greater than one. The described problem has been extensively studied during the last years. However, the known solutions work only for specific initial configurations and leave some open cases. In this paper, we provide an algorithm which solves the general problem but for few marginal and specific cases, and is able to detect all the ungatherable configurations. It is worth noting that our new algorithm makes use of some previous techniques and unifies them with new strategies in order to deal with any initial configuration, even those left open by previous works.     

Scalable multirobot planning for informed spatial sampling

This paper presents a distributed scalable multi-robot planning algorithm for informed sampling of quasistatic spatials fields. We address the problem of efficient data collection using multiple autonomous vehicles and consider the effects of communication between multiple robots, acting independently, on the overall sampling performance of the team. We focus on the distributed sampling problem where the robots operate independent of their teammates, but have the ability to communicate their current state to other neighbors within a fixed communication range. Our proposed approach is scalable and adaptive to various environmental scenarios, changing robot team configurations, and runs in real-time, which are important features for many real-world applications. We compare the performance of our proposed algorithm to baseline strategies through simulated experiments that utilize models derived from both synthetic and field deployment data. The results show that our sampling algorithm is efficient even when robots in the team are operating with a limited communication range, thus demonstrating the scalability of our method in sampling large-scale environments.

     

Analyzing the Multiple-target-multiple-agent Scenario Using Optimal Assignment Algorithms

This work considers the problem of maximum utilization of a set of mobile robots with limited sensor-range capabilities and limited travel distances. The robots are initially in random positions. A set of robots properly guards or covers a region if every point within the region is within the effective sensor range of at least one vehicle. We wish to move the vehicles into surveillance positions so as to guard or cover a region, while minimizing the maximum distance traveled by any vehicle. This problem can be formulated as an assignment problem, in which we must optimally decide which robot to assign to which slot of a desired matrix of grid points. The cost function is the maximum distance traveled by any robot. Assignment problems can be solved very efficiently. Solution times for one hundred robots took only seconds on a Silicon Graphics Crimson workstation. The initial positions of all the robots can be sampled by a central base station and their newly assigned positions communicated back to the robots. Alternatively, the robots can establish their own coordinate system with the origin fixed at one of the robots and orientation determined by the compass bearing of another robot relative to this robot. This paper presents example solutions to the multiple-target-multiple-agent scenario using a matching algorithm. Two separate cases with one hundred agents in each were analyzed using this method. We have found these mobile robot problems to be a very interesting application of optimal assignment algorithms, and we expect this to be a fruitful area for future research.     

State-chain sequential feedback reinforcement learning for path planning of autonomous mobile robots

This paper deals with a new approach based on Q-learning for solving the problem of mobile robot path planning in complex unknown static environments.As a computational approach to learning through interaction with the environment,reinforcement learning algorithms have been widely used for intelligent robot control,especially in the field of autonomous mobile robots.However,the learning process is slow and cumbersome.For practical applications,rapid rates of convergence are required.Aiming at the problem of slow convergence and long learning time for Q-learning based mobile robot path planning,a state-chain sequential feedback Q-learning algorithm is proposed for quickly searching for the optimal path of mobile robots in complex unknown static environments.The state chain is built during the searching process.After one action is chosen and the reward is received,the Q-values of the state-action pairs on the previously built state chain are sequentially updated with one-step Q-learning.With the increasing number of Q-values updated after one action,the number of actual steps for convergence decreases and thus,the learning time decreases,where a step is a state transition.Extensive simulations validate the efficiency of the newly proposed approach for mobile robot path planning in complex environments.The results show that the new approach has a high convergence speed and that the robot can find the collision-free optimal path in complex unknown static environments with much shorter time,compared with the one-step Q-learning algorithm and the Q(λ)-learning algorithm.     

Anonymous graph exploration without collision by mobile robots

Considering autonomous mobile robots moving on a finite anonymous graph, this paper focuses on the Constrained Perpetual Graph Exploration problem (CPGE). That problem requires each robot to perpetually visit all the vertices of the graph, in such a way that no vertex hosts more than one robot at a time, and each edge is traversed by at most one robot at a time. The paper states an upper bound k on the number of robots that can be placed in the graph while keeping CPGE solvability. To make the impossibility result as strong as possible (no more than k robots can be initially placed in the graph), this upper bound is established under a strong assumption, namely, there is an omniscient daemon that is able to coordinate the robots movements at each round of the synchronous system. Interestingly, this upper bound is related to the topology of the graph. More precisely, the paper associates with each graph a labeled tree that captures the paths that have to be traversed by a single robot at a time (as if they were a simple edge). The length of the longest of these labeled paths reveals to be the key parameter to determine the upper bound k on the number of robots.     

Robots in the classroom: Differences in students’ perceptions of credibility and learning between “teacher as robot” and “robot as teacher”

Advancements in technology are bringing robotics into interpersonal communication contexts, including the college classroom. This study was one of the first to examine college students’ communication-related perceptions of robots being used in an instructional capacity. Student participants rated both a human instructor using a telepresence robot and an autonomous social robot delivering the same lesson as credible. However, students gave higher credibility ratings to the teacher as robot, which led to differences between the two instructional agents in their learning outcomes. Students reported more affective learning from the teacher as robot than the robot as teacher, despite controlled instructional performances. Instructional agent type had both direct and indirect effects on behavioral learning. The direct effect suggests a potential machine heuristic in which students are more likely to follow behavioral suggestions offered by an autonomous social robot. The findings generally support the MAIN model and the Computers are Social Actors paradigm, but suggest that future work needs to be done in this area.     

A hybrid metaheuristic algorithm to optimise a real-world robotic cell

In this paper, a real-world robotic cell is investigated by transforming it into a special job shop with a set of stationary robots for manufacturing the parts of a product (i.e., operations of a job) at multiple operational stages. In addition, this robotic cell contains a particular mobile robot to transport the parts among stationary robots inside the cell as well as a depot (for initialising the production) and a stockpile (for stocking the complete products) outside the cell. Thus, a new scheduling problem called Blocking Job Shop Scheduling problem with Robotic Transportation (BJSSRT) is proposed. A numerical example is presented to illustrate the characteristics and complexity of BJSSRT. According to the problem properties, four types of robotic movements are defined for a mobile robot in an operation’s execution: processing-purpose, depot-purpose, return-purpose and stocking-purpose. By satisfying complex feasibility conditions, an innovative graph-based constructive algorithm is developed to produce a good feasible BJSSRT schedule. Embedded with the constructive algorithm, a hybrid Tabu Search and Threshold Accepting metaheuristic algorithm is developed to find a near-optimal solution in an efficient way. The proposed BJSSRT methodology has practical benefits in modelling the automated production system using stationary and mobile robots, especially in manufacturing and mining industries.     

Applying a path planner based on RRT to cooperative multirobot box-pushing

Considering robot systems in the real world, a multirobot system where multiple robots work simultaneously without colliding with each other is more practical than a single-robot system where only one robot works. Therefore, solving the path-planning problem in a multirobot system is very important. In this study, we developed a path-planner based on the rapidly exploring random tree (RRT), which is a data structure and algorithm designed for efficiently searching for multirobot box-pushing, and made experiments in real environments. A path planner must construct a plan which avoids the robot colliding with obstacles or with other robots. Moreover, in some cases, a robot must collaborate with other robots to transport the box without colliding with any obstacles. Our proposed path planner constructs a box-transportation plan and the path plans of each robot bearing the above considerations in mind. Experimental results showed that our proposed planner can construct a multirobot box-pushing plan without colliding with obstacles, and that the robots can execute tasks according to the plan in real environments. We also checked that multiple robots can perform problem tasks when only one robot could not transport the box to the goal. This work was presented in part at the 13th International Symposium on Articifial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Darwinian swarm exploration under communication constraints: Initial deployment and fault-tolerance assessment

In most real multi-robot applications, such as search-and-rescue, cooperative robots have to move to complete their tasks while maintaining communication among themselves without the aid of a communication infrastructure. However, initially deploying and ensuring a mobile ad-hoc network in real and complex environments is an arduous task since the strength of the connection between two nodes (i.e., robots) can change rapidly in time or even disappear. An extension of the Particle Swarm Optimization to multi-robot applications has been previously proposed and denoted as Robotic Darwinian PSO (RDPSO). This paper contributes with a further extension of the RDPSO, thus integrating two research aspects: (i) an autonomous, realistic and fault-tolerant initial deployment strategy denoted as Extended Spiral of Theodorus (EST); and (ii) a fault-tolerant distributed search to prevent communication network splits. The exploring agents, denoted as scouts, are autonomously deployed using supporting agents, denoted as rangers. Experimental results with 15 physical scouts and 3 physical rangers show that the algorithm converges to the optimal solution faster and more accurately using the EST approach over the random deployment strategy. Also, a more fault-tolerant strategy clearly influences the time needed to converge to the final solution, but is less susceptible to robot failures.     

Reducing the Range of Perception in Multi-agent Patrolling Strategies

Multi-Agent Patrolling Problems consist in moving agents throughout a graph in order to optimize a collective performance metric. Some strategies from the literature tackle this problem by dispatching decentralized autonomous agents that coordinate themselves merely by sensing and writing information in the nodes. In this work, they are called k-range local strategies, were k indicates the range, in number of edges, of the agents’ sensing capabilities. The 1-range strategies (where agents can sense up to its neighbor nodes) are certainly the most common case in the literature. And only few 0-range strategies (where agents can only sense its current node) were found, although this type of strategy has the advantage of requiring simpler hardware, when applied in the design of real robots. In this work, we propose two higher-level procedures to reduce the perception range of 1-range strategies to 0: the Zr Method and the EZr Method. Applying both methods in 1-range strategies found in the literature, we created twenty new 0-range strategies, which were evaluated in a simulation experiment described and analyzed here. We also developed a prototype of a low-cost patrolling robot that is able to run the 0-range strategies proposed in this work.     

Swarm robotics search & rescue: A novel artificial intelligence-inspired optimization approach

In this paper, a novel heuristic algorithm is proposed to solve continuous non-linear optimization problems. The presented algorithm is a collective global search inspired by the swarm artificial intelligent of coordinated robots. Cooperative recognition and sensing by a swarm of mobile robots have been fundamental inspirations for development of Swarm Robotics Search & Rescue (SRSR). Swarm robotics is an approach with the aim of coordinating multi-robot systems which consist of numbers of mostly uniform simple physical robots. The ultimate aim is to emerge an eligible cooperative behavior either from interactions of autonomous robots with the environment or their mutual interactions between each other. In this algorithm, robots which represent initial solutions in SRSR terminology have a sense of environment to detect victim in a search & rescue mission at a disaster site. In fact, victim’s location refers to global best solution in SRSR algorithm. The individual with the highest rank in the swarm is called master and remaining robots will play role of slaves. However, this leadership and master position can be transitioned from one robot to another one during mission. Having the supervision of master robot accompanied with abilities of slave robots for sensing the environment, this collaborative search assists the swarm to rapidly find the location of victim and subsequently a successful mission. In order to validate effectiveness and optimality of proposed algorithm, it has been applied on several standard benchmark functions and a practical electric power system problem in several real size cases. Finally, simulation results have been compared with those of some well-known algorithms. Comparison of results demonstrates superiority of presented algorithm in terms of quality solutions and convergence speed.     

From Trajectory Tracking Control to Leader–Follower Formation Control

Abstract

This work investigates the leader–follower formation control of multiple nonholonomic mobile robots. First, the formation control problem is converted into a trajectory tracking problem and a tracking controller based on the dynamic feedback linearization technique drives each follower robot toward its corresponding reference trajectory in order to achieve the formation. The desired orientation for each follower is selected such that the nonholonomic constraint of the robot is respected, and thus the tracking of the reference trajectory for each follower is feasible. An adaptive dynamic controller that considers the actuators dynamics in the design procedure is proposed. The dynamic model of the robots includes the actuators dynamics in order to obtain the velocities as control inputs instead of torques or voltages. Using Lyapunov control theory, the tracking errors are proven to be asymptotically stable and the formation is achieved despite the uncertainty of the dynamic model parameters. In order to assess the proposed control laws, a ROS-framework is developed to conduct real experiments using four ROS-enabled mobile robots TURTLEBOTs. Moreover, the leader fault problem, which is considered as the main drawback of the leader–follower approach, is solved under ROS. An experiment is conducted where in order to overcome this problem, the desired formation and the leader role are modified dynamically during the experiment.     

Probabilistic network formation through coverage and freeze-tag

We address the problem of propagating a piece of information among robots scattered in an environment. Initially, a single robot has the information. This robot searches for other robots to pass it along. When a robot is discovered, it can participate in the process by searching for other robots. Since our motivation for studying this problem is to form an ad hoc network, we call it the Network Formation Problem. In this paper, we study the case where the environment is a rectangle and the robots’ locations are unknown but chosen uniformly at random. We present an efficient network formation algorithm, Stripes, and show that its expected performance is within a logarithmic factor of the optimal performance. We also compare Stripes with an intuitive network formation algorithm in simulations. The feasibility of Stripes is demonstrated with a proof-of-concept implementation.     

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.     

Cartesian control of redundant robots

This article presents a Cartesian-space position/force controller for redundant robots. The proposed control structure partitions the control problem into a nonredundant position/force trajectory tracking problem and a redundant mapping problem between Cartesian control input F ? R ^m and robot actuator torque T ? R ⁿ(for redundant robots, m < n). The underdetermined nature of the F → T map is exploited so that the robot redundancy is utilized to improve the dynamic response of the robot. This dynamically optimal F → T map is implemented locally (in time) so that it is computationally efficient for on-line control; however, it is shown that the map possesses globally optimal characteristics. Additionally, it is demonstrated that the dynamically optimal F→T map can be modified so that the robot redundancy is used to simultaneously improve the dynamic response and realize any specified kinematic performance objective (e.g., manipulability maximization or obstacle avoidance). Computer simulation results are given for a four degree of freedom planar redundant robot under Cartesian control, and demonstrate that position/force trajectory tracking and effective redundancy utilization can be achieved simultaneously with the proposed controller.     

Towards temporal verification of swarm robotic systems

A robot swarm is a collection of simple robots designed to work together to carry out some task. Such swarms rely on the simplicity of the individual robots; the fault tolerance inherent in having a large population of identical robots; and the self-organised behaviour of the swarm as a whole. Although robot swarms present an attractive solution to demanding real-world applications, designing individual control algorithms that can guarantee the required global behaviour is a difficult problem. In this paper we assess and apply the use of formal verification techniques for analysing the emergent behaviours of robotic swarms. These techniques, based on the automated analysis of systems using temporal logics, allow us to analyse whether all possible behaviours within the robot swarm conform to some required specification. In particular, we apply model-checking, an automated and exhaustive algorithmic technique, to check whether temporal properties are satisfied on all the possible behaviours of the system. We target a particular swarm control algorithm that has been tested in real robotic swarms, and show how automated temporal analysis can help to refine and analyse such an algorithm.     

Integrating human observer inferences into robot motion planning

Our goal is to enable robots to produce motion that is suitable for human–robot collaboration and co-existence. Most motion in robotics is purely functional, ideal when the robot is performing a task in isolation. In collaboration, however, the robot’s motion has an observer, watching and interpreting the motion. In this work, we move beyond functional motion, and introduce the notion of an observer into motion planning, so that robots can generate motion that is mindful of how it will be interpreted by a human collaborator. We formalize predictability and legibility as properties of motion that naturally arise from the inferences in opposing directions that the observer makes, drawing on action interpretation theory in psychology. We propose models for these inferences based on the principle of rational action, and derive constrained functional trajectory optimization techniques for planning motion that is predictable or legible. Finally, we present experiments that test our work on novice users, and discuss the remaining challenges in enabling robots to generate such motion online in complex situations.     

Robotic clusters: Multi-robot systems as computer clusters: A topological map merging demonstration

In most multi-robot systems, an individual robot is not capable of solving computationally hard problems due to lack of high processing power. This paper introduces the novel concept of robotic clusters to empower these systems in their problem solving. A robotic cluster is a group of individual robots which are able to share their processing resources, therefore, the robots can solve difficult problems by using the processing units of other robots. The concept, requirements, characteristics and architecture of robotic clusters are explained and then the problem of “topological map merging” is considered as a case study to describe the details of the presented idea and to evaluate its functionality. Additionally, a new parallel algorithm for solving this problem is developed. The experimental results proved that the robotic clusters remarkably speedup computations in multi-robot systems. The proposed mechanism can be used in many other robotic applications and has the potential to increase the performance of multi-robot systems especially for solving problems that need high processing resources.