Coordination Of Multiple Robots Research Articles

Motion Coordination of Multiple Autonomous Mobile Robots under Hard and Soft Constraints

This paper presents a distributed approach to the motion control problem for a platoon of unicycle robots moving through an unknown environment filled with static obstacles under multiple hard and soft operational constraints. Each robot has an onboard camera to determine its relative position in relation to its predecessor and proximity sensors to detect and avoid nearby obstascles. Moreover, no robot apart from the leader can independently localize itself within the given workspace. To overcome this limitation, we propose a novel distributed control protocol for each robot of the fleet, utilizing the Adaptive Performance Control (APC) methodology. By utilizing the APC approach to address input constraints via the on-line modification of the error specifications, we ensure that each follower effectively tracks its predecessor without encountering collisions with obstacles, while simultaneously maintaining visual contact with its preceding robot, thus ensuring the inter-robot visual connectivity. Finally, extensive simulation results are presented to demonstrate the effectiveness of the presented control system along with a real-time experiment conducted on an actual robotic system to validate the feasibility of the proposed approach in real-world scenarios.

Approximate optimal and safe coordination of nonlinear second-order multirobot systems with model uncertainties

This paper investigates the approximate optimal coordination for nonlinear uncertain second-order multi-robot systems with guaranteed safety (collision avoidance) Through constructing novel local error signals, the collision-free control objective is formulated into an coordination optimization problem for nominal multi-robot systems. Based on approximate dynamic programming technique, the optimal value functions and control policies are learned by simplified critic-only neural networks (NNs). Then, the approximated optimal controllers are redesigned using adaptive law to handle the effects of robots’ uncertain dynamics. It is shown that the NN weights estimation errors are uniformly ultimately bounded under proper conditions, and safe coordination of multiple robots can be achieved regardless of model uncertainties. Numerical simulations finally illustrate the effectiveness of the proposed controller.

Strategies for a scalable multi-robot large scale wire arc additive manufacturing system

Conventional robotic wire arc additive manufacturing technologies enable the rapid production of moderate-sized components using low-cost wire feedstocks and robotic welding systems. Efforts to date have primarily focused on single robot solutions. However, new configurations are possible with coordination of multiple robots and multi-degree of freedom positioners. This paper describes a new multi-agent control paradigm that enables multiple robots to work collaboratively on manufacturing a single component on a rotating platform. The advantages of this approach are increased deposition rate and productivity. This paper demonstrates this control strategy on a 19 degrees-of-freedom platform based on three wire arc additive systems surrounding a single rotating platform.

A Review of Swarm Robotics in a NutShell

A swarm of robots is the coordination of multiple robots that can perform a collective task and solve a problem more efficiently than a single robot. Over the last decade, this area of research has received significant interest from scientists due to its large field of applications in military or civil, including area exploration, target search and rescue, security and surveillance, agriculture, air defense, area coverage and real-time monitoring, providing wireless services, and delivery of goods. This research domain of collective behaviour draws inspiration from self-organizing systems in nature, such as honey bees, fish schools, social insects, bird flocks, and other social animals. By replicating the same set of interaction rules observed in these natural swarm systems, robot swarms can be created. The deployment of robot swarm or group of intelligent robots in a real-world scenario that can collectively perform a task or solve a problem is still a substantial research challenge. Swarm robots are differentiated from multi-agent robots by specific qualifying criteria, including the presence of at least three agents and the sharing of relative information such as altitude, position, and velocity among all agents. Each agent should be intelligent and follow the same set of interaction rules over the whole network. Also, the system’s stability should not be affected by leaving or disconnecting an agent from a swarm. This survey illustrates swarm systems’ basics and draws some projections from its history to its future. It discusses the important features of swarm robots, simulators, real-world applications, and future ideas.

Distributed and Time-Delayed k-Winner-Take-All Network for Competitive Coordination of Multiple Robots.

In this article, a distributed and time-delayed k-winner-take-all (DT-kWTA) network is established and analyzed for competitively coordinated task assignment of a multirobot system. It is considered and designed from the following three aspects. First, a network is built based on a k-winner-take-all (kWTA) competitive algorithm that selects k maximum values from the inputs. Second, a distributed control strategy is used to improve the network in terms of communication load and computational burden. Third, the time-delayed problem prevalent in arbitrary causal systems (especially, in networks) is taken into account in the proposed network. This work combines distributed kWTA competition network with time delay for the first time, thus enabling it to better handle realistic applications than previous work. In addition, it theoretically derives the maximum delay allowed by the network and proves the convergence and robustness of the network. The results are applied to a multirobot system to conduct its robots' competitive coordination to complete the given task.

Multiple Mobile Robots Coordination in Shared Workspace for Task Makespan Minimization

In this paper we consider a system of multiple mobile robots (MMRS) and the process of their concurrent motion in a shared two-dimensional workspace. The goal is to plan the robot movement along given fixed paths so as to minimize the completion time of all the robots while ensuring that they never collide. Thus, the considered problem combines the problems of robot schedule optimization with collision and deadlock avoidance. The problem formulation is presented and its equivalent reformulation that does not depend explicitly on the geometry of the robot paths is proposed. An event-based solution representation is proposed, allowing for a discrete optimization approach. Two types of possible deadlocks are identified and deadlock avoidance procedures are discussed. We proposed two types of solving methods. First, we implemented two metaheuristics: the local-search-based taboo search as well as the population-based artificial bee colony. Next, we implemented 14 simple constructive algorithms, employing dispatch rules such as first-in first-out, shortest distance remaining first, and longest distance remaining first, among others. A set of problem instances for different numbers of robots is created and provided as a benchmark. The effectiveness of the solving methods is then evaluated by simulation using the generated instances. Both deterministic and lognormal-distributed uncertain robot travel times are considered. The results prove that the taboo search metaheuristic obtained the best results for both deterministic and uncertain cases, with only artificial bee colony and a few constructive algorithms managing to remain competitive. Detailed results as well as ideas to further improve proposed methods are discussed.

Effectiveness of Swarm Robotics in Coordination of Multiple Robots as a System That Consists of Large Numbers of Mostly Simple Physical Robots

The concurrent work in a task with multiple robots is gaining faster achievement with different tasks. Swarming is able to ensure a new way of collaboration which is helping to provide diverse skill growth in the environment. The combined goal and also development of different new skills are gained from swarm robotics. Inspiration for swarm robotics is gained with social insects as they are able to exchange information with each other and modify their behaviors according to the environment. The formation of the swarm is developed with the help of multiple robots as these can build progress in work. Moving in the same direction as their neighbours are the most simple rule that is followed by swarm robotics which can be ensured a better improvement compared with single robots. Multiple robots are helping to produce better improvements in different sectors such as agriculture, military operation, rescue operation, and much more. Different designs of machines are helping to assist humans to solve different real-life issues of the people. With the help of multiple robotic functions, safety and speed are ensured which can also solve any hard issues for people. Robotics are helping to provide errorless development which is important for gaining growth and assurance for improving the quality of humans. Keywords : Autonomous mobile robots, Swarm robotic functions, Swarm intelligence theory.

AR-assisted digital twin-enabled robot collaborative manufacturing system with human-in-the-loop

The teleoperation and coordination of multiple industrial robots play an important role in today’s industrial internet-based collaborative manufacturing systems. The user-friendly teleoperation approach allows operators from different manufacturing domains to reduce redundant learning costs and intuitively control the robot in advance. Nevertheless, only a few preliminary works have been introduced very recently, let alone its effective implementation in the manufacturing scenarios. To address the gap, this research proposes a novel multi-robot collaborative manufacturing system with human-in-the-loop control by leveraging the cutting-edge augmented reality (AR) and digital twin (DT) techniques. In the proposed system, the DTs of industrial robots are firstly mapped to physical robots and visualize them in the AR glasses. Meanwhile, a multi-robot communication mechanism is designed and implemented, to synchronize the state of robots in the twin. Moreover, a reinforcement learning algorithm is integrated into the robot motion planning to replace the conventional kinematics-based robot movement with corresponding target positions. Finally, three interactive AR-assisted DT modes, including real-time motion control, planned motion control, and robot monitoring mode are generated, which can be readily switched by human operators. Two experimental studies are conducted on (1) a single robot with a commonly used peg-in-hole experiment, and (2) the motion planning of multi-robot collaborative tasks, respectively. From the experimental results, it can be found that the proposed system can well handle the multi-robot teleoperation tasks with high efficiency and owns great potentials to be adopted in other complicated manufacturing scenarios in the near future.

Game-Theoretic Strategic Coordination and Navigation of Multiple Wheeled Robots

Multiple robots negotiating in a dynamic workspace may lead to collisions. To avoid such issues, multi-robot navigation and coordination becomes necessary but is computationally very challenging, particularly when there are many robots. This article addresses the problem of multi-robot navigation where individual robots require coordination. Although a few such attempts for modeling multi-robot coordination and navigation have been studied, this work proposes a game-theoretic coordination strategy, also referred to as strategic coordination. We make use of a genetic algorithm tuned fuzzy logic–based motion planner. The proposed strategic coordination strategy has been pitted against a basic potential field-based motion planner, also referred to as the heuristic method, for performance comparison. Results are compared through computer simulation with 8 to 17 robots at different rounds. From the obtained results, it was observed that the proposed coordination scheme’s efficacy is strong for a larger number of robots. In addition, the proposed strategic coordination scheme with the genetic-fuzzy-based motion planner was found to outperform other combinations as far as the quality of solutions and time to reach the goal positions. The computational complexity of different methods has also been compared and presented.

COSEM: Collaborative Semantic Map Matching Framework for Autonomous Robots

Relative localization is a fundamental requirement for the coordination of multiple robots. To date, existing research in relative localization mainly depends on the extraction of low-level geometry features such as planes, lines, and points, which may fail in challenging cases when the initial error is large and the overlapping area is low. In this article, a novel approach named collaborative semantic map matching (COSEM) is proposed to estimate the relative transformation between robots. COSEM jointly performs multimodal information fusion, semantic data association, and optimization in a unified framework. First, each robot applies a multimodal information fusion model to generate local semantic maps. Since the correspondences between local maps are latent variables, a flexible semantic data association strategy is proposed using expectation-maximization. Instead of assigning hard geometry data association, semantic association and geometry association are jointly estimated. Then, the minimization of the expected cost results in a rigid transformation matrix between two semantic maps. Evaluations on semantic KITTI benchmarks and real-world experiments show the improved accuracy, convergence, and robustness.

An indoor and outdoor errand-behavior system based on the coordination of multiple robots

An indoor and outdoor errand-behavior system based on the coordination of multiple robots

Safe coordination of robots in cyclic paths

This paper presents a MILP (mixed integer linear programming) based formulation for the coordination of multiple robots. We consider robots that must follow closed intersecting paths persistently. We propose an off-line planning of velocity profiles preventing the need of online collision avoidance maneuvers or path replanning. Our robot model considers minimum and maximum speed constraints, which allows our strategy to be applied to fixed-wing aerial robots. We also deal with three important other issues which are usually disregarded in the related literature: minimum spatial separation; acceleration limits; and uncertainties on the speeds and positions. Simulations with up to 48 robots show the efficiency of our strategy. A real experiment with 3 actual e-puck robots is presented in order to demonstrate the robustness of our formulation in a real world scenario.

Multiple Autonomous Robots Coordination and Navigation

Multiple Autonomous Robots Coordination and Navigation

Secure Cooperative Event-Triggered Control of Linear Multiagent Systems Under DoS Attacks

This paper studies secure cooperative event-triggered control of linear multiagent systems under denial-of-service (DoS) attacks. The DoS attacks refer to interruptions of communication channels carried out by an intelligent adversary. We consider a class of time-sequence-based DoS attacks allowed to occur aperiodically. Then, an explicit analysis of the frequency and duration of DoS attacks is investigated for both secure leaderless and leader-following consensus problems. A resilient cooperative event-triggered control scheme is developed and scheduling of controller updating times is determined in the presence of DoS attacks. It is shown that based on the proposed distributed algorithms, all the agents can achieve secure consensus exponentially. The effectiveness of the developed methods is illustrated through three case studies: 1) multiple robot coordination; 2) distributed voltage regulation of power networks; and 3) distributed cooperative control of unstable dynamic systems.

A Two-Stage Approach to Collaborative Fiber Placement through Coordination of Multiple Autonomous Industrial Robots

The use of multiple Autonomous Industrial Robots (AIRs) as opposed to a single AIR to perform fiber placement brings about many challenges which have not been addressed by researchers. These challenges include optimal division and allocation of the work and performing path planning in a coordinated manner while considering the requirements and constraints that are unique to the fiber placement task. To solve these challenges, a two-stage approach is proposed in this paper. The first stage considers multiple objectives to optimally allocate each AIR with surface areas, while the second stage aims to generate coordinated paths for the AIRs. Within each stage, mathematical models are developed with several unique objectives and constraints that are specific to the multi-AIR collaborative fiber placement. Several case studies are presented to validate the approach and the proposed mathematical models. Comparison studies with different number of AIRs and variations of the developed mathematical models are also presented.

Dynamics and Control of a Differential Drive Robot With Wheel Slip: Application to Coordination of Multiple Robots

In differential drive robots, wheel slip severely affects the ability to track a desired motion trajectory and the problem is exacerbated when differential drive robots are used in applications involving coordination of multiple robots. This problem is investigated and, based on the wheel–ground traction forces, a simple slip avoidance control strategy is discussed. Differential drive robots with two driven wheels and one or more ball-type caster wheels are considered. The traction forces between the wheels and the ground surface are determined by assuming rigid wheel, rigid ground interaction. These traction forces are used to determine the maximum value of the input wheel torque that can be applied on the wheel before it slips. To avoid wheel slip, this limiting torque value is used to set a saturation limit for the input torque computed by a trajectory tracking controller. Stability of the closed-loop system with the slip avoidance strategy is shown. Experiments are conducted with this strategy using a single robot as well as multiple robots in a platoon. A representative sample of the experimental results is presented and discussed.

An immune-inspired swarm aggregation algorithm for self-healing swarm robotic systems

Swarm robotics is concerned with the decentralised coordination of multiple robots having only limited communication and interaction abilities. Although fault tolerance and robustness to individual robot failures have often been used to justify the use of swarm robotic systems, recent studies have shown that swarm robotic systems are susceptible to certain types of failure. In this paper we propose an approach to self-healing swarm robotic systems and take inspiration from the process of granuloma formation, a process of containment and repair found in the immune system. We use a case study of a swarm performing team work where previous works have demonstrated that partially failed robots have the most detrimental effect on overall swarm behaviour. We have developed an immune inspired approach that permits the recovery from certain failure modes during operation of the swarm, overcoming issues that effect swarm behaviour associated with partially failed robots.

Coordinated Robot Navigation via Hierarchical Clustering

We introduce the use of hierarchical clustering for relaxed deterministic coordination and control of multiple robots. Traditionally, an unsupervised learning method, hierarchical clustering offers a formalism for identifying and representing spatially cohesive and segregated robot groups at different resolutions by relating the continuous space of configurations to the combinatorial space of trees. We formalize and exploit this relation, developing computationally effective reactive algorithms for navigating through the combinatorial space in concert with geometric realizations for a particular choice of the hierarchical clustering method. These constructions yield computationally effective vector field planners for both hierarchically invariant as well as transitional navigation in the configuration space. We apply these methods to the centralized coordination and control of $n$ perfectly sensed and actuated Euclidean spheres in a $d$ -dimensional ambient space (for arbitrary $n$ and $d$ ). Given a desired configuration supporting a desired hierarchy, we construct a hybrid controller that is quadratic in $n$ and algebraic in $d$ and prove that its execution brings all but a measure zero set of initial configurations to the desired goal, with the guarantee of no collisions along the way.

Distributed Receding Horizon Coverage Control for Multiple Mobile Robots

This paper presents a distributed coverage control scheme based on receding horizon control for the coordination of multiple mobile robots to cover an event. The mission space is modeled using a probability density function representing the probability of occurrence of events. The distributed scheme is generated by the decomposition of a single optimal coverage problem into distributed receding horizon coverage control problems, each of them associated with one robot. The distributed coverage scheme is proven to optimally stabilize robots at a centroidal Voronoi configuration, which is an optimal configuration to cover an event. The control scheme is tested in three simulation environments to illustrate its good performance at environments with any probability density function of events, as well as its ability to generalize to much larger groups of mobile robots.

Robust finite-time containment control for high-order multi-agent systems with matched uncertainties under directed communication graphs

ABSTRACTIn this paper, we study the robust finite-time containment control problem for a class of high-order uncertain nonlinear multi-agent systems modelled as high-order integrator systems with bounded matched uncertainties. When relative state information between neighbouring agents is available, an observer-based distributed controller is proposed for each follower using the sliding mode control technique which solves the finite-time containment control problem under general directed communication graphs. When only relative output information is available, robust exact differentiators and high-order sliding-mode controllers are employed together with the distributed finite-time observers. It is shown that robust finite-time containment control can still be achieved in this situation. An application in the coordination of multiple non-holonomic mobile robots is used as an example to illustrate the effectiveness of the proposed control strategies.