Multiple Autonomous Robots Research Articles

Hybrid planning for challenging construction problems: An Answer Set Programming approach

We study construction problems where multiple robots rearrange stacks of prefabricated blocks to build stable structures. These problems are challenging due to ramifications of actions, true concurrency, and requirements of supportedness of blocks by a surface or a robot and stability of the overall structure at all times. We propose a general elaboration tolerant method to solve a wide range of construction problems, based on the knowledge representation and reasoning paradigm of Answer Set Programming. This method not only (i) determines a stable final configuration of the structure, but also (ii) computes the order of manipulation tasks for multiple autonomous robots to build the structure from an initial configuration, (iii) while simultaneously ensuring the requirements of supportedness and stability at all times. We prove the soundness and completeness of our method with respect to these properties. We introduce a set of challenging construction benchmark instances, including construction of (uneven) bridges and overhangs, and discuss the usefulness of our framework over these instances. Furthermore, we perform experiments to investigate the computational performance of our hybrid method, and demonstrate the applicability of our method using a bimanual Baxter robot.

Non-intersecting curved paths using Bezier curves in the 2D-Euclidean plane for multiple autonomous robots

Non-intersecting curved paths using Bezier curves in the 2D-Euclidean plane for multiple autonomous robots

Cooperative Following of Multiple Autonomous Robots Based on Consensus Estimation

When performing a specific task, a Multi-Agent System (MAS) not only needs to coordinate the whole formation but also needs to coordinate the dynamic relationship among all the agents, which means judging and adjusting their positions in the formation according to their location, velocity, surrounding obstacles and other information to accomplish specific tasks. This paper devises an integral separation feedback method for a single-agent control with a developed robot motion model; then, an enhanced strategy incorporating the dynamic information of the leader robot is proposed for further improvement. On this basis, a method of combining second-order formation control with path planning is proposed for multiple-agents following control, which uses the system dynamic of one agent and the Laplacian matrix to generate the consensus protocol. Due to a second-order consensus, the agents exchange information according to a pre-specified communication digraph and keep in a certain following formation. Moreover, an improved path planning method using an artificial potential field is developed to guide the MAS to reach the destination and avoid collisions. The effectiveness of the proposed approach is verified with simulation results in different scenarios.

Aerial additive manufacturing with multiple autonomous robots.

Additive manufacturing methods1-4 using static and mobile robots are being developed for both on-site construction5-8 and off-site prefabrication9,10. Here we introduce a method of additive manufacturing, referred to as aerial additive manufacturing (Aerial-AM), that utilizes a team of aerial robots inspired by natural builders11 such as wasps who use collective building methods12,13. We present a scalable multi-robot three-dimensional (3D) printing and path-planning framework that enables robot tasks and population size to be adapted to variations in print geometry throughout a building mission. The multi-robot manufacturing framework allows for autonomous three-dimensional printing under human supervision, real-time assessment of printed geometry and robot behavioural adaptation. To validate autonomous Aerial-AM based on the framework, we develop BuilDrones for depositing materials during flight and ScanDrones for measuring the print quality, and integrate a generic real-time model-predictive-control scheme with the Aerial-AM robots. In addition, we integrate a dynamically self-aligning delta manipulator with the BuilDrone to further improve the manufacturing accuracy to five millimetres for printing geometry with precise trajectory requirements, and develop four cementitious-polymeric composite mixtures suitable for continuous material deposition. We demonstrate proof-of-concept prints including a cylinder 2.05 metres high consisting of 72 layers of a rapid-curing insulation foam material and a cylinder 0.18 metres high consisting of 28 layers of structural pseudoplastic cementitious material, a light-trail virtual print of a dome-like geometry, and multi-robot simulations. Aerial-AM allows manufacturing in-flight and offers future possibilities for building in unbounded, at-height or hard-to-access locations.

Indoor Visual Exploration with Multi-Rotor Aerial Robotic Vehicles.

In this work, we develop a reactive algorithm for autonomous exploration of indoor, unknown environments for multiple autonomous multi-rotor robots. The novelty of our approach rests on a two-level control architecture comprised of an Artificial-Harmonic Potential Field (AHPF) for navigation and a low-level tracking controller. Owing to the AHPF properties, the field is provably safe while guaranteeing workspace exploration. At the same time, the low-level controller ensures safe tracking of the field through velocity commands to the drone’s attitude controller, which handles the challenging non-linear dynamics. This architecture leads to a robust framework for autonomous exploration, which is extended to a multi-agent approach for collaborative navigation. The integration of approximate techniques for AHPF acquisition further improves the computational complexity of the proposed solution. The control scheme and the technical results are validated through high-fidelity simulations, where all aspects, from sensing and dynamics to control, are incorporated, demonstrating the capacity of our method in successfully tackling the multi-agent exploration task.

Dependable Navigation for Multiple Autonomous Robots with Petri Nets Based Congestion Control and Dynamic Obstacle Avoidance

In this paper, a novel path planning algorithm for multiple robots using congestion analysis and control is presented. The algorithm ensures a safe path planning solution by avoiding collisions among robots as well as among robots and humans. For each robot, alternative paths to the goal are realised. By analysing the travelling time of robots on different paths using Petri Nets, the optimal configuration of paths is selected. The prime objective is to avoid congestion when routing many robots into a narrow area. The movements of robots are controlled at every intersection by organising a one-by-one passing of the robots. Controls are available for the robots which are able to communicate and share information with each other. To avoid collision with humans and other moving objects (i.e. robots), a dipole field integrated with a dynamic window approach is developed. By considering the velocity and direction of the dynamic obstacles as sources of a virtual magnetic dipole moment, the dipole-dipole interaction between different moving objects will generate repulsive forces proportional to the velocity to prevent collisions. The whole system is presented on the widely used platform Robot Operating System (ROS) so that its implementation is extendable to real robots. Analysis and experiments are demonstrated with extensive simulations to evaluate the effectiveness of the proposed approach.

Local and Global Path Planning for Autonomous Mobile Robots Using Hierarchized Maps

We are currently facing a “labor crisis,” particularly in the field of logistics, because of reductions in the labor force. Therefore, industries must make their logistics more efficient by utilizing autonomous mobile robotics technologies. This paper proposes a hierarchized map concept that makes unmanned delivery tasks which use multiple autonomous robots more efficiently. Using our proposed concept, an autonomous mobile robot can move automatically on a more efficient path than using current methods. In addition, the management platform for autonomous robots can be used to prevent accidents such as collisions or deadlocks between autonomous robots.

Information-Based Control of Robots in Search-and-Rescue Missions With Human Prior Knowledge

Multirobot systems provide a scalable and robust solution for monitoring tasks. In time-intensive missions such as search and rescue, the inclusion of a human has the potential advantage of incorporating prior knowledge about the target location or dynamics. In this article, we develop a general information-theoretic framework to control multiple autonomous robots in search and rescue missions that include a human teleoperator. Human prior knowledge is modeled to capture the target location and dynamics, and mutual-information-based control is formulated to let autonomous robots weigh between two strategies: independent search or assisting the human by staying in proximity. The control actions optimize a weighted sum of normalized mutual information calculated using particle-filtered estimates of the target and the reference robot. We implement the framework to simulate two widely different scenarios designed after search-and-rescue missions from literature, and incorporate varying levels of accuracy in human prior knowledge. Our results indicate that the mission performance depends on how robots weigh between the two strategies, with the amount of the optimal control effort shared between strategies affected by prior knowledge and the number of robots. Comparison with existing strategies points to the benefits of an information-based control in situations where human prior knowledge is inaccurate. The proposed information-theoretic abstraction of the human–robot interaction can be implemented on a wide variety of scenarios and the results highlight the role of human prior knowledge toward effective robotic assistance in time-intensive missions.

Non-intersecting curved paths using Bezier curves in the 2D-Euclidean plane for multiple autonomous robots

Non-intersecting curved paths using Bezier curves in the 2D-Euclidean plane for multiple autonomous robots

Technology Focus: Intelligent Operations (May 2021)

The last year has seen people in many sectors unexpectedly confronting a new challenge—working remotely. Even before this, our industry has been trying to operate fields remotely (either partially or fully) and make operations smarter and more automated. Key drivers are to improve safety in operations, maximize production, and make operations more efficient. These efforts have been enabled by the rapidly changing technology landscape—in sophisticated acquisition and analysis of data and increased connectivity (from both fiber-optic and cellular networks). It also has been accelerated by the push across the industry to digitalize. We now acquire, process, and analyze much more detailed operations data and use the analysis to actively control wells and operations. This feature highlights recently presented papers that cover the following topics. How Digital Transformation Has Progressed. Paper OTC 30794 discusses similar efforts in other sectors, including marine/ship building and auto manufacturing. Paper SPE 200728 discusses use of a digital twin to improve operational efficiency in a mature brownfield setting (Brage Field in the Norwegian North Sea). Paper OTC 30488 describes extensible and scalable remote monitoring and control using a digital decision assistant. How Technology Has Enabled Data Acquisition and Analysis From Relatively New Sources [e.g., Distributed Temperature Sensing (DTS) or Distributed Acoustic Sensing]. Paper SPE 200826 describes seven DTS applications from around the world that monitor well integrity, stimulation, and injection profiles and identify gas, water, or sand production. Paper OTC 30442 and other papers from the Bokor field in Malaysia describe DTS data from fiber-optic cable behind casing in wells with smart completions. Papers IPTC 19574 and SPE 202349 show how pressure telemetry can enable wireless control of completions. The Path to Fully Remotely Operated Fields. Paper SPE 203461 discusses the design and execution of digitalization and remote operations in a new development area with high hydrogen sulfide (the Mender satellite field in the UAE). Paper SPE 202667 describes the applications for multiple autonomous robots controlled remotely. Digital transformation of work flows and operations clearly is happening across the industry and adding significant value. The next frontier on the digital transformation and Industry 4.0 journey might be to achieve step-change increases in oil and gas recovery factors. Recommended additional reading at OnePetro: www.onepetro.org. SPE 200728 - The Digitalization Journey of the Brage Digital Twin by Peter Kronberger, Wintershall, et al. OTC 30442 - Innovative Solution for IWAG Injection Monitoring Using Fiber-Optic Cable Cemented Behind Casing in an Intelligent Well: A First in Malaysia by Nur Faizah P. Mosar, Schlumberger, et al. SPE 202667 - Operations Room: A Key Component for Upscaling Robotic Solutions on Site by Jean-Michel Munoz, Total, et al. OTC 30488 - Machine-Learning-Enabled Digital Decision Assistant for Remote Operations by Vitor Alves da Cruz Mazzi, Intelie, et al. IPTC 19574 - Research and Application of Downhole Remote Wireless Control Technology Based on Gas Pressure Wave in Tubing by Mingge He, China National Petroleum Corporation, et al. SPE 202349 - Pressure Wave Downhole Communication Technique for Smart Zonal Water Injection by Quanbin Wang, China National Petroleum Corporation, et al.

Multirobot coordination with deep reinforcement learning in complex environments

In the multiple autonomous robot system, it is very important to complete path planning coordinately and effectively in the processes of interference avoidance, resource allocation and information sharing. In traditional multirobot coordination algorithms, most of the solutions are in known environments, the target position that each robot needs to move to and the robot priority are set, which limits the autonomy of the robot. Only using visual information to solve the problem of multirobot coordination is still less. This paper proposes a multi-robot cooperative algorithm based on deep reinforcement learning to make the robot more autonomous in the process of selecting target positions and moving. We use the end-to-end approach, using only the top view, that is, a robot-centered top view, and the first-person view, that is, the image information collected from the first-person perspective of the robot, as input. The proposed algorithm, which includes a dueling neural network structure, can solve task allocation and path planning; we call the algorithm TFDueling. Through its perception and understanding of the environment, the robot can reach the target position without collision, and the robot can move to any target position. We compare the proposed algorithm, TFDueling, with different input structure algorithms, TDueling and FDueling, and with different neural network structures, TFDQN and TFDDQN. Experiments show that the proposed TFDueling algorithm has the highest accuracy and robustness.

RoVaLL: Design and Development of a Multi-Terrain Towed Robot With Variable Lug-Length Wheels

Robotic systems play a very important role in exploration, allowing us to reach places that would otherwise be unsafe or unreachable to humans, such as volcanic areas, disaster sites or unknown areas in other planets. As the area to be explored increases, so does the time it takes for robots to explore it. One approach to reduce the required time is using multiple autonomous robots to perform distributed exploration. However, this significantly increases the associated cost and the complexity of the exploration process. To address these issues, in the past we proposed a leader-follower architecture where multiple two-wheeled passive robots capable of steering only using brakes are pulled by a leader robot. By controlling their relative angle with respect to the leader, the followers could move in arbitrary formations. The proposed follower robots used rubber tires, which allowed it to perform well in rigid ground, but poorly in soft soil. One alternative is to use lugged wheels, which increase the traction in soft soils. In this letter we propose a robot with shape-shifting wheels that allow it to steer in both rigid and soft soils. The wheels use a cam mechanism to push out and retract lugs stored on its inside. The shape of the wheel can be manipulated by controlling the driving torque exerted on the cam mechanism. Through experiments we verified that the developed mechanism allowed the follower robots to control their relative angle with respect to the leader in both rigid and soft soils.

Multi-robot information driven path planning under communication constraints

We consider the problem of simultaneous exploration and information collection in an initially unknown environment by multiple autonomous robots when the communication between robots is unreliable and intermittent. We propose a novel algorithm where decisions to select locations for exploration and information collection are guided by a utility function that combines Gaussian Process-based distributions for information entropy and communication signal strength, along with a distributed coordination protocol to avoid path conflicts between robots and repeated exploration and information collection from the same region by different robots. Our proposed algorithm was experimentally validated in simulation and on hardware Clearpath Jackal robots while using a realistic signal loss model from the literature. Our results show that our approach plans it samples such that it receives up to 25 dBm more signal strength throughout navigation compared to approaches that do not consider communications when selecting locations to sample from while accomplishing similar levels of model error.

Stable emergent formations for a swarm of autonomous car-like vehicles

A biological swarm is an ideal multi-agent system that collectively self-organizes into bounded, if not stable, formations. A mathematical model, developed appropriately from some principle of swarming, should enable one, therefore, to study formation strategies for multiple autonomous robots. In this article, based on the hypothesis that swarming is an interplay between long-range attraction and short-range repulsion between the individuals in the swarm, a planar individual-based or Lagrangian swarm model is constructed using the Direct Method of Lyapunov. While attraction ensures the swarm is cohesive, meaning that the individuals in the swarm remain close to each other at all times, repulsion ensures that the swarm is well-spaced, meaning that no two individuals in the swarm occupy the same space at the same time. Via a novel Lyapunov-like function with attractive and repulsive components, the article establishes the global existence, uniqueness, and boundedness of solutions about the centroid. This paves the way to prove that the swarm model, governed by a system of first-order ordinary differential equations (ODEs), is cohesive and well-spaced. The article goes on to show that the artificial swarm can collectively self-organize into two stable formations: (i) a constant arrangement about the centroid when the system has equilibrium points, and (ii) a highly parallel formation when the system does not have equilibrium points. Computer simulations not only illustrate these but also reveal other emergent patterns such as swirling structures and random-like walks. As an application, we tailor the model accordingly and propose new autonomous steering laws giving rise to pattern-forming for multiple nonholonomic car-like vehicles.

New advances in multiple autonomous aerial robots formation control technology

New advances in multiple autonomous aerial robots formation control technology

Multiple Autonomous Robots Coordination and Navigation

Multiple Autonomous Robots Coordination and Navigation

Cooperation of multiple autonomous robots and analysis of their swarm behavior

In this paper, we extended previous studies of cooperating autonomous robots to include situations when environmental changes and changes in the number of robots in the swarm can affect the efficiency to execute tasks assigned to the swarm of robots. We have presented a novel approach based on partition of the robot behaviour. The sub-diagrams describing sub-routs allowed us to model advanced interactions between autonomous robots using limited number of state combinations avoiding combinatorial explosion of reachability. We identified the systems for which we can ensure the correctness of robots interactions. New techniques were presented to verify and analyze combined robots’ behaviour. The partitioned diagrams allowed us to model advanced interactions between autonomous robots and detect irregularities such as deadlocks, lack of termination etc. The techniques were presented to verify and analyze combined robots’ behaviour using model checking approach. The described system, Dedan verifier, is still under development. In the near future, timed and probabilistic verification are planned..

Cold standby renewal process integrated with environmental factor effects for reliability evaluation of multiple autonomous robot system

PurposeThe purpose of this paper is to estimate the required number of robots consisting of some non-repairable components, by employing a renewal model. Considering the importance of the availability of standby autonomous robots for reducing and preventing down-times of advanced production systems, which imposes a considerable loss, the present research tries to introduce a practical model for the determination of the required number of autonomous robots.Design/methodology/approachMost of the available research on the estimation of the required standby components based on the reliability characteristics of components has not considered the environmental factors influencing the reliability characteristics. Therefore, such estimations are not accurate enough. In contrast, this paper focuses on the influence of the environmental and human factors (e.g. the operators’ skill) on the robot reliability characteristics.FindingsA model based on the Weibull renewal process combined with the cold standby strategy is developed for reliability evaluation of the system. The effectiveness of the proposed integrated reliability evaluation model is worked out in some cases.Originality/valueDetermining a required number of robots is an important issue in availability and utilization of a complex robotic production system. In an advanced production system, while the estimation process of a required number of robots can be performed through different approaches, one of the realistic estimation methods is based on the system’s reliability that takes into consideration the system operating environment. To forecast the required number of robots for an existing production system, in some cases, the assumption of a constant failure rate does not differ much from the assumption of a non-constant failure rate and can be made with an acceptable error.

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.

Establishing an emergency communication network and optimal path using multiple autonomous rover robots

SummaryThe natural calamity or disaster may destroy all communication networks especially a cellular network that relies on a tower. Although many solutions to an ad hoc wireless network have been proposed, forming a network covering a respective region with mobile robots toward optimal coverage remains to be an open problem. In this paper, we take the initiative to handle the optimal network coverage and path selection in disaster region with the help of multiple movable/rover robots. This paper consists of load balance distribution algorithm and optimal coverage algorithm applied to find the next optimally possible node location for all robots. Next, the robots maneuvering in an unknown disaster environment to identify the optimal path between the source and destination by using a particle swarm optimization algorithm. Finally, simulated results show that the algorithms can significantly improve the network coverage in the entire region, and the optimal path can effectively identify the optimal solution for all rover robots.