The smart cities that collaborate with AI-driven autonomous robots are attracting attention for supporting various social activities in the real world. In facilities that provide such services, various systems managing the facility and robots may coexist in the common area. By enabling the systems to interoperate and share information about the status of the facility and robots, it becomes possible to realize a variety of services that support safety and security within the facility. However, while the operators of the facility want to monitor the detailed conditions of the robots, the operators of the robots are cautious about providing the information about the status of robots such as the moving trajectory and various sensor data. To resolve this dilemma, we propose a new system that enables estimation of the operational conditions of the robots by verifying the positions and trajectories at landmarks in the facility without disclosing their internal information. In the proposed system, we focus on the observation of environmental information that accurately reflects the real-world situation for estimating the proximity between the robot and each landmark. As the environmental information, both systems on robots and a facility measure CSI (Channel State Information) and acoustic information. In addition, by utilizing zero-knowledge proof (ZKP) technology, the system for the facility confirms the reliability of the process for estimating the proximity of the robots to the landmark without exchanging detailed internal information. Through the proof-of-concept experiment, applying the proposed system achieved high-accuracy proximity detection with both methods (CSI and acoustic information) yielding precision and recall rates exceeding 0.90.
Navigation in unstructured, GPS-denied environments, such as forests and agricultural fields, poses persistent challenges for heterogeneous robotic teams. While visual homing and Wide Area Visual Navigation (WAVN) enable lightweight, map-free operation, their effectiveness in large-scale, decentralized settings can be constrained by the absence of a coordination mechanism that accounts for varying reliability across robots. This article examines the innovative combination of blockchain techniques with WAVN to tackle visual navigation issues in diverse mobile robots used in unstructured sectors like agriculture and forestry. It addresses GPS reliance, adapts to environmental shifts, and reduces computational burdens by integrating RoboStake, a novel blockchain Proof-of-Stake (PoS) mechanism, into the WAVN system. This solution seeks to bolster cooperative navigation by assessing the reliability of each robotâs navigational input. With methods including a stake weight function, a PoS consensus score, and a navigability function, this strategy confronts the computational hurdles of coordinating robots and verifying data. Lastly, we showcase how the proposed approach upholds critical navigability features of the WAVN system and present results from scalable simulation experiments to highlight the improved efficiency achieved through enhanced cooperation.
The separation of the control plane from the data plane in a blockchain-enabled software-defined network (SDN) has emerged as a promising solution in the industry for optimising network traffic management. This approach provides network administrators with enhanced flexibility and efficiency in managing network traffic. However, SDN also presents new security challenges, such as denial-of-service (DoS) attacks and a single point of failure. To mitigate these attacks in real-time, blockchain uses a distributed network of nodes to verify transactions, which are then added to an immutable ledger. However, deploying SDN blockchain in resource-constrained devices with issues such as efficient energy utilisation, huge data processing, and minimised end-to-end delay is challenging. In this work, we discuss a hierarchical architecture for secure and efficient deployment, as well as a task offloading strategy that aims to improve the efficiency of resource-constrained edge devices by utilising a centralised algorithm for low latency, secure, and reliable decision-making. To maintain the confidentiality of the information and prevent unauthorised access, it is recommended to encrypt data before storing it in blocks. Finally, enforcing flow rules in the network at a granular level provides a secure and tamper-proof record of the rules enforced in the switches. These methods can enhance the adaptability of blockchain-based SDN in resource-constrained network devices compared with existing methods.
Abstract Offshore wind farms will play a vital role in the global ambition of net zero energy generation. Future offshore wind farms will be larger and further from the coast, meaning that traditional humanâbased operations and maintenance approaches will become infeasible due to safety, cost, and skills shortages. The use of remotely operated or autonomous robotic assistants to undertake these activities provides an attractive alternative solution. This paper presents an autonomous multirobot system which is able to transport, deploy and retrieve a wind turbine blade inspection robot using an unmanned aerial vehicle (UAV). The proposed solution is a fully autonomous system including a robot deployment interface for deployment, a mechatronic linkâhook module (LHM) for retrieval, both installed on the underside of a UAV, a mechatronic onâload attaching module installed on the robotic payload and an intelligent global mission planner. The LHM is integrated with a 2âDOF hinge that can operate either passively or actively to reduce the swing motion of a slung load by approximately 30%. The mechatronic modules can be coupled and decoupled by special maneuvers of the UAV, and the intelligent global mission planner coordinates the operations of the UAV and the mechatronic modules for synchronous and seamless actions. For navigation in the vicinity of wind turbine blades, a visualâbased localization merged with the location knowledge from Global Navigation Satellite System has been developed. A proofâofâconcept system was field tested on a fullâsize decommissioned windâturbine blade. The results show that the experimental system is able to deploy and retrieve a robotic payload onto and from a wind turbine blade safely and robustly without the need for human intervention. The vicinity localization and navigation system have shown an accuracy of 0.65 and 0.44 m in the horizontal and vertical directions, respectively. Furthermore, this study shows the feasibility of systems toward autonomous inspection and maintenance of offshore windfarms.
Simultaneous localization and mapping (SLAM) in unmanned vehicles, such as drones, has great usability potential in versatile applications. When operating SLAM in multi-drone scenarios, collecting and sharing the map data and deriving converged maps are major issues (regarded as the bottleneck of the system). This paper presents a novel approach that utilizes the concepts of distributed ledger technology (DLT) for enabling the online map convergence of multiple drones without a centralized station. As DLT allows each agent to secure a collective database of valid transactions, DLT-powered SLAM can let each drone secure global 3D map data and utilize these data for navigation. However, block-based DLTâa so called blockchainâmay not fit well to the multi-drone SLAM due to the restricted data structure, discrete consensus, and high power consumption. Thus, we designed a multi-drone SLAM system that constructs a DAG-based map database and sifts the noisy 3D points based on the DLT philosophy, named DAGmap. Considering the differences between currency transactions and data constructions, we designed a new strategy for data organization, validation, and a consensus framework under the philosophy of DAG-based DLT. We carried out a numerical analysis of the proposed system with an off-the-shelf camera and drones.
Roman Overko, Rodrigo Ordóñez-Hurtado, Sergiy Zhuk, Robert Shorten
In this paper, we describe an approach to guide drivers searching for a parking space (PS). The proposed system suggests a sequence of routes that drivers should traverse in order to maximise the expected likelihood of finding a PS and minimise the travel distance. This system is built on our recent architecture SPToken, which combines both Distributed Ledger Technology (DLT) and Reinforcement Learning (RL) to realise a system for the estimation of an unknown distribution without disturbing the environment. For this, we use a number of virtual tokens that are passed from vehicle to vehicle to enable a massively parallelised RL system that estimates the best route for a given origin-destination (OD) pair, using crowdsourced information from participant vehicles. Additionally, a moving window with reward memory mechanism is included to better cope with non-stationary environments. Simulation results are given to illustrate the efficacy of our system.
Ahmad Reza Cheraghi, Abdelrahman Abdelgalil, KĂĄlmĂĄn Graffi
This research introduces the design and implementation of a universal systematic 2-dimensional terrain marking and coverage solution. Real world applications such as lawn mowing, mine detection, chemical spill clean-up, and humanitarian search and rescue missions can be automated by employing swarms of autonomous mobile robots to complete the task. In most of these real world applications, efficiency is of utmost importance especially when human lives are involved. The solution proposed builds up on two graph traversal algorithms; Depth First Search (DFS) and Breadth First Search (BFS), where the algorithms are adapted and modified to be utilized for coverage of 2-dimensional isometric grid-like representations of terrains. The solution is developed so that each robot in the swarm would be fully capable of covering and marking any terrain by itself. The efficiency of the solution is optimized by increasing the swarm size, as robots benefit from data sharing in their path planning and self organization within the terrain. Communication between robots enable them to perform on a higher level by benefiting from the collaborative distributed behavior of the swarm as a whole. The communication between particles is decentralized and is carried out at a local level with no need for a central guidance mechanism. Robot abilities in this research are limited, where a robot can see and move only to locations that are adjacent to its current location. A simulation based evaluation is conducted in this research to assess the robots' area coverage and marking performance. The results show that the simulated robot swarm systems are suited for efficient flat area coverage, allowing for redundancy in data collection, and tolerating individual robot errors and shortcomings as the number of robots becomes more abundant.
Semi- or fully autonomous personal aerial vehicles (PAVs) are currently studied and developed by public and private organizations as a solution for traffic congestion. While optimal collision-free navigation algorithms have been proposed for autonomous robots, trajectories and accelerations for PAVs should also take into account human comfort. In this letter, we propose a reactive decentralized collision avoidance strategy that incorporates passenger physiological comfort based on the optimal reciprocal collision avoidance strategy. We study in simulation the effects of increasing PAV densities on the level of comfort, on the relative flight time and on the number of collisions per flight hour and demonstrate that our strategy reduces collision risk for platforms with limited dynamic range. Finally, we validate our strategy with a swarm of ten quadcopters flying outdoors.