Dynamic Replanning Research Articles

Autonomous Exploration Method for Fast Unknown Environment Mapping by Using UAV Equipped With Limited FOV Sensor

Autonomous exploration is crucial and highly challenging for various intelligent operations of unmanned aerial vehicles (UAVs). However, low-efficiency problems caused by low-quality trajectory or back-and-forth maneuvers (BFM) still exist. To improve the exploration efficiency in unknown environments, a fast autonomous exploration planner (FAEP) is proposed in this paper. We firstly design a novel frontiers exploration sequence generation method to significantly reduce BFM during the exploration by considering multi-level factors in asymmetric traveling salesman problem (ATSP). Then, according to the exploration sequence and the distribution of frontiers, an adaptive yaw planning method is proposed to cover more frontiers by yaw change during an exploration journey. In addition, to increase the fluency of flight, a dynamic replanning strategy is also adopted. The main technical contributions of the proposed approach are to provide a comprehensive global coverage path with less BFM, and use adaptive yaw planning to generate more reasonable yaw trajectory. We present extensive comparison and real-world experiments to validate the effectiveness and correctness of our method. Experimental results show the proposed approach has better performance compared to typical and state-of-the-art methods in these flat scenarios without lots of scattered obstacles. We release our implementation as an open-source package <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{1}$</tex-math></inline-formula> for the community.

Moving toward lean construction through automation of planning and control in last planner system: A systematic literature review

The Last Planner System (LPS) is a production planning and control system based on Lean Construction (LC) principles. Effectively implementing the LPS in the construction industry remains challenging, impeding the realization of LC principles. Automation of planning and control in the LPS has gained attention to address this challenge. However, research exploring the state-of-the-art of such automation and its impacts on realizing LC principles is still lacking. Therefore, this study performed a systematic literature review of 112 relevant studies, identifying 50 functionalities and associated benefits of automation across six LPS stages, with the lookahead planning stage having the maximum functionalities. Analyzing their interactions with LC principles revealed that automated planning and control in the LPS can support achieving ten LC principles. Future research directions are suggested in areas such as constraint management, knowledge reuse, dynamic replanning, and construction workforce training.

Kid-size robot humanoid walking with heel-contact and toe-off motion.

Human-like features, like toe-off, heel-strike can enhance the performance of bipedal robots. However, few studies have considered the anthropomorphism of walking planning. Fewer studies have achieved their toe-off, heel-strike gait planning framework in a child-sized humanoid robot platform. This paper presents a human-like walking control framework based on the Divergent Component of Motion (DCM) com planning method that enables a child-sized humanoid robot to walk with a humanoid pattern with a speed of 0.6 s per step a strike of 30 cm. The control framework consists of three parts: the human-like gait generation of the center of mass (CoM) and swings foot trajectory, the dynamic replan in phase switch and the upper body stabilization controller. The dynamic replanning of the CoM and foot trajectory can efficiently decrease the vibration in the step-phase switch. The up-body stabilization controller can reduce the up-body swing in walking and increase the robot's stability while walking. The robot uses a mems-based inertial measurement unit (IMU) and joint position encoders to estimate the current state of the robot and use force-sensitive resistors (FSR) on the robot foot to identify the actual step phase of the robot. None of these solutions is high-cost or difficult to integrate with a child-size robot. Software simulations and walking experiments are using to verify the motion control algorithm. The effectiveness of the pattern generation and the controller can realize more human-like walking styles in a child-size robot are confirmed.

Automated Lane Change Featuring Re-Planning in Dynamic Environments and Sensitivity Analysis of Main Operational Parameters

Lane changes are frequent maneuvers in everyday driving and have to be included in automated driving functions. We present a real-time capable implementation of an algorithm for an automated lane change with the capability of dynamic re-planning in case the environmental situation changes, e.g. other traffic participants change their intention and the previously calculated trajectory does not guarantee safety anymore. Trajectories are described by polynomials and stored in look-up tables for different longitudinal velocities, longitudinal accelerations and maneuver duration times. Using a single-track model within a linear-quadratic control, the lateral deviation to the reference trajectory and the vehicle’s orientation are controlled. The influence of parameter changes from additional payload, tire-road friction coefficients and tire properties on the vehicle-controller interaction are investigated with a sensitivity analysis on data generated with a simulation. The performance of the algorithm is presented and it is shown that many parameter variations will probably be perceived by occupants, but will not lead to safety-critical situations.

IMPROVING HUMAN AWARENESS DURING COLLABORATION WITH ROBOT: REVIEW

Human-robot collaboration is a widespread topic within the concept of Industry 4.0. Such collaboration brings new opportunities to improve ergonomics and innovative options for manufacturing automation; however, most of the modern collaborative industrial applications are limited by the fact that neither collaborative side is fully aware of the partner: the human operator may not see the robot movement due to own engagement in the work process, and the collaborative robot simply has no means of knowing the position of the operator. Dynamic replanning of the robot trajectory with respect to the operator's current position can increase the efficiency and safety of cooperation since the robot will be able to avoid collisions and proceed in task completion; however, the other side of communication remains unresolved. This paper provides a review of methods of improving human awareness during collaboration with a robot. Covered techniques include graphical, acoustic and haptic feedback implementations. The work is focused on the practical applicability of the approaches, and analyses present challenges associated with each method.

Service Discovery and Selection Based on Dynamic QoS in the Internet of Things

Web services are self-describing and self-contained modular applications based on the network. With the deepening of web service applications, service consumers have gradually increased their requirements for service functions and service quality. Aiming at how to select the optimal plan from a large number of execution plans with the same function and different QoS characteristics, this paper proposes a web service selection algorithm that supports QoS global optimization and dynamic replanning. The algorithm uses position matrix coding to represent all execution paths and replanning information of the service combination. By calculating the Hamming distance of the service quality between individuals, the quality of the service portfolio is improved. By specifying the total user time limit and implementing a good solution retention strategy, the problem of the impact of algorithm running time on service quality is solved. The experimental results show that the method proposed in this paper is effectively integrated into the development trend of QoS and close to the requester’s needs and can better meet user needs. This algorithm improves the user’s satisfaction with the returned service to a certain extent and improves the efficiency of service invocation.

Production Process Interoperability for Cyber-Physical Production Systems

Abstract Operating/maintaining a shop-floor and planning for the efficient utilization of said shop floor, are typically two different sides of a coin. While the operation side is focused on making the interaction between humans, machines and software run flawlessly (e.g. by utilizing orchestration software), the planning side is focused on finding the right order of doing things for good utilization of resources. Planning is mostly done according to a regular schedule (once a day, once a week). The production of individual products is intended to happen in the regularly pre-determined order, including some slack to account for minor problems. During production, shop-floor workers (or smart machinery) can deal with problems, but they are nonetheless intended to follow the pre-determined plan. The contribution of this paper is a case-study that explores the integration and interoperability problems that occur, when a flexible shop-floor orchestration software centurio.work is coupled with a high-performance scheduling software (NgMPSS) in order to allow for ad-hoc planning and re-planning of a production schedule. The idea behind this integration is, (1) to allow for a system that is more robust when dealing with problems, but also (2) allows for reaping additional overall time savings, because dynamic re-planning allows for minimal slack between tasks. This research has been conducted on the interface between the Center for Digital Production (CDP) and the Pro2Future (P2F) as part of a common research project.

Navigation of autonomous systems based on situation control with dynamic replanning

The solution to the problem of autonomous mobile systems navigation is a complex task, traditionally presented in the form of solving the sequence of subtasks: perception of information about the environment, localization and mapping, path planning, and motion control. A large number of scientific works are devoted to the solution of the listed subtasks. However, existing research does not pay enough attention to the integration of individual elements of the navigation cycle solutions into a single homogeneous system. This leads to an additional accumulation of errors in the process of a complex solution to the navigation problem. In previous works, a model was proposed that provides homogeneous integration, using for this a multi-level structure of representing an autonomous system's knowledge in the form of sets of fuzzy rules and facts. The five-level model represents the autonomous system's knowledge of goals, paths, an environment map, strategies, and specific controls necessary to achieve the goal. To ensure adequate processing of fuzzy rules, a modified Takagi-Sugeno-Kang fuzzy inference model is proposed. In this work, the previously proposed model is expanded. The model was tested in conditions of noisy sensor data. A method is proposed for the formation of level 2 rules, which describe an autonomous system's cartographic knowledge about the environment, using the well-known methods of global path planning. Extension of the model provides dynamic paths replanning of the autonomous system, using the processing of present knowledge about existing paths. Such replanning is effective in terms of computational time and independent of the completeness of the knowledge base of complete paths.

Route Planning of Unmanned Aerial Vehicle Based on Sparse A* Algorithm

Considering the matters of accuracy, real-time, task adaptability and dynamic re-planning for the route planning of unmanned aerial vehicle. When the unmanned aerial vehicle is planning its route on a largescale map, it needs to ensure better real-time performance. If new obstacles are encountered during flight, the route planning algorithm needs to be able to carry out dynamic planning to re-plan the route to avoid the new obstacles. This paper presented an optimized route planning of unmanned aerial vehicle algorithm, which mainly through the management of number of search nodes and stratification strategy while still maintaining a safe route. We also presented a dynamic route re-planning function when encountering new obstacles by updating the map. The algorithm has better real-time performance and robustness. It has great reference value for the practical application of unmanned aerial vehicle.

Beyond surge: Coping with mass burn casualty in the closest hospital to the Formosa Fun Coast Dust Explosion

PurposeTo provide an insight into the challenges faced by the closest hospital to the Formosa Fun Coast Dust Explosion (FFCDE) disaster scene, and to examine how the hospital staff adapted to cope with the mass burn casualty (MBC) in their overcrowded emergency department (ED) after the disaster. Material and methodsThe critical incident technique was used for the investigation. Data was gathered through in-depth individual interviews with 15 key participants in this event. The interview data was combined with the medical records of the FFCDE patients and admission logs to build a detailed timeline of ED workload. Process tracing analysis was used to evaluate how the ED and other units adapted to deal with actual and potential bottlenecks created by the patient surge. ResultsFifty-eight burn patients were treated and registered in approximately six hours while the ED managed 43 non-FFCDE patients. Forty-four patients with average total body surface area burn 51.3% were admitted. Twenty burn patients were intubated. The overwhelming demand created shortages primarily of clinicians, ED space, stretchers, ICU beds, and critical medical materials for burn care. Adaptive activities for the initial resuscitation are identified and synthesized into three typical adaptation patterns. These adaptations were never previously adopted in ED normal practices for daily surge nor in periodical exercises. The analysis revealed adaptation stemmed from the dynamic re-planning and coordination across roles and units and the anticipation of bottlenecks ahead. ConclusionIn the hospital closest to the FFCDE disaster scene, it caused an overwhelming demand in an already crowded, beyond-nominal-capacity ED. This study describes how the hospital mobilized and reconfigured response capacity to cope with overload, uncertainty, and time pressure. These findings support improving disaster planning and preparedness for all healthcare entities through organizational support for adaptation and routine practice coping with unexpected scenarios.

Smart Process Optimization and Adaptive Execution with Semantic Services in Cloud Manufacturing †

A new requirement for the manufacturing companies in Industry 4.0 is to be flexible with respect to changes in demands, requiring them to react rapidly and efficiently on the production capacities. Together with the trend to use Service-Oriented Architectures (SOA), this requirement induces a need for agile collaboration among supply chain partners, but also between different divisions or branches of the same company. In order to address this collaboration challenge, we propose a novel pragmatic approach for the process analysis, implementation and execution. This is achieved through sets of semantic annotations of business process models encoded into BPMN 2.0 extensions. Building blocks for such manufacturing processes are the individual available services, which are also semantically annotated according to the Everything-as-a-Service (XaaS) principles and stored into a common marketplace. The optimization of such manufacturing processes combines pattern-based semantic composition of services with their non-functional aspects. This is achieved by means of Quality-of-Service (QoS)-based Constraint Optimization Problem (COP) solving, resulting in an automatic implementation of service-based manufacturing processes. The produced solution is mapped back to the BPMN 2.0 standard formalism by means of the introduced extension elements, fully detailing the enactable optimal process service plan produced. This approach allows enacting a process instance, using just-in-time service leasing, allocation of resources and dynamic replanning in the case of failures. This proposition provides the best compromise between external visibility, control and flexibility. In this way, it provides an optimal approach for business process models’ implementation, with a full service-oriented taste, by implementing user-defined QoS metrics, just-in-time execution and basic dynamic repairing capabilities. This paper presents the described approach and the technical architecture and depicts one initial industrial application in the manufacturing domain of aluminum forging for bicycle hull body forming, where the advantages stemming from the main capabilities of this approach are sketched.

Extended rapidly exploring random tree–based dynamic path planning and replanning for mobile robots

It is necessary for a mobile robot or even a multi-robot team to be able to efficiently plan a path from its starting or current location to a desired goal location. This is a trivial task when the environment is static. However, the operational environment of the robot is rarely static, and it often has many moving obstacles. The robot may encounter one, or many, of these unknown and unpredictable moving obstacles. The robot will need to decide how to proceed when one of these obstacles is obstructing its path. In this article, a new method of dynamic replanning is proposed to allow the robot to efficiently plan a path in such complex environments. Our proposed replanning method is based on an extended rapidly exploring random tree. The robot will modify its current plan when unknown random moving obstacles obstruct the path. We extend the proposed replanning method to multi-robot scenarios in which the ability to share path planning and search tree information is valuable. An efficient method of node sharing is proposed to allow the multi-robot team to quickly develop path plans. Various experimental results in both single and multi-robot scenarios show the effectiveness of the proposed methods.

Automatic differentiation of rigid body dynamics for optimal control and estimation

Many algorithms for control, optimization and estimation in robotics depend on derivatives of the underlying system dynamics, e.g. to compute linearizations, sensitivities or gradient directions. However, we show that when dealing with rigid body dynamics, these derivatives are difficult to derive analytically and to implement efficiently. To overcome this issue, we extend the modelling tool ‘RobCoGen’ to be compatible with Automatic Differentiation. Additionally, we propose how to automatically obtain the derivatives and generate highly efficient source code. We highlight the flexibility and performance of the approach in two application examples. First, we show a trajectory optimization example for the quadrupedal robot HyQ, which employs auto-differentiation on the dynamics including a contact model. Second, we present a hardware experiment in which a six-DoF robotic arm avoids a randomly moving obstacle in a go-to task by fast, dynamic replanning.

Multiple Mobile Robot Dispatch Strategy for Cooperative Applications in Confined Space

In this paper, we propose a path planning and dispatch strategy of multiple mobile robots for cooperative applications in confined spaces. The proposed two level path planning method incorporates the segment reservation mechanism to maximize the utilization of robots. The two level path planning reduces the computational complexity while providing the flexibility for real-time adaptation. Any potential deadlock is completely eliminated and the starvation due to unpredictable service request patterns is avoided. Since the proposed methodology is used for confined areas, any possible path blockage due to idling robots is eliminated with idling robot relocation mechanism. The dynamic path planning is achieved by monitoring and controlling the speed of the robots. The dynamic replanning is triggered with periodic monitoring of the service constraints. The method can be effectively used for low power multiple robot navigation applications and the performance of the proposed system is evaluated with largescale simulations.

Virtual Infrastructures as a service enabling converged optical networks and Data Centres

Cloud computing service emerged as an essential component of the Enterprise IT infrastructure. Migration towards a full range and large-scale convergence of Cloud and network services has become the current trend for addressing requirements of the Cloud environment. Our approach takes the infrastructure as a service paradigm to build converged virtual infrastructures, which allow offering tailored performance and enable multi-tenancy over a common physical infrastructure. Thanks to virtualization, new exploitation activities of the physical infrastructures may arise for both transport network and Data Centres services. This approach makes network and Data Centres׳ resources dedicated to Cloud Computing to converge on the same flexible and scalable level. The work presented here is based on the automation of the virtual infrastructure provisioning service. On top of the virtual infrastructures, a coordinated operation and control of the different resources is performed with the objective of automatically tailoring connectivity services to the Cloud service dynamics. Furthermore, in order to support elasticity of the Cloud services through the optical network, dynamic re-planning features have been provided to the virtual infrastructure service, which allows scaling up or down existing virtual infrastructures to optimize resource utilization and dynamically adapt to users׳ demands. Thus, the dynamic re-planning of the service becomes key component for the coordination of Cloud and optical network resource in an optimal way in terms of resource utilization. The presented work is complemented with a use case of the virtual infrastructure service being adopted in a distributed Enterprise Information System, that scales up and down as a function of the application requests.

Planning of dynamic virtual optical cloud infrastructures: The GEYSERS approach

This article focuses on planning and replanning of virtual infrastructures over optical cloud infrastructures comprising integrated optical network and IT resources. This concept has been developed in the context of the European project GEYSERS. GEYSERS has proposed a novel multi-layer architecture, described in detail, that employs optical networking capable of provisioning optical network and IT resources for end-to-end cloud service delivery. The procedures required to perform virtual infrastructure planning and replanning at the different architecture layers are also detailed. An optimization scheme suitable to dynamically plan and replan virtual infrastructures is presented and compared to conventional approaches, and the benefits of dynamic replanning are discussed and quantified. The final project demonstration, focusing on planning, replanning, and dynamically establishing virtual infrastructures over the physical resources, is presented, while some emulation results are provided to further evaluate the performance of the GEYSERS solution.

Dynamic Virtual Optical Networks Supporting Uncertain Traffic Demands [Invited

This paper focuses on the concept of dynamic virtual optical networks (VONs) as a key enabling technology to support the requirements of the future Internet. To address the unpredictable nature of services and their characteristics, dynamic and on-demand planning and replanning of VONs is required. This paper proposes the use of novel modeling approaches to specifically consider the uncertain nature of traffic requests. In addition, it studies mechanisms to evaluate and minimize the impact of this uncertainty on the availability and efficiency of the virtual resources, considering at the same time requirements such as service resilience and security. The effect of periodic VON replanning is investigated and the relevant trade-offs are identified. Our modeling results show that taking into consideration the dynamic and uncertain nature of services provides significant benefits regarding the ability of the infrastructure to support these services and the associated resource efficiency.

Coordinated dynamic mission planning scheme for intelligent multi-agent systems

Mission planning was thoroughly studied in the areas of multiple intelligent agent systems, such as multiple unmanned air vehicles, and multiple processor systems. However, it still faces challenges due to the system complexity, the execution order constraints, and the dynamic environment uncertainty. To address it, a coordinated dynamic mission planning scheme is proposed utilizing the method of the weighted AND/OR tree and the AOE-Network. In the scheme, the mission is decomposed into a time-constraint weighted AND/OR tree, which is converted into an AOE-Network for mission planning. Then, a dynamic planning algorithm is designed which uses task subcontracting and dynamic re-decomposition to coordinate conflicts. The scheme can reduce the task complexity and its execution time by implementing real-time dynamic re-planning. The simulation proves the effectiveness of this approach.

Design and Validation of a Synthetic Task Environment to Study Dynamic Unmanned Aerial Vehicle Re-Planning

Design and Validation of a Synthetic Task Environment to Study Dynamic Unmanned Aerial Vehicle Re-Planning

Design and Validation of a Synthetic Task Environment to Study Dynamic Unmanned Aerial Vehicle Re-Planning

A key challenge facing unmanned aerial vehicle (UAV) operators is the need to re-plan routes on-the-fly when situations change. Operators must comprehend three-dimensional (3D) scenes and a multitude of potentially competing 3D mission and routing constraints in order to successfully re-plan, often under time pressure. Currently, there is significant research interest in supporting UAV operators through automation and improved visualizations. However, development and integration of these methods requires a careful understanding of the 3D spatial awareness challenges and requirements facing operators. To facilitate this understanding, here we report the design and validation of a synthetic task environment (STE) and testbed to study UAV re-planning. The STE is derived from a recent task analysis conducted with Navy UAV operators that focused on the key 3D spatial challenges entailed in re-planning. In an initial validation of the STE implemented in a re-planning testbed, several measures of re-planning performance were assessed for 36 participants working through controlled re-planning scenarios. The presence of mountainous terrain and the spatial overlap of mission constraints were parametrically varied. Performance was consistently worse in mountainous terrain, and in more highly-constrained conditions in mountainous terrain. In flat terrain, however, less constrained conditions resulted in paradoxically worse performance. Results have both basic and applied implications. Theoretically, the study provides a bridge between applied re-planning research and classic human problem solving work by allowing apparently simpler, unconstrained re-planning to be conceived of as less bounded search through re-planning problem space. For application, the results help constrain and define the requirements for future 3D visualization and automation support for UAV re-planning displays.