Multi-objective Anti-swing Trajectory Planning of Double-pendulum Tower Crane Operations using Opposition-based Evolutionary Algorithm

Underactuated tower crane lifting requires time-energy optimal trajectories for the trolley/slew operations and reduction of the unactuated swings resulting from the trolley/jib motion. In scenarios involving non-negligible hook mass or long rig-cable, the hook-payload unit exhibits double-pendulum behaviour, making the problem highly challenging. This article introduces an offline multi-objective anti-swing trajectory planning module for a Computer-Aided Lift Planning (CALP) system of autonomous double-pendulum tower cranes, addressing all the transient state constraints. A set of auxiliary outputs are selected by methodically analyzing the payload swing dynamics and are used to prove the differential flatness property of the crane operations. The flat outputs are parameterized via suitable B\'{e}zier curves to formulate the multi-objective trajectory optimization problems in the flat output space. A novel multi-objective evolutionary algorithm called Collective Oppositional Generalized Differential Evolution 3 (CO-GDE3) is employed as the optimizer. To obtain faster convergence and better consistency in getting a wide range of good solutions, a new population initialization strategy is integrated into the conventional GDE3. The computationally efficient initialization method incorporates various concepts of computational opposition. Statistical comparisons based on trolley and slew operations verify the superiority of convergence and reliability of CO-GDE3 over the standard GDE3. Trolley and slew operations of a collision-free lifting path computed via the path planner of the CALP system are selected for a simulation study. The simulated trajectories demonstrate that the proposed planner can produce time-energy optimal solutions, keeping all the state variables within their respective limits and restricting the hook and payload swings.Comment: 14 pages, 14 figures, 6 table

Intelligent lift motion planning for autonomous tower cranes in dynamic BIM environments

A Lift Planning System (LPS) is an imperative component for optimal and safe autonomous crane lifting. Two crucial issues concerning the dynamic nature of the crane system and the construction environment are lifting path re-planning and lifting trajectory planning. The path re-planning scenario involves making decisions on re-generating portions of an already planned lifting path in the presence of dynamic objects in the construction scene and strategizing the implementation of that decision. On the other hand, the trajectory planning problem calls for anti-swing optimal trajectory generation of planned lifting paths to supply reference inputs to the controller of underactuated tower cranes. The primary focus of the current research work is to develop a path re-planning module and a trajectory planning module for an LPS possessing the superior ability to plan collision-free optimal lifting paths in complex construction environments in near real-time. The application is exclusive to tower crane operations in residential or non-residential building construction. Building environments from Building Information Modeling (BIM) systems are utilized in the LPS. Dynamic objects in the scene, considered obstacles, are classified according to their effect on the planned lifting path. The obstacles are updated to the original SDM of the environment via a Single-level Depth Map (SDM) integration technique to portray the dynamic nature of the construction scene. Following the obstacle modelling, a re-planning module constituting a Decision Support System (DSS) and a Path Re-planner (PRP) are prepared. A novel re-planning decision-making algorithm using multi-level Oriented Bounding Boxes (OBBs) is formulated for the DSS. A path re-planning strategy via updating the start configuration for the local path is devised for the PRP. Experiments with scaled real-world models of a building and a specific tower crane show excellent decision accuracy and near real-time re-planning with high optimality. Reducing the unactuated payload motion is a crucial issue for underactuated tower cranes with spherical pendulum dynamics. Moreover, the planned trajectory should be optimal in terms of time and energy to facilitate optimum output at the expense of optimum effort. An offline anti-swing multi-objective trajectory planner is developed in this research for autonomous tower cranes where the hoist-cable and the payload together act as the pendulum. Analyzing the nonlinear dynamics of all the fundamental crane operations, the trajectory planning problems are converted to constrained Multi-Objective Trajectory Optimization Problems (MOTOPs) by parameterizing the corresponding flat outputs via suitably selected Bézier curves. A well-established Multi-Objective Evolutionary Algorithm (MOEA), namely Generalized Differential Evolution 3 (GDE3), is selected as the optimizer, through a detailed comparison with another MOEA, i.e. Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II). The crane operation trajectories are computed via the corresponding planned flat output trajectories. Experiments simulating all lifting operations validate the effectiveness and reliability of the advocated strategy. In scenarios involving comparable masses of hook and payload or long rig-cable, both the hook and the payload exhibit spherical-pendulum behaviour, making the trajectory planning problem highly challenging. The aforementioned offline trajectory planner is equipped with the additional flat output constructor for autonomous double-pendulum tower cranes to deal with the double-swing. The MOTOPs for the trolley/slew operations are formulated through consideration of mechanical and safety constraints, via Bézier curve parameterization. The conventional GDE3 optimizer is improved by integrating a new population initialization strategy, incorporating various concepts of computational opposition. Statistical results of experimental studies with trolley and slew operations verify the superiority of the new MOEA, namely Collective Oppositional GDE3 (CO-GDE3), over the standard GDE3, in terms of convergence and reliability. The simulated trajectory results demonstrate that the proposed planner can produce time-energy optimal trajectories, keeping all the state variables within their respective limits, and reducing the residual payload swing to zero.Doctor of Philosoph

Automatic re-planning of lifting paths for robotized tower cranes in dynamic BIM environments

Computer-Aided Lift Planning (CALP) systems provide smart and optimal solutions for automatic crane lifting, supported by intelligent decision-making and planning algorithms along with computer graphics and simulations. Re-planning collision-free optimal lifting paths in near real-time is an essential feature for a robotized crane operating in a construction environment that is changing with time. The primary focus of the present research work is to develop a re-planning module for the CALP system designed at Nanyang Technological University. The CALP system employs GPU-based parallelization approach for discrete and continuous collision detection as well as for path planning. Building Information Modeling (BIM) is utilized in the system, and a Single-level Depth Map (SDM) representation is implemented to reduce the huge data set of BIM models for usage in discrete and continuous collision detection. The proposed re-planning module constitutes of a Decision Support System (DSS) and a Path Re-planner (PRP). A novel re-planning decision making algorithm using multi-level Oriented Bounding Boxes (OBBs) is formulated for the DSS. A path re-planning strategy via updating the start configuration for the local path is devised for the PRP. Two case studies are carried out with real-world models of a building and a specific tower crane to validate the effective performance of the re-planning module. The results show excellent decision accuracy and near real-time re-planning with high optimality.Nanyang Technological UniversityThe authors are grateful to the Interdisciplinary Graduate School and the Energy Research Institute @ NTU of Nanyang Technological University, Singapore, for the financial support in the current research

BIM4D-based scheduling for assembling and lifting in precast-enabled construction

This research addresses the problem of assembly scheduling in crane-assisted precast construction while considering issues of building layout interference and optimal crane lifting. Traditionally, assembly scheduling and lifting path planning are treated as two separate issues due to their distinct natures. The current work introduces an approach that combines them for precast construction planning to achieve a comprehensive and cost-effective solution. A BIM4D-based Intelligent Assembly Scheduler (BIAS) is designed in conjunction with the Computer-Aided Lifting Planner developed at Nanyang Technological University, Singapore. BIM4D is the Building Information Modeling (BIM) with the time dimension (i.e., scheduling information). Our scheduler takes an inbuilt timeframe for selected precast elements from BIM4D as input and outputs the micro-schedule in terms of the assembly sequence for these precast elements. This problem is solved using multi-objective optimization. Given a group of precast elements and their BIM4D timeframe, the micro-scheduling is determined based on (1) the relative importance of the elements’ physical properties, (2) the interference (neighbouring relation) among the elements’ positions, and (3) collision-free lifting paths of the elements. A Multi-level Elitist Genetic Algorithm (MEGA) is proposed to determine the optimal sequence taking into consideration of both assembling and lifting for the elements. A case study is performed with the BIM4D data of a residential building. The results of the case study demonstrate BIAS's efficiency and effectiveness for BIM4D based construction scheduling.This study is supported under the RIE2020 Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from Surbana Jurong Pte Ltd

Point cloud based path planning for tower crane lifting

This paper discusses automatic path planning for tower crane lifting in highly complex environments to be digitized using point cloud representation. A mathematical optimization technique is developed to identify the lifting path with GPU accelerated massively parallel genetic algorithm. A continuous collision detection method is designed for real time application of collision avoidance during the crane lifting process.NRF (Natl Research Foundation, S’pore)Accepted versio