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

https://doi.org/10.1016/j.autcon.2019.102998Get rights and content

Highlights

  • This work introduces a re-planning module for a Computer-Aided Lift Planning system for robotized tower cranes.

  • 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 OBBs is formulated for the DSS.

  • A path re-planning strategy via updating the start configuration for the local lifting path is devised for the PRP.

  • Two case studies are conducted with complex real-world BIM models of a residential building and 3D model of a tower crane.

Abstract

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.

Introduction

Path planning for automated lifting using robotized cranes requires rapid and accurate computation of optimal paths to be followed for each lifting task specified. This is one of the major reasons for the increased demand of Computer-Aided Lift Planning (CALP) systems among current construction industries. The automated operation of the crane depends on the smart solutions from the CALP system, which uses computer simulation and intelligent algorithms. Automated lifting path planners can provide a collision-free optimal lifting path for the robotized crane to follow, hence increasing both safety and productivity. The most abundant and pertinent type of cranes used in the construction sector of buildings (residential and non-residential) are tower cranes. For the computational simulations in a CALP system, models of cranes along with proper environmental representation of the construction site and the building data are needed. 3D virtual models of different types of crane can be robotized by assigning convenient degrees-of-freedom (DOF), like 4-DOF for tower cranes, to be used for planning and simulation. Modern buildings are presented digitally via intelligent management tools such as Building Information Modeling (BIM) [1] system. These systems manage the comprehensive geometric, scheduling and cost information for the building to be constructed. The information can be utilized to produce complete and accurate models of the objects in the environment and the lifting targets. These digital tools can be integrated to design a CALP system which is a core technology supporting automatic lift planning.

However, there has been barely adequate work done in the area of addressing CALP systems as automatic lift planners. For advanced applications like industrial lifting tasks in complex building construction scenes, tower cranes operate in dynamic environments, where there is always need for obstacle avoidance. If the construction site contains dynamic objects, the planning algorithm has to include re-planning steps to modify the lifting path previously computed according to the environment changes. There are limited work in the literature for dynamic re-planning for crane lifting paths using real industrial buildings in 3D. Most of the work either addresses static conditions or rely on conventional Computer Aided Design (CAD) models of building and construction site, restraining the application to relatively simpler cases.

Recent studies which deal with crane lifting path planning in static environment use combinations of different search algorithms and collision detection strategies to plan paths for automatic crane lifting in a static representation of the construction site. The first class of methods employ global optimization search algorithms to achieve high optimality. These algorithms usually pre-compute the configuration spaces (C-spaces). Sivakumar et al. [2] have represented cranes as 2-DOF planar kinematic chains. They have applied simple Genetic Algorithm (GA) [3] on the 2D C-space. The path optimization problem treats the pre-computed collision results as hard constraints. Ali et al. [4] have applied a two-stage serial GA to search in pre-calculated C-spaces. Even though both of the GA-based methods can supply solutions with high optimality, the computational intensity of GA renders these methods as impractical. As a result, the methods can only incorporate simple CAD models. Moreover, the pre-computation of the C-space in Ali's algorithm is computationally time-expensive as well.

The second set of methods use fast search algorithms to obtain good results, irrespective of the global optimum of the lifting paths. Among these algorithms, some also rely on pre-calculated C-spaces to improve the runtime efficiency. The method by Reddy and Varghese [5] uses interference detection technique to generate the C-space. It relies on two levels of heuristic search to determine the obstacle-free path and then to seek optimality in a confined search space. This method spends a significant period of time and huge memory to compute the 3D C-space free of obstacles. Chang et al. [6] have reduced the complexity of the C-space by determining maximum and minimum hoist-heights allowed for each 2D configuration of the crane. They have used a Probabilistic Road Map (PRM) [7] method to generate paths. Hoisting planning is then conducted by checking the resulted 2D paths with the obstacle heights, to achieve near real-time solution in simple environments. However, the optimality of the paths cannot be guaranteed since the problem has been decomposed. The algorithm by Olearczyk et al. [8] defines the lifting paths as a piece-wise continuous sequence of swinging and luffing along a horizontal plane, at a fixed height. A* search [9] is conducted to search for the paths in that 2D matrix by merging obstacle envelopes for collision clearance. The algorithm has the problem overly constrained and thus can only produce sub-optimal paths. Other methods perform collision detection on the go during the search (henceforth called online collision detection) to avoid the calculation of C-space beforehand. The complexity of the online collision check strategy does not depend on the dimension of the C-space and thus the method can deal with high-DOF crane work-spaces more efficiently. The research by Lin et al. [10] has addressed the lifting path planning problem for 7-DOF crawler cranes. Their algorithm has utilized a bi-directional Rapidly-exploring Random Tree (RRT) [11] and reported reasonable planning time. However, it tends to produce zigzagged paths when most of the DOFs are enabled.

Cai et al. [12] have designed a novel lift planner using Multi-level Depth Map (MDM) representation of complex industrial plants. In order to achieve highly optimized solutions, a Master-Slave Parallel Genetic Algorithm (MSPGA) [13] is used, which has the scope of massive parallelization. As a result, the computational complexity of GA is simplified using parallel threads in Graphics Processing Unit (GPU). The planner uses both online collision detection and a pre-computed 2D C-space to achieve a trade-off between the pre-processing and the planning query time. An image-space collision detection strategy based on GPU have been implemented to handle the online collision detection of the crane and the lifting target with the environment. Efficiency, solution quality and success rates of the proposed lifting path planning algorithm is significantly higher than other ventures. The work has been extended in [14] for cooperative lifting with a prioritized multi-objective approach.

All these work are limited to providing only an initial plan for lifting as they fail to address the issue of dynamic path planning, where a planned lifting path needs to be redesigned either partially or totally, depending on the dynamic nature of the objects in the construction scene. Hence, the CALP systems discussed above are rendered impractical for automatic lifting as most of the real-world construction sites are dynamic in nature where new objects (tools, building components, vehicles etc.) are frequently introduced in the scene after the lifting path has been planned or during the lifting process itself. Such scenarios are quite common in regular building construction sites where tower cranes operate in dynamic environments.

The dynamic aspect of automatic crane lift planning is often neglected in literature, in spite of being a major practical factor in the whole process. The transient behaviour of the construction environment requires real-time monitoring of the scene during execution of a lifting task along a planned path, and a re-planning approach to modify the original planned path of the target object being lifted, if needed. During execution of a lifting task in a dynamic environment, rapid re-planning is essential. The efficiency of a re-planning algorithm is measured by its ability to plan optimal paths in near real-time by modifying invalid portions of the paths according to availability of new information. Limited work has been reported on optimal path planning in dynamic environment and even fewer address the problem of crane lifting which usually includes a computationally expensive C-space.

Deterministic re-planning algorithms such as Dynamic A* (D*) [15] efficiently revise prior planned paths when environmental changes take place. The disadvantage of the basic D*, however, is that it replaces all the invalid nodes (affected portions of the path), without considering the benefit of the change. Efficiency of the D* can be improved by applying heuristic technique to focus on the direction of the motion and follow the expansions of nodes thereafter. Stentz [16] has proposed an extension to the basic D* that utilize a heuristic focusing function to find optimal path to the goal based on the position of the target load. The net effect is a reduction in run-time. Another modification to conventional D* is reported in [17] where a Two-Way D* (TWD) algorithm has been introduced based on a 2D occupancy grid map. However, as the dimension of the C-space increases, deterministic algorithms fail to cope with the huge computational power needed.

As a result, the scarce research found in literature dealing with lifting path re-planning use randomized probabilistic approaches such as RRTs, since such algorithms are not incapacitated by the high dimensionality of the crane C-space in complex environments. For re-planning with RRTs, Bruce and Veloso [18] have proposed an extended version of the RRT algorithm, called Extended RRT (ERRT), to build a real-time path planning system which balances the computation of planning and execution of the lifting path. They have added way-point cache and adaptive cost penalty search to improve the re-planning efficiency and quality of the path. On another approach presented by Ferguson et al. [19], a strategy similar to deterministic re-planning algorithm is followed by efficiently removing only the invalid portions of the current RRT, instead of discarding it and re-growing a new RRT. This is known as Dynamic RRT (DRRT), which is essentially the probabilistic analog of the D* algorithm. Unfortunately, the experiments represented in the work are idealized and limited to a discretized limited environment. AlBahnassi and Hammad [20] have developed a framework for near real-time motion planning of cranes using a modified DRRT to plan efficiently in dynamic environments. They have addressed other cranes present in the scene as dynamic obstacles for a particular crane. Their algorithm concentrates the modifications towards areas in the RRT that have been affected, by adding biasing probability. However, choice of high-biasing probability in the planning phase makes the DRRT inefficient. Also, it is a quick but not necessarily optimal search since some portions of the original tree are removed during repairing.

There are a few other ventures in this field using approaches such as evolutionary algorithms. Mohajer et al. [21] have proposed an online random particle optimization algorithm considering mobile robots in dynamic environments. Kala et al. [22] have attempted to apply a two-layer hierarchical evaluation algorithm which employs a coarse path planner in a static environment consisting of a low-resolution robotic map and finer planners on sections of the map for computation of both static and dynamic environment. Wang et al. [23] have applied a hybrid GA technique, where the search space has been restricted by considering only vertices of obstacles. They have also incorporated the selection of robot speed into the GA genes, resulting in higher number of alternative solutions. Miao and Tian [24] have allowed deletion and switch of path operators and have added the initial path selection heuristics into the standard Simulated Annealing (SA) algorithm [25] for robot path planning in dynamic environments with both static and dynamic obstacles. Even though the approach enhances the computational performance of the standard SA by some amount, it may output near-optimal paths.

Another group of research on dynamic path planning is based on generation of paths periodically, with reference to each time interval, in an unknown environment. Hossain and Ferdous [26] have explored the application of Bacterial Foraging Optimization (BFO) to determine the shortest feasible path to move a mobile robot from any current position to the target position with moving obstacles in the environment. Montiel et al. [27] have computed optimal paths in environments with static and dynamic obstacles for a mobile robot using a new method called Bacterial Potential Field (BPF), which is a crossover between the Artificial Potential Field (APF) method and a Bacterial Evolutionary Algorithm (BEA). A function segment artificial moment method is proposed by Xu et al. [28]. Different from the existing artificial moment methods, attractive function segments are used for attractive points and the artificial moment motion controller is improved. However, these approaches are unable to clearly address the issues in a dynamic environment, which include varying magnitude of changes at a given moment and subsequent requirement of the modifications based on the current location on the path.

Most of the aforementioned work deal with simple environments, though they address the dynamic nature of the scenes. The problem of searching for a re-plan in a map with obstacles for a mobile robot is significantly simpler than that for a robotized crane with higher DOF. Most of the approaches in the literature fail to address the computational intensity of the construction sites as they deal with either 2D environments or simplistic 3D CAD models. Although very few work have been able to incorporate complex scenes in their algorithmic approach, they suffer from optimality efficiency in their algorithms. It is evident that there is an obvious demand for a CALP system with an effective re-planning decision-making system and an optimal re-planning strategy which will address the dynamic issues of the task of crane lifting path planning in a complex construction site.

The proposed work in the current investigation extends the scope of the MSPGA based CALP system [12], by designing a re-planning module for automatic tower crane lifting in dynamic building environments to achieve optimal re-plans. Dynamic objects in the construction environment are modeled as obstacles. The obstacles are categorized based on their effect on the initial planned path. A Single-level Depth Map (SDM) approach is taken to reduce the huge data set of the 3D BIM model for discrete collision check with the Oriented Bounding Boxes (OBBs) [29] of all the moving components of the crane and continuous collision check with the Analytical Swept Volumes (ASVs) [30] of the crane jib and the lifting target. The obstacles are integrated with the original SDM representation of the environment scene, which is prepared in the pre-processing stage of the collision detection engine. A nodal representation of lifting paths is prepared for tower cranes by developing 4-DOF robotized crane models and a new internal path building strategy. The two main parts of the proposed re-planning architecture are a Decision Support System (DSS) and a Path Re-planner (PRP). A re-planning decision-making algorithm (for the DSS) employing the proximity status of the crane and the target load via multi-level OBBs is designed. A local re-planning strategy (for the PRP) using the MSPGA planner of the CALP system is also introduced in this work to deal with the complex construction environment of real-world building sites.

Section snippets

Model formulation

In real-world construction sites, there are multiple objects (equipment moved by construction workers, vehicles, other cranes etc.) in the scene which may shift their position after the initial lifting plan for a particular building component has been prepared or during execution of the lifting task. As a result, the construction environment is dynamic in nature since it changes with respect to time, either after the initial optimal lifting plan is constructed or during lifting operation itself.

Dynamic re-planning architecture

To address the dynamic situations during automatic lifting, a re-planning mechanism for the tower crane lifting path needs to be designed. Any re-planning mechanism should involve two distinct steps - deciding when to re-plan and strategizing how to re-plan. The CALP system should be able to identify the dynamic scenario and take a decision of re-plan in a methodical manner. This is achieved in the proposed work by designing a Decision Support System (DSS) and a Path Re-planner (PRP),

Case studies

The proposed re-planning module for a tower crane model is applied with a real-world building model obtained from BIM system, for the scenario of obstacles being introduced into the scene after initial lifting plans have been prepared for building components. The 3D building model used is a residential block in Singapore. A 3D CAD model of the hammerhead tower crane Terex SK 415-20 is imported to the CALP system for lifting path planning, re-planning and simulation.

Conclusion

Automatic re-planning is an essential component in automated building construction where cranes have to work in a dynamic and complex 3D environment. There are very few prior arts which deal with crane lifting path re-planning in presence of dynamic objects in the construction process. The present approach proposes a re-planning module for the Computer-Aided Lift Planning system developed in Nanyang Technological University, which is designed on GPU-based MSPGA framework. The scope of work is

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The authors would like to express their thanks to the Housing and Development Board of Singapore, for providing the BIM model of the residential building for the case studies. The authors are also 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.

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