Changjoo Nam

Assignment algorithms for modeling resource contention and interference in multi-robot task-allocation

We consider optimization of the multi-robot task-allocation problem when the overall performance of the team need not be a standard sum-of-cost model. We introduce a generalization that allows for the additional cost incurred by resource contention to be treated in a straightforward manner. In this variant, robots may choose one of shared resources to perform a task, and interference may be modeled as occurring when multiple robots use the same resource. We investigate the general NP-hard problem and instances where the interference results in linear or convex penalization functions. We propose an exact algorithm for the general problem and polynomial-time algorithms for the other problems. The exact algorithm finds an optimal assignment in a reasonable time on small instances. The other two algorithms quickly find an optimal and a high-quality approximation assignment even if a problem is of considerable size. In contrast to conventional approximation methods, our algorithm provides the performance guarantee.

VPass: Algorithmic compass using vanishing points in indoor environments

In this paper, we propose an algorithmic compass that yields the heading information of a mobile robot using the vanishing point in indoor environments: VPass. With the VPass, a loop-closing effect (which is a significant reduction of errors by revisiting a known place through a loop) can be achieved even for a loop-less environment. From the implementation point of view, the VPass is useful because it can be appended upon any existing navigation algorithms. Experimental results show that the VPass yields accurate angle information in indoor environments for paths with lengths of around 200m.

When to do your own thing: Analysis of cost uncertainties in multi-robot task allocation at run-time

We address the problem of finding the optimal assignment of tasks to a team of robots when the associated costs may vary, which arises when robots deal with uncertain or dynamic situations. We detail how to compute a sensitivity analysis that characterizes how much costs may change before optimality is violated. Using this analysis, robots are able to avoid unnecessary re-assignment computations and reduce global communication. First, given a model of how costs may evolve, we develop an algorithm to partition the robots into independent cliques, each of which maintains global optimality by communicating only amongst themselves. Second, we propose a method for computing the worst-case sub-optimality if robots persist with the initial assignment, performing no further communication/computation. Lastly, we develop an algorithm that assesses whether cost changes affect the optimality through an escalating succession of local checks. Experiments show that the methods reduce the degree of centralization needed by a multi-robot system.

Local path planning scheme for car-like vehicle's shortest turning motion using geometric analysis

This paper deals with a path planning problem for turning motion of a car-like vehicle. We propose a turning method which finds a curvature continuous optimal path between two positions for a car-like vehicle.

Assignment Algorithms for Modeling Resource Contention in Multirobot Task Allocation

This paper considers multirobot task allocation problems where the estimated costs for performing tasks are interrelated, and the overall team objective need not be a standard sum-of-costs (or utilities) model, enabling straightforward treatment of the additional costs incurred by resource contention. In the model we introduce, a team may choose one of a set of shared resources to perform a task (e.g., several routes to reach a destination), and interference is modeled when multiple robots use the same resource. We show that the general problem is NP-hard, and investigate specialized subinstances with particular cost structures. For the general problem, we describe an exact algorithm which finds an optimal assignment in a reasonable time on small instances. Aiming at larger problems, we turn two particular subinstances, introducing an two algorithms that find assignments quickly even for problems of considerable size, the first being optimal, the second being an approximation algorithm but also producing high-quality solutions with bounded suboptimality.

Development of robotic-crane based automatic construction system for steel structures of high-rise buildings

In this paper, we address a new technique for automatic construction of steel structure in high-rise buildings termed RCA system (Robotics & Crane based Automated Construction System). RCA system can be divided into four core systems: 1) Monitoring and control system, 2) Material assembly system, 3) Beam assembly system, 4) Construction Factory (CF) system. Through our research, we expect that this new technique will increase the construction efficiency and it will alleviate the man power shortage problem.

Development of minimal grasper: Preliminary result of a simple and flexible enveloping grasper

In this paper, we propose a new design of a flexible enveloping grasper for pick and place tasks with the low complexity in manipulation and task planning for the purpose of practical use in the near future. Flexible material for the grasper has many advantageous characteristics inherently including robustness against manipulation errors and the ability to increase contact area with a grasped object and the grasping force. Compliance of the grasper material also contributes to reduction in complexity of the processes such as the force control, sensor-motor coordination, and manipulation by self-adaptation. Two properties, flexibility and compliance, mentioned above help the proposed grasper minimize the internal forces in a passive manner and achieve the successful force distribution with self-adaptivity when performing enveloping grasping. In order to demonstrate our work, we have constructed 2 different prototypes of flexible enveloping grasper. Experimental results validate robust performances of the proposed grasper.

Bundling Policies for Sequential Stochastic Tasks in Multi-robot Systems

This paper studies multi-robot task allocation in settings where tasks are revealed sequentially for an infinite or indefinite time horizon, and where robots may execute bundles of tasks. The tasks are assumed to be synergistic so efficiency gains accrue from performing more tasks together. Since there is a tension between the performance cost (e.g., fuel per task) and the task completion time, a robot needs to decide when to stop collecting tasks and to begin executing its whole bundle. This paper explores the problem of optimizing bundle size with respect to the two objectives and their trade-off. Based on qualitative properties of any multi-robot system that bundles sequential stochastic tasks, we introduce and explore an assortment of simple bundling policies. Our experiments examine how these policies perform in a warehouse automation scenario, showing that they are efficient compared to baseline policies where robots do not bundle tasks strategically.

A practical 2D/3D SLAM using directional patterns of an indoor structure

This paper presents a practical two-dimensional (2D)/three-dimensional (3D) simultaneous localization and mapping (SLAM) algorithm using directional features for ordinary indoor environments; this algorithm is adaptable to various conditions, computationally inexpensive, and accurate enough to use for practical applications. The proposed algorithm uses odometry acquired from other sensors or other algorithms as the initial estimate and the directional features of indoor structures as landmarks. The directional features can only correct the rotation error of the odometry. However, we show that the greater part of the translation error of the odometry can also be corrected when the directional features are detected at almost positions accurately. In that case, there is no need to use other kinds of features to correct translation error. The directions of indoor structures have two advantages as landmarks. First, the extraction of them is not affected by obstacles. Second, the number of them is small regardless of the size of the building. Because of these advantages, the proposed SLAM algorithm shows robustness for parameters and lightweight properties. From extensive experiments with 2D/3D datasets taken from different buildings, we show the practicality of the proposed algorithm. We also demonstrate that the 2D algorithm runs in real time on a low-end smartphone.

Predicting trust in human control of swarms via inverse reinforcement learning

In this paper, we study the model of human trust where an operator controls a robotic swarm remotely for a search mission. Existing trust models in human-in-the-loop systems are based on task performance of robots. However, we find that humans tend to make their decisions based on physical characteristics of the swarm rather than its performance since task performance of swarms is not clearly perceivable by humans. We formulate trust as a Markov decision process whose state space includes physical parameters of the swarm. We employ an inverse reinforcement learning algorithm to learn behaviors of the operator from a single demonstration. The learned behaviors are used to predict the trust level of the operator based on the features of the swarm.