Lifelong multiagent path finding (LMAPF) requires continuous task assignment and collision-free path planning for large robot fleets. However, existing LMAPF methods assume synchronous unit-time actions, whereas real robots execute movements asynchronously with nonuniform durations. This mismatch can lead to execution-time collisions and makes frequent replanning challenging in large-scale systems. We study LMAPF with asynchronous actions and present a framework that ensures safety under asynchronous execution and real-time scalability through coordinated path planning and action scheduling, complemented by a simple greedy task allocation. To ensure safety, the path planner eliminates cycle conflicts to prevent structural deadlocks, while a lightweight action scheduler enforces vertex precedence during execution, resolving following conflicts without explicit temporal modeling. To achieve real-time performance, the planner performs partial replanning by reusing the residual paths of nonidle agents, which are converted into a synchronous form through the scheduler’s path resynchronization, enabling consistent and efficient replanning. Experiments in warehouse environments with up to 1000 robots demonstrate that our framework more than doubles throughput compared with state-of-the-art LMAPF methods, while maintaining safe operations under asynchronous actions.

We consider the problem of retrieving a target object from a confined space by two robotic manipulators where overhand grasps are not allowed. If other movable obstacles occlude the target, more than one object should be relocated to clear the path to reach the target object. With two robots, the relocation could be done efficiently by simultaneously performing relocation tasks. However, the precedence constraint between the tasks (e.g, some objects at the front should be removed to manipulate the objects in the back) makes the simultaneous task execution difficult. We propose a coordination method that determines which robot relocates which object so as to perform tasks simultaneously. Given a set of objects to be relocated, the objective is to maximize the number of switches between the robots in performing relocation tasks. Thus, one robot can pick an object in the clutter while the other robot places an object in hand to the outside of the clutter. However, the object to be relocated may not be accessible to all robots, so switching could not always be achieved. Our method is based on the uniform-cost search so the number of switches can be maximized. We also propose a greedy variant whose computation time is shorter. From experiments, we show that our method reduces the completion time of the mission by at least 22.9% (at most 27.3%) compared to the methods with no consideration of switching.

We consider the problem of rearranging objects in a cluttered and confined space using a robotic manipulator. The goal is to retrieve a target object from the clutter where the target is occluded by other objects. In situations where overhand grasps are not allowed, the robot needs to remove some objects to make the target accessible. In the course of removing the objects, the robot also needs to determine the locations to place the removed objects. If the robot can access enough empty spaces around or inside the clutter, the placement of the objects is trivially simple. If empty spaces are scarce, placing objects should be done in a principled way as an incorrect placement would deplete the empty spaces quickly.In this work, we propose a method that solves the problems of what and where to relocate objects inside the clutter to retrieve the target. Previously, there have been several efficient methods proposed that deal with each of the what and where to relocate problems separately. We solve the problems together using a graph structure constructed from an object configuration. Also, the method runs fast so scalable in the number of objects. Compared to a state-of-the-art method, our method reduces task and motion planning time up to 74.9% (at least 56.7%) and has a higher success rate under a short time limit for planning, which is 3 minutes.

We propose a tree search-based planning algorithm for a robot manipulator to rearrange objects and grasp a target in a dense space. We consider environments where tasks cannot be completed with prehensile planning only. As assuming that a manipulator is only allowed to grasp from the top, we aim to minimize the number of rearrangement actions and the total execution time, which affects the efficiency of manipulation. The proposed search algorithm determines the optimal sequence of object rearrangement with prehensile and non-prehensile grasping until grasping a target. For non-prehensile grasping, a heuristic function is employed to model frictions and contacts between objects and a table. Experimental results in a realistic simulated environment show that the proposed algorithm can reduce the number of rearranged obstacles up to 27% and the total execution time up to 15% with 14 objects compared to the previous work.

Studies on the production of emotions have been conducted to create robotic facial expressions. The reported methodologies for generating emotions for a robot have focused on recognizing a user’s emotions using devices, such as cameras and microphones, and then generating the reactive emotions of a robot according to the user’s emotions. However, these methodologies may have some limitations in delivering emotions in the robot that match the robot’s utterances to users. In this paper, we propose a methodology for producing robotic emotions suitable for a robot’s utterances based on texts so that it can be applied to various fields such as robotic dialogue and reading services. To produce human-like emotions in a robot, our methodology applied patterns of human emotional changes as well as the resilience theory that humans have an ability to recover their emotional states considering their own personality over time. We measured the performance of the model for analyzing texts and observed that there is a linear correlation between the predicted emotions and the annotated ones from humans. Furthermore, when we carried out the experiments based on scenarios, our methodology could produce human-like patterns of emotional changes and the robot could recover its emotional state on its own.

In this article, we present a task and motion planning method for retrieving a target object from clutter using a robotic manipulator. We consider dense and cluttered environments where some objects must be removed in order to retrieve the target without collisions. To ensure a successful execution, the interplay between task planning (what to remove in what order) and motion planning (how to remove) is crucial. Thus, the task and motion planning approach combining a symbolic task planner and a geometric motion planner becomes one of the major paradigms in manipulation planning. However, motion planning in dense clutter often leads to frequent failures, so repetitive task replanning is inevitable. Although symbolic task planners are general and domain-independent, they do not scale; so we need an efficient task planner specialized for dense clutter for fast completion of tasks. We propose a polynomial-time task planner for object manipulation in clutter that can be combined with any motion planner. We aim to optimize the number of pick-and-place actions which often determines the efficiency of object manipulation tasks. We consider common situations that could occur in clutter: 1) all object locations are known, 2) some hidden objects are revealed while relocating some front objects, and 3) the target is hidden until some objects are removed. Our method is shown to reduce the number of pick-and-place actions compared to baseline methods (e.g., at least 28.0% of reduction in a known static environment with 20 objects). We also deploy the proposed method to two physical robots with vision systems to show that our method can solve real-world problems.

We present an algorithm determining where to relocate objects inside a cluttered and confined space while rearranging objects to retrieve a target object. Although methods that decide what to remove have been proposed, planning for the placement of removed objects inside a workspace has not received much attention. Rather, removed objects are often placed outside the workspace, which incurs additional laborious work (e.g., motion planning and execution of the manipulator and the mobile base, perception of other areas). Some other methods manipulate objects only inside the workspace but without a principle so the rearrangement becomes inefficient.In this work, we consider both monotone (each object is moved only once) and non-monotone arrangement problems which have shown to be $\mathcal{N}\mathcal{P}$-hard. Once the sequence of objects to be relocated is given by any existing algorithm, our method aims to minimize the number of pick-and-place actions to place the objects until the target becomes accessible. From extensive experiments, we show that our method reduces the number of pick-and-place actions and the total execution time (the reduction is up to 23.1% and 28.1% respectively) compared to baseline methods while achieving higher success rates.

This paper presents a task and motion planning (TAMP) framework for a robotic manipulator in order to retrieve a target object from clutter. We consider a configuration of objects in a confined space with a high density so no collision-free path to the target exists. The robot must relocate some objects to retrieve the target without collisions. For fast completion of object rearrangement, the robot aims to optimize the number of pick-and-place actions which often determines the efficiency of a TAMP framework.We propose a task planner incorporating motion planning to generate executable plans which aims to minimize the number of pick-and-place actions. In addition to fully known and static environments, our method can deal with uncertain and dynamic situations incurred by occluded views. Our method is shown to reduce the number of pick-and-place actions compared to baseline methods (e.g., at least 28.0% of reduction in a known static environment with 20 objects).

We present an algorithm that produces a plan for relocating obstacles in order to grasp a target in clutter by a robotic manipulator without collisions. We consider configurations where objects are densely populated in a constrained and confined space. Thus, there exists no collision-free path for the manipulator without relocating obstacles. Since the problem of planning for object rearrangement has shown to be NP-hard, it is difficult to perform manipulation tasks efficiently which could frequently happen in service domains (e.g., taking out a target from a shelf or a fridge). Our proposed planner employs a collision avoidance scheme which has been widely used in mobile robot navigation. The planner determines an obstacle to be removed quickly in real time. It also can deal with dynamic changes in the configuration (e.g., changes in object poses). Our method is shown to be complete and runs in polynomial time. Experimental results in a realistic simulated environment show that our method improves up to 31% of the execution time compared to other competitors.