arXiv subjects

Tomohiko Jimbo

Publications and source records attributed to Tomohiko Jimbo.

15 recordsLinked to original sources

arXiv2025

Cooperative Transportation using Multiple Single-Rotor Robots and Decentralized Control for Unknown Payloads

Cooperative transportation via multiple aerial robots has the potential to support various payloads and reduce the chances of them being dropped. Furthermore, autonomously controlled robots render the system scalable with respect to the payload. In this study, a cooperative transportation system was developed using rigidly attached single-rotor robots, and a decentralized controller was proposed to guarantee asymptotic stability of the error dynamics for unknown strictly positive real systems. A feedback controller was used to transform unstable systems into strictly positive real ones considering the shared attachment positions. First, the cooperative transportation of unknown payloads with different shapes larger than the carrier robots was investigated via numerical simulations. Second, cooperative transportation of an unknown payload (with a weight of approximately 2.7 kg and maximum length of 1.6 m) was demonstrated using eight robots, even under robot failure. Finally, the proposed system was shown to be capable of carrying an unknown payload, even if the attachment positions were not shared, that is, even if asymptotic stability was not strictly guaranteed.

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arXiv2025

Autonomous Cooperative Transportation System involving Multi-Aerial Robots with Variable Attachment Mechanism

Cooperative transportation by multi-aerial robots has the potential to support various payloads and improve failsafe against dropping. Furthermore, changing the attachment positions of robots according payload characteristics increases the stability of transportation. However, there are almost no transportation systems capable of scaling to the payload weight and size and changing the optimal attachment positions. To address this issue, we propose a cooperative transportation system comprising autonomously executable software and suitable hardware, covering the entire process, from pre-takeoff setting to controlled flight. The proposed system decides the formation of the attachment positions by prioritizing controllability based on the center of gravity obtained from Bayesian estimations with robot pairs. We investigated the cooperative transportation of an unknown payload larger than that of whole carrier robots through numerical simulations. Furthermore, we performed cooperative transportation of an unknown payload (with a weight of about 3.2 kg and maximum length of 1.76 m) using eight robots. The proposed system was found to be versatile with regard to handling unknown payloads with different shapes and center-of-gravity positions.

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arXiv2025

Imitation-regularized Optimal Transport on Networks: Provable Robustness and Application to Logistics Planning

Transport systems on networks are crucial in various applications, but face a significant risk of being adversely affected by unforeseen circumstances such as disasters. The application of entropy-regularized optimal transport (OT) on graph structures has been investigated to enhance the robustness of transport on such networks. In this study, we propose an imitation-regularized OT (I-OT) that mathematically incorporates prior knowledge into the robustness of OT. This method is expected to enhance interpretability by integrating human insights into robustness and to accelerate practical applications. Furthermore, we mathematically verify the robustness of I-OT and discuss how these robustness properties relate to real-world applications. The effectiveness of this method is validated through a logistics simulation using automotive parts data.

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arXiv2025

Reinforcement Learning of Multi-robot Task Allocation for Multi-object Transportation with Infeasible Tasks

Multi-object transport using multi-robot systems has the potential for diverse practical applications such as delivery services owing to its efficient individual and scalable cooperative transport. However, allocating transportation tasks of objects with unknown weights remains challenging. Moreover, the presence of infeasible tasks (untransportable objects) can lead to robot stoppage (deadlock). This paper proposes a framework for dynamic task allocation that involves storing task experiences for each task in a scalable manner with respect to the number of robots. First, these experiences are broadcasted from the cloud server to the entire robot system. Subsequently, each robot learns the exclusion levels for each task based on those task experiences, enabling it to exclude infeasible tasks and reset its task priorities. Finally, individual transportation, cooperative transportation, and the temporary exclusion of tasks considered infeasible are achieved. The scalability and versatility of the proposed method were confirmed through numerical experiments with an increased number of robots and objects, including unlearned weight objects. The effectiveness of the temporary deadlock avoidance was also confirmed by introducing additional robots within an episode. The proposed method enables the implementation of task allocation strategies that are feasible for different numbers of robots and various transport tasks without prior consideration of feasibility.

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arXiv2024

Insect-Scale Tailless Robot with Flapping Wings: A Simple Structure and Drive for Yaw Control

Insect-scale micro-aerial vehicles, especially lightweight, flapping-wing robots, are becoming increasingly important for safe motion sensing in spatially constrained environments such as living spaces. However, yaw control using flapping wings is fundamentally more difficult than using rotating wings. In this study, an insect-scale, tailless robot with four paired tilted flapping wings (weighing 1.52 g) was fabricated to enable simultaneous control of four states, including yaw angle. The controllability Gramian was derived to quantify the controllability of the fabricated configuration and to evaluate the effects of the tilted-wing geometry on other control axes. This robot benefits from the simplicity of directly driven piezoelectric actuators without transmission, and lift control is achieved simply by changing the voltage amplitude. However, misalignment or modeling errors in lift force can cause offsets. Therefore, an adaptive controller was designed to compensate for such offsets. Numerical experiments confirm that the proposed controller outperforms a conventional linear quadratic integral controller under unknown offset conditions. Finally, in a tethered and controlled flight experiment, yaw drift was suppressed by combining the tilted-wing arrangement with the proposed controller.

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arXiv2024

Decentralized Control for Heterogeneous Battery Energy Storage System

Battery energy storage systems (BESSs) are essential for stable power supply in renewable energy systems that can operate in all weather. Future BESSs will be massive and pluggable with several heterogeneous batteries. In this paper, a novel decentralized control method for a heterogeneous BESS is proposed, in which each battery autonomously operates based on its characteristics. First, a control method that uses only one broadcast signal for each type of battery is proposed. Second, the asymptotic stability of the tracking error is proved. Third, numerical simulations confirm that the proposed control method has robust tracking performance of the total electric power to the demanded power when some batteries fail and are detached from the system. Last, in order to suppress degradation of battery, equalization of the state of charge is achieved for each type of battery without communication among the batteries.

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arXiv2024

Task-priority Intermediated Hierarchical Distributed Policies: Reinforcement Learning of Adaptive Multi-robot Cooperative Transport

Multi-robot cooperative transport is crucial in logistics, housekeeping, and disaster response. However, it poses significant challenges in environments where objects of various weights are mixed and the number of robots and objects varies. This paper presents Task-priority Intermediated Hierarchical Distributed Policies (TIHDP), a multi-agent Reinforcement Learning (RL) framework that addresses these challenges through a hierarchical policy structure. TIHDP consists of three layers: task allocation policy (higher layer), dynamic task priority (intermediate layer), and robot control policy (lower layer). Whereas the dynamic task priority layer can manipulate the priority of any object to be transported by receiving global object information and communicating with other robots, the task allocation and robot control policies are restricted by local observations/actions so that they are not affected by changes in the number of objects and robots. Through simulations and real-robot demonstrations, TIHDP shows promising adaptability and performance of the learned multi-robot cooperative transport, even in environments with varying numbers of robots and objects. Video is available at https://youtu.be/Rmhv5ovj0xM

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arXiv2023

Operational modal analysis of under-determined system based on Bayesian CP decomposition

Modal parameters such as natural frequencies, modal shapes, and the damping ratio are useful to understand structural dynamics of mechanical systems. Modal parameters need to be estimated under operational conditions for use in structural health monitoring. Therefore, operational modal analysis (OMA) without input signals has been proposed to easily extract modal parameters under operational conditions. Recently, OMA for under-determined systems with more active modes than measurement outputs has been investigated to reduce the number of sensors. This study proposes the OMA framework for under-determined systems based on Bayesian CP (CANDECOMP/PARAFAC) decomposition of second-order statistical data. The proposed method enables us to extract the modal parameters from under-determined systems without tuning the number of active modes, because the rank of the tensor data corresponding to the number of active modes is automatically determined via Bayesian inference. The effectiveness of this method is demonstrated using artificial vibration data of a mass-spring system under operational and under-determined conditions.

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arXiv2022

Learning Locally, Communicating Globally: Reinforcement Learning of Multi-robot Task Allocation for Cooperative Transport

We consider task allocation for multi-object transport using a multi-robot system, in which each robot selects one object among multiple objects with different and unknown weights. The existing centralized methods assume the number of robots and tasks to be fixed, which is inapplicable to scenarios that differ from the learning environment. Meanwhile, the existing distributed methods limit the minimum number of robots and tasks to a constant value, making them applicable to various numbers of robots and tasks. However, they cannot transport an object whose weight exceeds the load capacity of robots observing the object. To make it applicable to various numbers of robots and objects with different and unknown weights, we propose a framework using multi-agent reinforcement learning for task allocation. First, we introduce a structured policy model consisting of 1) predesigned dynamic task priorities with global communication and 2) a neural network-based distributed policy model that determines the timing for coordination. The distributed policy builds consensus on the high-priority object under local observations and selects cooperative or independent actions. Then, the policy is optimized by multi-agent reinforcement learning through trial and error. This structured policy of local learning and global communication makes our framework applicable to various numbers of robots and objects with different and unknown weights, as demonstrated by numerical simulations.

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arXiv2022

Resilience Evaluation of Entropy Regularized Logistic Networks with Probabilistic Cost

The demand for resilient logistics networks has increased because of recent disasters. When we consider optimization problems, entropy regularization is a powerful tool for the diversification of a solution. In this study, we proposed a method for designing a resilient logistics network based on entropy regularization. Moreover, we proposed a method for analytical resilience criteria to reduce the ambiguity of resilience. First, we modeled the logistics network, including factories, distribution bases, and sales outlets in an efficient framework using entropy regularization. Next, we formulated a resilience criterion based on probabilistic cost and Kullback--Leibler divergence. Finally, our method was performed using a simple logistics network, and the resilience of the three logistics plans designed by entropy regularization was demonstrated.

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arXiv2022

Deep reinforcement learning of event-triggered communication and consensus-based control for distributed cooperative transport

In this paper, we present a solution to a design problem of control strategies for multi-agent cooperative transport. Although existing learning-based methods assume that the number of agents is the same as that in the training environment, the number might differ in reality considering that the robots' batteries may completely discharge, or additional robots may be introduced to reduce the time required to complete a task. Therefore, it is crucial that the learned strategy be applicable to scenarios wherein the number of agents differs from that in the training environment. In this paper, we propose a novel multi-agent reinforcement learning framework of event-triggered communication and consensus-based control for distributed cooperative transport. The proposed policy model estimates the resultant force and torque in a consensus manner using the estimates of the resultant force and torque with the neighborhood agents. Moreover, it computes the control and communication inputs to determine when to communicate with the neighboring agents under local observations and estimates of the resultant force and torque. Therefore, the proposed framework can balance the control performance and communication savings in scenarios wherein the number of agents differs from that in the training environment. We confirm the effectiveness of our approach by using a maximum of eight and six robots in the simulations and experiments, respectively.

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arXiv2021

Tracking Control foe Multi-Agent Systems Using Broadcast Signals Based on Positive Realness

Broadcast control is one of decentralized control methods for networked multi-agent systems. In this method, each agent does not communicate with the others, and autonomously determines its own action using only the same signal sent from a central controller. Therefore, it is effective for systems with numerous agents or no-communication between agents. However, it is difficult to manage the stochastic action process of agents considering engineering applications. This paper proposes a decentralized control such that agents autonomously select the deterministic actions. Firstly, a non-linear controller with a binary output of each agent including 0 is introduced in order to express stop actions autonomously when the target is achieved. The asymptotic stability to the target is proved. Secondly, the controller can adjust the tendency of actions in order to make it easier to manage the actions. Thirdly, the controller is extended to that with a continuous output in order to reduce the tracking error to the target and the output vibration. Finally, the effectiveness of the proposed control is verified by numerical experiments.

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arXiv2021

Development of global optimal coverage control using multiple aerial robots

Coverage control has been widely used for constructing mobile sensor network such as for environmental monitoring, and one of the most commonly used methods is the Lloyd algorithm based on Voronoi partitions. However, when this method is used, the result sometimes converges to a local optimum. To overcome this problem, game theoretic coverage control has been proposed and found to be capable of stochastically deriving the optimal deployment. From a practical point of view, however, it is necessary to make the result converge to the global optimum deterministically. In this paper, we propose a global optimal coverage control along with collision avoidance in continuous space that ensures multiple sensors can deterministically and smoothly move to the global optimal deployment. This approach consists of a cut-in algorithm based on neighborhood importance of measurement and a modified potential method for collision avoidance. The effectiveness of the proposed algorithm has been confirmed through numerous simulations and some experiments using multiple aerial robots.

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arXiv2021

Robust shape estimation with false-positive contact detection

We propose a means of omni-directional contact detection using accelerometers instead of tactile sensors for object shape estimation using touch. Unlike tactile sensors, our contact-based detection method tends to induce a degree of uncertainty with false-positive contact data because the sensors may react not only to actual contact but also to the unstable behavior of the robot. Therefore, it is crucial to consider a robust shape estimation method capable of handling such false-positive contact data. To realize this, we introduce the concept of heteroscedasticity into the contact data and propose a robust shape estimation algorithm based on Gaussian process implicit surfaces (GPIS). We confirmed that our algorithm not only reduces shape estimation errors caused by false-positive contact data but also distinguishes false-positive contact data more clearly than the GPIS through simulations and actual experiments using a quadcopter.

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arXiv2021

Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport

In this paper, we explore a multi-agent reinforcement learning approach to address the design problem of communication and control strategies for multi-agent cooperative transport. Typical end-to-end deep neural network policies may be insufficient for covering communication and control; these methods cannot decide the timing of communication and can only work with fixed-rate communications. Therefore, our framework exploits event-triggered architecture, namely, a feedback controller that computes the communication input and a triggering mechanism that determines when the input has to be updated again. Such event-triggered control policies are efficiently optimized using a multi-agent deep deterministic policy gradient. We confirmed that our approach could balance the transport performance and communication savings through numerical simulations.

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