Bipedal robots have long been a focal point of robotics research with an unwavering emphasis on platform stability. Achieving stability necessitates meticulous design considerations at every stage, encompassing resilience against environmental disturbances and the inevitable wear associated with various tasks. In pursuit of these objectives, here, the bipedal L04 Robot is introduced. The L04 Robot employs a groundbreaking approach by compactly enclosing the hip joints in all directions and employing a coupled joint design. This innovative approach allows the robot to attain the traditional 6 degrees of freedom in the hip joint while using only four motors. This design not only enhances energy efficiency and battery life but also safeguards all vulnerable motor reducers. Moreover, the double-slider leg design enables the robot to simulate knee bending and leg height adjustment through leg extension. This simulation can be mathematically modeled as a linear inverted pendulum (LIP), rendering the L04 Robot a versatile platform for research into bipedal robot motion control. A dynamic analysis of the bipedal robot based on this structural innovation is conducted accordingly. The design of motion control laws for forward, backward, and lateral movements are also presented. Both simulation and physical experiments corroborate the excellent bipedal walking performance, affirming the stability and superior walking capabilities of the L04 Robot.

In this paper, we propose a novel algorithm to solve multi-agent task assignment and path finding (MATAPF) problem in the Federated Internet of Things (FIoT). Conventional MATAPF studies mostly take the form of two-phase planning that assigns tasks to the agents, then finds inter-agent collision-free paths to accomplish these tasks. In contrast, we consider the task assignment problem and the path finding problem at the same. The agents can adjust their paths according to the changing task assignment situations such that all the tasks can be finished in the minimum distance cost. This is realized by constructing an automaton model to incorporate the movements of the agents and the execution process of the tasks. An optimal controller is designed to assign each newly issued task to the most appropriate agent and to control all the agents to their target positions safely (without collision and deadlock) and efficiently (in the minimum distance cost) at each time step. Additionally, when we construct the roadmap for multi-agent path finding, we consider the kinematic constraint of each agent. That is, from any position of the roadmap, all its successive positions are physically reachable to all the agents, which can improve the efficiency of the path planning, especially when there exist a large number of agents. As a case study, we then apply this online control scheme to model and control a warehouse automation system, which is a common FIoT application scenario. Simulations in various layouts of the warehouse demonstrate that MATAPF significantly outperforms standard approaches, i.e., solving more problem instances much faster. The effectiveness of the proposed framework has also been tested for a increasing number of agents.

The hitting position and velocity control for table tennis robots have been investigated widely in the literature. However, most of the studies conducted do not consider the opponent’s hitting behaviors, which may reduce hitting accuracy. This paper proposes a new table tennis robot framework that returns the ball based on the opponent’s hitting behaviors. Specifically, we classify the opponent’s hitting behaviors into four categories: forehand attacking, forehand rubbing, backhand attacking, and backhand rubbing. A tailor-made mechanical structure that consists of a robot arm and a two-dimensional slide rail is developed such that the robot can reach large workspaces. Additionally, a visual module is incorporated to enable the robot to capture opponent motion sequences. Based on the opponent’s hitting behaviors and the predicted ball trajectory, smooth and stable control of the robot’s hitting motion can be obtained by applying quintic polynomial trajectory planning. Moreover, a motion control strategy is devised for the robot to return the ball to the desired location. Extensive experimental results are presented to demonstrate the effectiveness of the proposed strategy.

This paper considers modeling and state estimation of timed discrete-event systems (TDESs) with communication delays and losses. In the previous framework, the specified closed-loop system language may contain physically impossible strings, i.e., strings never occur in practice. To exclude these physically impossible strings, this paper presents a new modeling framework for supervisory control of networked TDESs. Specifically, we first analyze how to model and update the observation channel and the control channel under communication delays and losses. We then construct an automaton that explicitly describes the interaction process between the plant and the supervisor over the observation channel and the control channel so that the language of the closed-loop system can be specified. Compared with the existing work, the proposed framework can model the “dynamics” of the closed-loop system more accurately. Under the proposed framework, we further estimate states of the closed-loop system under communication delays and losses. The state estimation is implemented on-the-fly and bases on only the information of observations and controls in history. As an application of the proposed modeling and state estimation approaches, we finally extend the existing approaches to find all the admissible and safe supervisors of networked TDESs.

In the context of networked discrete-event systems (DESs), communication delays and losses exist between the plant and the supervisor for observation and between the supervisor and the actuator for control. In this paper, we first introduce a new framework for supervisory control of networked DESs. Under the introduced framework, we address the state estimation problem for supervisor synthesis of networked DESs with both communication delays and losses. The estimation algorithm considers the effect of the controls imposed on the system. Additionally, the estimation algorithm is based on the control decisions available up to the moment, and all the future control decisions are assumed to be unknowable. Two notions, called “observation channel configuration” for tracking observation delays and losses and “control channel configuration” for tracking control delays and losses, are defined. Then, we introduce an online approach for state estimation of the controlled system. Compared with the existing approach, the proposed approach under the introduced framework can estimate the states of the controlled system more accurately. As an application of the proposed approach, we finally show that the existing methods can be applied to synthesize maximally permissible and safe networked supervisors.