This work proposes an online task-scheduling method using mixed-integer programming for a multi-tasking problem regarding a dual-arm cooking robot in a controlled environment. Given each task’s processing time, their location in the working space, dependency, the required number of arms, and the kinematic constraints of the dual-arm robot, the proposed optimization algorithm can produce a feasible solution to scheduling the cooking order for each task and for each associated arms so that the total cooking time and the total moving distance for each arm are minimized. We use a subproblem optimization strategy in which the number of tasks to be planned is divided into several groups instead of planning all tasks at the same time. By doing so, the planning time can be significantly decreased, making the algorithm practical for online implementation. The feasibility of our optimization method and the effectiveness of the subproblem optimization strategy were verified through simulated experiments consisting of 30 to 120 tasks. The results showed that our strategy is advantageous in terms of computation time and makespan for large problems.

This study presents an approach to autonomous navigation for wheeled robots, combining radar-based dynamic obstacle detection with a BiGRU-based deep reinforcement learning (DRL) framework. Using filtering and tracking algorithms, the proposed system leverages radar sensors to cluster object points and track dynamic obstacles, enhancing precision by reducing noise and fluctuations. A BiGRU-enabled DRL model is introduced, allowing the robot to process sequential environmental data and make informed decisions in dynamic and unpredictable environments, achieving collision-free paths and reaching the goal. Simulation and experimental results validate the proposed method’s efficiency and adaptability, highlighting its potential for real-world applications in dynamic scenarios.

This paper presents an alarm system and teleoperation control framework, comparing ROS 1 and ROS 2 within a local network to mitigate the risk of robots failing to reach their goals during autonomous navigation. Such failures can occur when the robot moves through irregular terrain, becomes stuck on small steps, or approaches walls and obstacles without maintaining a safe distance. These issues may arise due to a combination of technical, environmental, and operational factors, including inaccurate sensor data, sensor blind spots, localization errors, infeasible path planning, and an inability to adapt to unexpected obstacles. The system integrates a web-based graphical interface developed using frontend frameworks and a joystick for real-time monitoring and control of the robot’s localization, velocity, and proximity to obstacles. The robot is equipped with RGB-D and tracking cameras, a 2D LiDAR, and odometry sensors, providing detailed environmental data. The alarm system provides sensory feedback through visual alerts on the web interface and vibration alerts on the joystick when the robot approaches walls, faces potential collisions with objects, or loses stability. The system is evaluated in both simulation (Gazebo) and real-world experiments, where latency is measured and sensor performance is assessed for both ROS 1 and ROS 2. The results demonstrate that both systems can operate effectively in real time, ensuring the robot’s safety and enabling timely operator intervention. ROS 2 offers lower latency for LiDAR and joystick inputs, making it advantageous over ROS 1. However, camera latency is higher, suggesting the need for potential optimizations in image data processing. Additionally, the platform supports the integration of additional sensors or applications based on user requirements.

A passive aligning strategy for the assembly task of a ribbon cable is presented using a sliding surface gripper. Once the gripper grasps the ribbon cable, the sliding surface of the gripper pulls the cable in one direction, and at the end of the pulling process, the box-shaped structure of the fingertip of the gripper passively aligns the cable to a predictable pose. The relationship between the alignment speed of the cable on the sliding surface and the grasping force was modeled and verified by experiments. The maximum position error of the ribbon cable connector aligned by the proposed method was 0.7 mm, which was smaller than the mating tolerance between typical male and female connectors. The mating of the connectors was successfully conducted with an industrial robot whose repeatability is less than a millimeter. The entire assembly process of ribbon cable connectors, including visual recognition of the ribbon cable pose, grasping and aligning of the ribbon cable, and mating a pair of cable connectors, was successfully implemented.

This paper proposes an efficient robot training method to navigate environments with static and dynamic obstacles and reach their goal autonomously using deep rein-forcement learning algorithms. Previous methods have focused on specific scenarios, such as crowded environments. However, in practice, the variety of scenarios with static and dynamic obstacles tends to make the real robotic system fail or be restricted. In this work, the training is performed in six scenarios per episode, whereas traditional methods only consider one scenario. Several neural networks are trained and compared based on the following metrics: success rate, collision rate, uncomfortable rate, travel time, and average travel distance. In addition, we conducted Gazebo simulations using ROS and experimental tests across four scenarios to demonstrate that our approach has a better performance compared to previous studies. The results show a greatly enhanced robot's ability to act in various situations, as shown in the following link: https://youtu.be/mvkMFZjlaQo