Robot navigation in crowded environments has recently benefited from advances in deep reinforcement learning (DRL) approaches. However, it still presents a challenge to designing socially compliant robot behavior. Avoiding collisions and the difficulty of predicting human behavior are crucial and challenging tasks while the robot navigates in a congested social environment. To address this issue, this study proposes a dynamic warning zone that creates a circular sector around humans based on the step length and speed of humans. To properly comprehend human behavior and keep a safe distance between the robot and the humans, warning zones are implemented during the robot’s training using deep enforcement learning techniques. In addition, a short-distance goal is established to help the robot efficiently reach the goal through a reward function that penalizes it for going away from the goal and rewards it for advancing towards it. The proposed model is tested on three state-of-the-art methods: collision avoidance with deep reinforcement learning (CADRL) , long short-term memory (LSTM-RL), and social attention with reinforcement learning (SARL). The suggested method is tested in the Gazebo simulator and the real world with a robot operating system (ROS) in three scenarios. The first scenario involves a robot attempting to reach a goal in free space. The second scenario uses static obstacles, and the third involves humans. The experimental results demonstrate that the model performs better than previous methods and leads to safe navigation in an efficient time.

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 paper proposes a design methodology for a six-wheeled rover that adapt to different stairs and maintain its stability based on the robot’s parameters, the kinematics constraints, the maximum height, and the minimum length of the step required to climb up and down. We also propose an emergency controller to prevent falls during the climb up or down using the contact angle measurement between wheel–ground by a laser scanner sensor on each side of the robot. Thus, the geometry terrain information and the wheel contact loss detection with the ground can be obtained. This loss of contact with the ground is a determining factor in an emergency case where the robot’s stability is at risk. Therefore, the controllers kick in to regain the wheel contact with the step, preventing the robot from falling. Simulations and experimental results when the robot climbs up and down stairs demonstrate the ability to react to possible falls.

Accurate dynamic model is critical for collaborative robots to achieve satisfactory performance in model-based control or other applications such as dynamic simulation and external torque estimation. Such dynamic models are frequently restricted to identifying important system parameters and compensating for nonlinear terms. Friction, as a primary nonlinear element in robotics, has a significant impact on model accuracy. In this paper, a reliable dynamic friction model, which incorporates the influence of temperature fluctuation on the robot joint friction, is utilized to increase the accuracy of identified dynamic parameters. First, robot joint friction is investigated. Extensive test series are performed in the full velocity operating range at temperatures ranging from 19 °C to 51 °C to investigate friction dependency on joint module temperature. Then, dynamic parameter identification is performed using an inverse dynamics identification model and weighted least squares regression constrained to the feasible space, guaranteeing the optimal solution. Using the identified friction model parameters, the friction torque is computed for measured robot joint velocity and temperature. Friction torque is subtracted from the measured torque, and a non-friction torque is used to identify dynamic parameters. Finally, the proposed notion is validated experimentally on the Indy7 collaborative robot manipulator, and the results show that the dynamic model with parameters identified using the proposed method outperforms the dynamic model with parameters identified using the conventional method in tracking measured torque, with a relative improvement of up to 70.37%.

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.