Summary: Conventional simultaneous localization and mapping (SLAM) has concentrated on two-dimensional (2D) map building. To adapt it to urgent search and rescue (SAR) environments, it is necessary to combine the fast and simple global 2D SLAM and three-dimensional (3D) objects of interest (OOIs) local sub-maps. The main novelty of the present work is a method for 3D OOI reconstruction based on a 2D map, thereby retaining the fast performances of the latter. A theory is established that is adapted to a SAR environment, including the object identification, exploration area coverage (AC), and loop closure detection of revisited spots. Proposed for the first is image optical flow calculation with a 2D/3D fusion method and RGB-D (red, green, blue + depth) transformation based on Joblove–Greenberg mathematics and OpenCV processing. The mathematical theories of optical flow calculation and wavelet transformation are used for the first time to solve the robotic SAR SLAM problem. The present contributions indicate two aspects: (i) mobile robots depend on planar distance estimation to build 2D maps quickly and to provide SAR exploration AC; (ii) 3D OOIs are reconstructed using the proposed innovative methods of RGB-D iterative closest points (RGB-ICPs) and 2D/3D principle of wavelet transformation. Different mobile robots are used to conduct indoor and outdoor SAR SLAM. Both the SLAM and the SAR OOIs detection are implemented by simulations and ground-truth experiments, which provide strong evidence for the proposed 2D/3D reconstruction SAR SLAM approaches adapted to post-disaster environments.

Collaborative control of a dual-arm robot refers to collision avoidance and working together to accomplish a task. To prevent the collision of two arms, the control strategy of a robot arm needs to avoid competition and to cooperate with the other one during motion planning. In this paper, a dual-arm deep deterministic policy gradient (DADDPG) algorithm is proposed based on deep reinforcement learning of multi-agent cooperation. Firstly, the construction method of a replay buffer in a hindsight experience replay algorithm is introduced. The modeling and training method of the multi-agent deep deterministic policy gradient algorithm is explained. Secondly, a control strategy is assigned to each robotic arm. The arms share their observations and actions. The dual-arm robot is trained based on a mechanism of “rewarding cooperation and punishing competition”. Finally, the effectiveness of the algorithm is verified in the Reach, Push, and Pick up simulation environment built in this study. The experiment results show that the robot trained by the DADDPG algorithm can achieve cooperative tasks. The algorithm can make the robots explore the action space autonomously and reduce the level of competition with each other. The collaborative robots have better adaptability to coordination tasks.

To balance the diagonal gait of a quadruped robot, a dynamic balance control method is presented to improve the stability of the quadruped robot by adjusting its foot position. We set up a trunk-based coordinate system and a hip-based local coordinate system for the quadruped robot, established the kinematics equation of the robot, and designed a reasonable initial diagonal gait through the spring inverted pendulum model. The current trunk posture of the quadruped robot is obtained by collecting the data of its pitch and roll angle, and the foot position is predicted according to the current posture and initial gait of the quadruped robot. To reduce the impact of one leg landing on the ground and increase the stability of the quadruped robot, we adjust the landing point of the robot according to the landing time difference between the diagonal legs. The proposed method can adjust the body in such scenarios as planar walking and lateral impact resistance. It can reduce the disturbance during the robot motion and make the robot move smoothly. The validity of this method is verified by simulation experiments.

Based on dimensional memory mechanism and adaptive elite population, this paper proposes a satisfactory and efficient artificial bee colony algorithm (DMABC_elite) to solve optimization problems and train artificial neural networks (ANN). DMABC_elite proposes the concept of adaptive elite population that changes dynamically with the search process, and modifies the search equations for employed and onlooker bee phases on this basis. In addition, a dimensional memory mechanism has been introduced that allows multi-dimensional updates, which improves exploitation and speeds up convergence. Next, a new selection strategy and a Lévy flight-based solution-generating method are introduced in the scout bee phase to enhance the global search ability. Finally, the performance of DMABC_elite on two different problem groups is analyzed experimentally. On the one hand, DMABC_elite is evaluated using 22 classical benchmark functions with different dimensions and CEC 2013 test functions. Compared with basic ABC and nine state-of-the-art ABC variants, DMABC_elite achieved better results, ranking first in all 10-, 30-and 100-dimensional tests across 22 classical benchmark functions and 30-dimensional tests across CEC 2013 test functions. On the other hand, DMABC_elite is compared with traditional backpropagation-based algorithms and other ABC variants when training seven different ANNs. The results show that DMABC_elite is efficient and competitive in training ANNs.