Inspired by the behaviour of animal populations in nature, we propose a novel exploration algorithm based on Lévy flight (LF) and artificial potential field (APF). The agent is extended to the swarm level using the APF method through the LF search environment. Virtual leaders generate moving steps to explore the environment through the LF mechanism. To achieve collision-free movement in an unknown constrained environment, a swarm-following mechanism is established, which requires the agents to follow the virtual leader to carry out the LF. The proposed method, combining the advantages of LF and APF which achieve the effect of flocking in an exploration environment, does not rely on complex sensors for environment labelling, memorising, or huge computing power. Agents simply perform elegant and efficient search behaviours as natural creatures adapt to the environment and change formations. The method is especially suitable for the camouflaged flocking exploration environment of bionic robots such as flapping drones. Simulation experiments and real-world experiments on E-puck2 robots were conducted to evaluate the effectiveness of the proposed LF-APF algorithm.

Since it is difficult to accurately track reference trajectories under the condition of stride constraints for an unmanned hexapod robot moving with rhythmic gait, an omnidirectional tracking strategy based on model predictive control and real-time replanning is proposed in this paper. Firstly, according to the characteristic that the stride dominates the rhythmic motion of an unmanned multi-legged robot, a body-level omnidirectional tracking model is established. Secondly, a quantification method of limb’s stretch and yaw constraints described by motion stride relying on a tripod gait is proposed, and then, a body-level accurate tracking controller based on constrained predictive control is designed. Then, in view of the low tracking efficiency of the robot under the guidance of common reference stride, a solution strategy of variable stride period and a real-time replanning scheme of reference stride are proposed based on the limb constraints and the integral mean, which effectively avoid the tracking deviation caused by the guidance of constant reference strides. Finally, the effectiveness and practicability of the proposed control strategy are demonstrated through the comparative analysis and simulation test of a hexapod robot WelCH with omnidirectional movement ability to continuously track the directed curve and the undirected polyline trajectory.

Deep reinforcement learning (DRL) has significantly advanced online path planning for unmanned aerial vehicles (UAVs). Nonetheless, DRL-based methods often encounter reduced performance when dealing with unfamiliar scenarios. This decline is mainly caused by the presence of non-causal and domain-specific elements within visual representations, which negatively impact the learning of policies. Present techniques generally depend on predefined augmentation or regularization methods intended to direct the model toward identifying causal and domain-invariant components, thereby enhancing the model’s ability to generalize. However, these manually crafted approaches are intrinsically constrained in their coverage and do not consider the entire spectrum of possible scenarios, resulting in less effective performance in new environments. Unlike prior studies, this work prioritizes representation learning and presents a novel method for causal representation disentanglement. The approach successfully distinguishes between causal and non-causal elements in visual data. By concentrating on causal aspects during the policy learning phase, the impact of non-causal factors is minimized, thereby improving the generalizability of DRL models. Experimental results demonstrate that our technique achieves reliable navigation and effective collision avoidance in unseen scenarios, surpassing state-of-the-art deep reinforcement learning algorithms.

The visual servoing of manipulators is challenged by two main problems: the singularity of the inverse Jacobian and the physical constraints of a manipulator. In order to overcome the singularity issue, this paper presents a novel approach for image-based visual servoing (IBVS), which converts the propagation of errors in the image plane into the conduction of virtual forces using the principle of virtual work. This approach eliminates the need for Jacobian inversion computations and prevents matrix inversion singularity. To tackle physical constraints, reverse thinking is adopted to derive the function of the upper and lower bounds of the joint velocity on the joint angle. This enables the proposed method to configure the physical constraints of the robot in a more intuitive manner. To validate the effectiveness of the proposed method, an eye-in-hand system based on UR5 in VREP, as well as a physical robot, were established.