Autonomous underwater vehicles (AUVs) as an efficient underwater exploration means have been used to perform various marine missions. However, limited by the technologies of underwater acoustic communications and intelligent autonomy, the most current and advanced AUVs only perform a limited number of tasks in the small-scale area and the known underwater environment. Therefore, in this paper, a one path planning model was proposed combining the global path planning and the local path planning for the large-scale complex marine environment. More specifically, the B-spline curve was used to represent the smooth path for the requirement of kinematic constraints of AUVs. After considering the various constraints, such as the energy/time consumption, the turning radius limitation, the marine environment, and the ocean current, the path planning was abstractly modeled as a multi-objective optimization model with the time cost, the curvature cost, the map cost, and the ocean current cost. The swarm hyper-heuristic algorithm (SHH) with the online learning ability was proposed to solve this model with real-time performance and stability. The results showed that the proposed online learning SHH algorithm had obvious advantages in terms of time efficiency, stability, and optimal performance compared with the results of two traditional heuristic algorithms, both particle swarm optimization (PSO) and firefly algorithm (FFA). The time efficiency of the online learning SHH algorithm improved at least 20% compared with PSO and FFA.

Conventional manipulators with rigid structures and stiffness actuators have poor flexibility, limited obstacle avoidance capability, and constrained workspace. Some developed flexible or soft manipulators in recent years have the characteristics of infinite degrees of freedom, high flexibility, environmental adaptability, and extended manipulation capability. However, these existing manipulators still cannot achieve the shrinking motion and independent control of specified segments like the animals, which hinders their applications. In this paper, a flexible bio-tensegrity manipulator, inspired by the longitudinal and transversal muscles of octopus tentacles, was proposed to mimic the shrinking behavior and achieve the variable motion patterns of each segment. Such proposed manipulator uses the elastic spring as the backbone, which is driven by four cables and has one variable structure mechanism in each segment to achieve the independent control of each segment. The variable structure mechanism innovatively contains seven lock-release states to independently control the bending and shrinking motion of each segment. After the kinematic modeling and analysis, one prototype of such bionic flexible manipulator was built and the open-loop control method was proposed. Some proof-of-concept experiments, including the shrinking motion, bending motion, and variable structure motion, were carried out by controlling the length of four cables and changing the lock-release states of the variable structure mechanism, which validate the feasibility and validity of our proposed prototype. Meanwhile, the experimental results show the flexible manipulator can accomplish the bending and shrinking motion with the relative error less than 6.8% through the simple independent control of each segment using the variable structure mechanism. This proposed manipulator has the features of controllable degree-of-freedom in each segment, which extend their environmental adaptability, and manipulation capability.

Autonomous underwater vehicles (AUVs) have been widely implemented to explore marine resources or perform marine missions; meanwhile, complex mission planning has been expected to enhance the intelligence level, improve energy efficiency, and expand AUVs' application areas. The mission planning model needs to consider energy consumption, mission quality, and mission quantity of long-term working in the marine environment. In this article, one unified and robust model was proposed based on the above requirements in the complex marine environment to improve the automaticity of AUV. However, solving this mission planning model is a nontrivial problem, especially when high robustness, high efficiency, fast response, and near-optimal results are required. Therefore, we propose a novel hyperheuristic algorithm based on evolutionary strategy (ES-HH). The proposed ES-HH combines a metaheuristic framework with a selection function to evaluate the performance of low-level heuristic operators online. This evolutionary strategy endows the hyperheuristic algorithm with the online learning feature, giving the ES-HH algorithm better computing efficiency, robustness, and near-optimal results. The experiment results show that the ES-HH algorithm can achieve better convergence and higher robustness than other algorithms, such as ant colony optimization and biogeography-based optimization algorithms. Compared with ACO and BBO algorithms, the proposed ES-HH algorithm can improve the mission completion rate by 3.42% and reduce the average energy consumption of a single task by 8.02%.

Jumping locomotion is much more effective than other locomotion means in order to tackle the unstructured and complex environment in research and rescue. Here, a bio-inspired jumping robot with a closed-chain mechanism is proposed to achieve the power amplification during taking-off. Through actuating one variable transmission mechanism to change the transmission ratio, the jumping robot reveals biological characteristics in the phase of posture adjustment when adjusting the height and distance of one jump. The kinematics and dynamics of the simplified jumping mechanism model in one jumping cycle sequence are analysed. A compliant contact model considering nonlinear damping is investigated for jumping performance under different terrain characteristics. The numerical simulation algorithm with regard to solving the dynamical equation is described and simulation results are discussed. Finally, one primary prototype and experiment are described. The experimental results show the distance of jumping in the horizontal direction increases with the increasing gear ratio, while the height of jumping decreases in reverse. The jumping robot can enhance the capability to adapt to unknown cluttered environments, such as those encountered in research and rescue, using this strategy.