Scientific exploration of challenging planetary analog environments with a team of legged robots

Venus Flytrap-Inspired Data-Center-Free Fast-Responsive Soft Robots Enabled by 2D Ni3(HITP)2 MOF and Graphite

The rapid and responsive capabilities of soft robots in perceiving, assessing, and reacting to environmental stimuli are highly valuable. However, many existing soft robots, designed to mimic humans and other higher animals, often rely on data centers for the modulation of mechanoelectrical transduction and electromechanical actuation. This reliance significantly increases system complexity and time delays. Herein, drawing inspiration from Venus flytraps, a soft robot employing a power modulation strategy is presented for active stimulus reaction, eliminating the need for a data center. This robot achieves mechanoelectrical transduction through Ni3(2,3,6,7,10,11-hexaiminotriphenylene)2 (Ni3(HITP)2) metal-organic framework (MOF) with an ultralow time delay (256 ns) and electromechanical actuation via graphite. The Joule heating effect in graphite is effectively modulated by Ni3(HITP)2 before and after the presence of pressure, thus enabling the stimulus reaction of soft robots. As demonstrated, three soft robots are created: low-level edge tongue robots, Venus flytrap robots, and high-level nerve-center-controlled dragonfly robots. This power modulation strategy inspires designs of edge soft robots and high-level robots with a human-like effective fusion of conditioned and unconditioned reflexes.

Optimization method for human-robot command combinations of hexapod robot based on multi-objective constraints

Due to the heavy burden on human drivers when remotely controlling hexapod robots in complex terrain environments, there is a critical need for robot intelligence to assist in generating control commands. Therefore, this study proposes a mapping process framework that generates a combination of human-robot commands based on decision target values, focusing on the task of robot intelligence assisting drivers in generating human-robot command combinations. Furthermore, human-robot state constraints are quantified as geometric constraints on robot motion and driver fatigue constraints. By optimizing and filtering the feasible set of human-robot commands based on human-robot state constraints, instruction combinations are formed and recommended to the driver in real-time, thereby enhancing the efficiency and safety of human-machine coordination. To validate the effectiveness of the proposed method, a remote human-robot collaborative driving control system based on wearable devices is designed and implemented. Experimental results demonstrate that drivers utilizing the human-robot command recommendation system exhibit significantly improved robot walking stability and reduced collision rates compared to individual driving.

Biomimetic lizard robot for adapting to Martian surface terrain

The exploration of the planet Mars still is a top priority in planetary science. The Mars surface is extensively covered with soil-like material. Current wheeled rovers on Mars have been occasionally experiencing immobilization instances in unexpectedly weak terrains. The development of Mars rovers adaptable to these terrains is instrumental in improving exploration efficiency. Inspired by locomotion of the desert lizard, this paper illustrates a biomimetic quadruped robot with structures of flexible active spine and toes. By accounting for spine lateral flexion and its coordination with four leg movements, three gaits of tripod, trot and turning are designed. The motions corresponding to the three gaits are conceptually and numerically analyzed. On the granular terrains analog to Martian surface, the gasping forces by the active toes are estimated. Then traversing tests for the robot to move on Martian soil surface analog with the three gaits were investigated. Moreover, the traversing characteristics for Martian rocky and slope surface analog are analyzed. Results show that the robot can traverse Martian soil surface analog with maximum forward speed 28.13 m s-1turning speed 1.94° s-1and obstacle height 74.85 mm. The maximum angle for climbing Martian soil slope analog is 28°, corresponding slippery rate 76.8%. It is predicted that this robot can adapt to Martian granular rough terrain with gentle slopes.

Residual dynamics learning for trajectory tracking for multi-rotor aerial vehicles

This paper presents a technique to model the residual dynamics between a high-level planner and a low-level controller by considering reference trajectory tracking in a cluttered environment as an example scenario. We focus on minimising residual dynamics that arise due to only the kinematical modelling of high-level planning. The kinematical modelling is sufficient for such scenarios due to safety constraints and aggressive manoeuvres that are difficult to perform when the environment is cluttered and dynamic. We used a simplified motion model to represent the motion of the quadrotor when formulating the high-level planner. The Sparse Gaussian Process Regression-based technique is proposed to model the residual dynamics. Recently proposed Data-Driven MPC is targeting aggressive manoeuvres without considering obstacle constraints. The proposed technique is compared with Data-Driven MPC to estimate the residual dynamics error without considering obstacle constraints. The comparison results yield that the proposed technique helps to reduce the nominal model error by a factor of 2 on average. Further, the proposed complete framework was compared with four other trajectory-tracking approaches in terms of tracking the reference trajectory without colliding with obstacles. The proposed approach outperformed the others with less flight time without losing computational efficiency.