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Smart manufacturing has boosted the wide application of mobile robots in robotic cells for automated material delivery. However, the mismatching between machine production process and robot movement process causes extensive energy waste. Nevertheless, most existing robotic job-shop scheduling (RJSP) studies mainly focus on minimising makespan but overlook the low energy efficiency problem faced by robotic cells. Motivated by the importance of green smart manufacturing, in this study, we innovatively propose to achieve robotic cell energy saving through coordinating the machine production process and robot movement process. Specifically, both machines and the mobile robot can flexibly adjust operating speeds with a V-scale speed framework. Two novel energy-efficient RJSP approaches (i.e. the RJSP-E and the RJSP-EM) are thus proposed. The RJSP-E focuses on minimising energy consumption, while the RJSP-EM simultaneously considers makespan (i.e. productivity) and energy consumption. Through computational experiments, the RJSP-E demonstrates superior performances in reducing energy consumption (15% on average), at a loss of productivity (20% on average). On the other hand, the RJSP-EM can select the most suitable energy-saving operating speeds without much sacrifice in productivity. Notably, the RJSP-EM can reduce energy consumption by a mean of 10% even without increasing makespan. The RJSP-EM also demonstrates higher solution efficiency. [ABSTRACT FROM AUTHOR]

Commercial automation systems for small- and medium-sized laboratories, including research environments, are often complex to use. For liquid handling systems (LHS), development is required not only for the robot's movements but also for adapting the bioanalytical method to the automated system. This study investigates whether a more human-like automation strategy—using a robotic system (RS)—is more suitable for research laboratories than a professional automation approach utilizing a commercial automated LHS. We conducted a series of measurements for protein determination using a Bradford assay manually, with a fully automated LHS, and with our human-like RS. Although the hand-like RS approach requires more than twice the time of the LHS, it achieved the best standard deviation in this setup (RS = 0.5, manual = 0.71, LHS = 0.86). Due to the low limit of detection (LOD) and limit of quantification (LOQ), most protein samples could be quantified with the RS (samples below LOQ = 9.7%, LOD = 0.23; LOQ = 0.25) compared to manual (samples below LOQ = 28.8%, LOD = 0.24; LOQ = 0.26) and the LHS (samples below LOQ = 36.1%, LOD = 0.27; LOQ = 0.31). In another time-dependent enzymatic assay test, the RS achieved results comparable to the manual method and the LHS, although the required time could be a constraint for short incubation times. Our results demonstrate that a more hand-like automation system closely models the manual process, leading easier to accurate bioanalytical results. We conclude that such a system could be more suitable for typical research environments than a complex LHS. [ABSTRACT FROM AUTHOR]

Cable-driven serial robots have emerged with high potential for wide applications due to their compact size and low inertia properties. However, developing this type of robot encounters a motion coupling issue that the movement of one joint leads to the motion of other joints, resulting in complex control. In this paper, we proposed a novel approach for motion decoupling based on a noncircular pulley. The length change of the driving cable caused by the motion coupling problem is resolved by using the noncircular pulley. The calculation process of the profile for the noncircular pulley is illustrated in detail. An optimization process based on the brute force method is presented to identify the optimal parameters to minimize the compensation error. A cable-driven serial robot based on the decoupling method is prototyped for assessments. Experiments are conducted to evaluate the performance of the proposed motion decoupling method. The results reveal that the proposed method can effectively resolve the motion coupling issue by maintaining almost constant cable length with a maximum accumulative error only as 0.086 mm, demonstrating the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Dynamical system (DS) based motion planning offers collision-free motion, with closed-loop reactivity thanks to their analytical expression. It ensures that obstacles are not penetrated by reshaping a nominal DS through matrix modulation, which is constructed using continuously differentiable obstacle representations. However, state-of-the-art approaches may suffer from local minima induced by non-convex obstacles, thus failing to scale to complex, high-dimensional joint spaces. On the other hand, sampling-based Model Predictive Control (MPC) techniques provide feasible collision-free paths in joint-space, yet are limited to quasi-reactive scenarios due to computational complexity that grows cubically with space dimensionality and horizon length. To control the robot in the cluttered environment with moving obstacles, and to generate feasible and highly reactive collision-free motion in robots' joint space, we present an approach for modulating joint-space DS using sampling-based MPC. Specifically, a nominal DS representing an unconstrained desired joint space motion to a target is locally deflected with obstacle-tangential velocity components navigating the robot around obstacles and avoiding local minima. Such tangential velocity components are constructed from receding horizon collision-free paths generated asynchronously by the sampling-based MPC. Notably, the MPC is not required to run constantly, but only activated when the local minima is detected. The approach is validated in simulation and real-world experiments on a 7-DoF robot demonstrating the capability of avoiding concave obstacles, while maintaining local attractor stability in both quasi-static and highly dynamic cluttered environments. [ABSTRACT FROM AUTHOR]

This study primarily focuses on design, mathematically model and simulate a novel two wheel-legged hybrid robot called W-Leg Jumping robot, which has a unique ability to overcome step-like obstacles efficiently. In general, in transformable wheel-leg robot studies, the leg and wheel structure perform their movements in an interdependent manner. However, in this study, it is aimed to design a robot in which the leg and wheel structure can move independently of each other and to develop a robot that can easily overcome obstacles on flat surfaces with the wheel mode and with the leg mode. The robot can fold its legs hidden within the wheels and deploy its two degree of freedom (DoF) legs when it detects step-like obstacles. This mechanism allows the robot to overcome an obstacle with a height of twice the radius of the robot's open/close mechanism of the legs, along with the two-dimensional kinematic and dynamic analyzes of the legs, are presented in detail within the scope of this study proportional-integral-derivative (PID) controller is designed to control the joint angles of the legs. The reference angle values to be followed according to the height of the obstacle are determined using artificial neural network (ANN). Additionally, motion simulations of the robot are conducted for four different obstacle heights (20, 30, 40, and 50 cm). As a result of the PID controller, when exceeding the highest obstacle of 50 cm, the average absolute joint angular tracking error is max. 1.8829°, average tracking error max. 0.265 s and max. [ABSTRACT FROM AUTHOR]

Learning sensorimotor trajectories through flexible neural representations is fundamental for robots as it facilitates the building of motor skills as well as equipping them with the ability to represent the world as predictable temporal events. Recent advances in deep learning led to the development of powerful learning from demonstration (LfD) systems such as Conditional Neural Movement Primitives (CNMPs). CNMPs can robustly represent skills as movement distributions and allow them to be 'recalled' by conditioning the movement on a few observation points. In this study, we focus on improving CNMPs to achieve a higher resource economy by adopting a divide-and-conquer approach. We propose a novel neural architecture called Coupled CNMP (C-CNMP), that couples the latent spaces of a pair of CNMPs that splits a given sensorimotor trajectory into segments whose learning is undertaken by smaller sub-networks. Therefore, each sub-network needs to deal with a less complex trajectory making the learning less resource-hungry. With systematic simulations on a controlled trajectory data set, we show that the overhead brought by the coupling introduced in our model is well offset by the resource and performance gain obtained. To be specific, with CNMP model as the baseline, it is shown that the proposed model is able to learn to generate trajectories in the data set with a lower trajectory error measured as the mean absolute difference between the generated trajectory and the ground truth. Importantly, our model can perform well with relatively limited resources, i.e., with less number of neural network parameters compared to the baseline. To show that the findings from the controlled data set well-transfer to robot data, we use robot joint data in an LfD setting and compare the learning performance of the proposed model with the baseline model at equal complexity levels. The simulation experiments show that with also the robot joint data, the proposed model, C-CNMP, learns to generate the joint trajectories with significantly less error than the baseline model. Overall, our study improves the state of the art in sensorimotor trajectory learning and exemplifies how divide-and-conquer approaches can benefit deep learning architectures for resource economy. [ABSTRACT FROM AUTHOR]

The efforts in soft materials actuators that can generate driving deformation under external energy stimulation have promoted the rapid development in miniature sized robotics. Recently, soft actuators with continuous and autonomous actuation behavior under constant stimulation are highly sought after. Light‐fueled self‐oscillators based on self‐shadowing effect triggered by their light‐induced actuation deformation provide an effective scheme. However, grand challenges remain in light‐driven self‐actuators that are multi‐directional controllable and can operate in low power density in air. Here, this study reports a MXene‐based multilayer actuator that generates self‐sustained robotic motion upon low light intensity– 50 mW cm−2 (half Sun). The actuator can self‐track the light sources and continuously oscillate while the incident angle is changed. The self‐sustained actuator exhibits high adaptation to types of light sources, including inherence and coherence light sources with different wavelengths. Diverse applications are demonstrated, including autonomous signal transmitter based on beam reflection, self‐propelled sailboat, light‐to‐mechanical motor, and soft robots with biomimetic movements. The results offer new insights for light‐fueled self‐oscillators with multi‐directional controllability and low‐intensity requirement and its potential application prospect. [ABSTRACT FROM AUTHOR]

The development of micro-robots based on bionic motion has garnered increasing attention with the advancement of bionic technology. These micro-robots can be employed in various tasks, such as military applications, disaster search and rescue, in vivo drug delivery, minimally invasive surgery, and tumor treatment. Robot joint motion is commonly achieved through actuators, which encompass motor, hydraulic, or pneumatic components that often carry excessive weight. To achieve the goal of miniaturizing robotic mechanisms, innovative actuators need to be developed. This study focuses on designing a four-limbed micro-robot that utilizes piezoelectric actuators. Automatic Dynamic Analysis of the Mechanical System is employed to investigate the kinematic aspects of the micro-robot’s motion, and finite element analysis is also used to obtain the resonant characteristics of the limbs actuated by piezoelectric bimorphs. The results indicate that the gait pattern of a piezoelectric robot changes according to the walking strategy and the robot dimension. The forward travel speed varies depending on the gait pattern, and adjustments in gait design can reduce the up/down oscillation and right/left offset. The robot’s travel speed increases with both trunk and limb length, although longer thigh and leg lengths might lead to greater offset and vertical oscillation. The resonant frequency and mode shapes change with the limb structure and affect the limb motion style. By adjusting the driving frequency of the bimorph actuators, the gait pattern can be manipulated. The information presented in this study contributes to a deeper understanding of micro-robot design. [ABSTRACT FROM AUTHOR]

Robots with parallel-link structures are generally considered useful for achieving high-speed motion; however, they are typically large in size. Robot size can be reduced by using a serial-link structure. However, their moving parts, typically having a large mass or moment of inertia, are considered unsuitable for high-speed motion. High-speed motion performance can be improved by avoiding mounting actuators on the moving parts using some method to transmit the drive force or motion. In this letter, we propose a model in which a non-pulley musculoskeletal robot, previously studied by us, is modified to avoid mounting actuators on the movable parts. We demonstrate that the robot can be controlled to achieve the target posture using this modified model. We also discuss the feasibility of high-speed motion of a prototype robot using muscular forces computed for several motion patterns based on the minimum muscle-tension change model. [ABSTRACT FROM AUTHOR]

CPG makes a great contribution to the control of gait motions in hexapod robots. To manage the gait movements of hexapod robots more efficiently and with lower energy consumption, this work introduced a Spiking-CPG(SCPG) neural network as the bionic control system for these robots. The system integrated Hopf oscillators with biologically inspired LIF neurons, which communicated through sparse spiking signals, in a ring topology. It was composed of six groups, each containing 2 000 LIF neurons, interconnected to form the network. This SCPG control system could produce common hexapod robot gaits such as the wave, tetrapod, and tripod gaits. It facilitated rapid, smooth, and stable transitions between motion gaits by adjusting phase difference parameters, and could dynamically adjust the required frequency and amplitude, exhibiting excellent robustness by quickly recovering from external disturbances. It controlled a simple 3D hexapod robot model on the Webots platform. It developed the robot by transforming the SCPG signal output through joint mapping functions, validating the motion stability of the designed hexapod robot and the feasibility and effectiveness of the SCPG control scheme. Finally, it ported the SCPG neural network controller proposed to Intel's Loihi chip, showing that it has high execution speed and lower energy consumption, indicating a promising prospect for application in the motion control of hexapod robots. [ABSTRACT FROM AUTHOR]

Path planning with multiple evaluation metrics makes the motion of a robot realistic, but the contradiction between the metrics and the lack of global search and obstacle avoidance capabilities increases the difficulty of obtaining the optimization solution. To solve these problems, an enhanced ant colony algorithm (EACA) with an obstacle avoidance strategy is proposed in this article. First, the path planning model is constructed, and strict movement rules are designed. Secondly, the EACA with global search, balancing the contradiction between metrics, is designed. The dynamic regulation of pheromone concentration and the mechanism of fluctuating pheromone distribution are explored, heuristic information is optimized and the path planning effect is enhanced. Finally, a new mechanism of away-from-obstacles is proposed as the obstacle avoidance strategy, which ensures a reasonable safe distance. Comparative simulations on several different maps validate the performance of EACA with the obstacle avoidance strategy for planning robot movement paths. [ABSTRACT FROM AUTHOR]

This work tackles the tracking control problem of robotic manipulators where the robot dynamics contains uncertain parameters and joint velocity measurements are not available. Specifically when the robotic manipulator is required to perform a periodic task repetitively, as in most industrial applications, a repetitive learning controller is proposed that does not require joint velocity measurements and can compensate the uncertainties of the robot dynamical parameters and additive disturbances caused due to the periodic joint motion. The proposed solution is achieved via the use of a novel learning component in the controller design in conjunction with a novel model‐free joint velocity observer design. The stability of the closed‐loop system and the convergence of both the joint position tracking error and the joint velocity observation error to the origin are guaranteed via Lyapunov based arguments. Experimental results performed on a 2 degree of freedom robot manipulator are presented to demonstrate the performance of the proposed observer–controller couple. [ABSTRACT FROM AUTHOR]

Human-Robot Collaboration (HRC) is key to achieving the flexible automation required by the mass personalization trend, especially towards human-centric intelligent manufacturing. Nevertheless, existing HRC systems suffer from poor task understanding and poor ergonomic satisfaction, which impede empathetic teamwork skills in task execution. To overcome the bottleneck, a Mixed Reality (MR) and visual reasoning-based method is proposed in this research, providing mutual-cognitive task assignment for human and robotic agents' operations. Firstly, an MR-enabled mutual-cognitive HRC architecture is proposed, with the characteristic of monitoring Digital Twins states, reasoning co-working strategies, and providing cognitive services. Secondly, a visual reasoning approach is introduced, which learns scene interpretation from the visual perception of each agent's actions and environmental changes to make task planning strategies satisfying human–robot operation needs. Lastly, a safe, ergonomic, and proactive robot motion planning algorithm is proposed to let a robot execute generated co-working strategies, while a human operator is supported with intuitive task operation guidance in the MR environment, achieving empathetic collaboration. Through a demonstration of a disassembly task of aging Electric Vehicle Batteries, the experimental result facilitates cognitive intelligence in Proactive HRC for flexible automation. [ABSTRACT FROM AUTHOR]

Navigating mobile robots in crowded environments poses a significant challenge and is essential for the coexistence of robots and humans in future intelligent societies. As a pragmatic data-driven approach, deep reinforcement learning (DRL) holds promise for addressing this challenge. However, current DRL-based navigation methods have possible improvements in understanding agent interactions, feedback mechanism design, and decision foresight in dynamic environments. This paper introduces the model inductive bias enhanced deep reinforcement learning (MIBE-DRL) method, drawing inspiration from a fusion of data-driven and model-driven techniques. MIBE-DRL extensively incorporates model inductive bias into the deep reinforcement learning framework, enhancing the efficiency and safety of robot navigation. The proposed approach entails a multi-interaction network featuring three modules designed to comprehensively understand potential agent interactions in dynamic environments. The pedestrian interaction module can model interactions among humans, while the temporal and spatial interaction modules consider agent interactions in both temporal and spatial dimensions. Additionally, the paper constructs a reward system that fully accounts for the robot's direction and position factors. This system's directional and positional reward functions are built based on artificial potential fields (APF) and navigation rules, respectively, which can provide reasoned evaluations for the robot's motion direction and position during training, enabling it to receive comprehensive feedback. Furthermore, the incorporation of Monte-Carlo tree search (MCTS) facilitates the development of a foresighted action strategy, enabling robots to execute actions with long-term planning considerations. Experimental results demonstrate that integrating model inductive bias significantly enhances the navigation performance of MIBE-DRL. Compared to state-of-the-art methods, MIBE-DRL achieves the highest success rate in crowded environments and demonstrates advantages in navigation time and maintaining a safe social distance from humans. [ABSTRACT FROM AUTHOR]

With the advancement of underwater biomimetic robots, many kinds of underwater snake robots have been developed to perform various marine inspection and maintenance operations. This paper introduces an innovative biomimetic extension to underwater robotic vehicles, an underwater rigid-soft snake robot, which is operated by a combination of rigid propulsion modules and soft joint modules. The essential point is how to jointly mathematically model soft joints and rigid modules with different physical property. The Constant Curvature assumption is widely used in modeling of soft module robots. However, the limitation of this assumption is that it can't accurately describe the deformation of soft robots when performing dynamic tasks. We propose a methodology, rigid-soft unified model, for kinematic and dynamic modeling by viewing soft joint as flexible universal joint. The motion analysis of the robot under the influence of water flow in different postures is completed. Furthermore, we develop a closed loop hierarchical trajectory controller for the snake robot. Especially in the part of dynamic control system, we compare the tracking performance of different controllers. [ABSTRACT FROM AUTHOR]

Service robots are of paramount importance in both industrial and residential environments. Nevertheless, these robots frequently encounter obstacles in the form of perilous elements present in their surroundings, which may result in their malfunction and hinder their optimal performance. Hence, autonomous robot applications heavily rely on coverage path planning algorithms, which facilitate the execution of area coverage operations in a streamlined and economical manner. The article presents a coverage path planning algorithm for service robots that is based on a backtracking procedure. The proposed method attempts to strike a balance between the safety of the cleaning automaton and the extent of coverage. Following straight-forward instructions, the mobile robot (MR) initially generates a spiral or zigzag trajectory. It examines each position for possible backtracking locations. When obstacles and visited positions obstruct the path, the improved A* algorithm is utilized to return to known unvisited regions. Subsequently, the robot proceeds to sweep the regions until no remaining back-tracking sites remain. Simulation of the method on the Robot Operating System (ROS) verifies that it is possible to restrict the movement of high-risk robots. [ABSTRACT FROM AUTHOR]

Soft robots typically involve manual assembly of core hardware components like actuators, sensors, and controllers. This increases fabrication time and reduces consistency, especially in small-scale soft robots. We present a scalable monolithic fabrication method for millimeter-scale soft-rigid hybrid robots, simplifying the integration of core hardware components. Actuation is provided by soft-foldable polytetrafluoroethylene film-based actuators powered by ionic fluid injection. The desired motion is encoded by integrating a mechanical controller, comprised of rigid-flexible materials. The robot's motion can be self-sensed using an ionic resistive sensor by detecting electrical resistance changes across its body. Our approach is demonstrated by fabricating three distinct soft-rigid hybrid robotic modules, each with unique degrees of freedom: translational, bending, and roto-translational motions. These modules connect to form a soft-rigid hybrid continuum robot with real-time shape-sensing capabilities. We showcase the robot's capabilities by performing object pick-and-place, needle steering and tissue puncturing, and optical fiber steering tasks. This work introduces a fabrication method for mechanically controllable, self-sensing soft-rigid hybrid robots. Translational, bending, and roto-translational modules are designed and assembled as a continuum robot with real-time shape-sensing. [ABSTRACT FROM AUTHOR]

In recent years, the development of robots for agro-industrial applications, such as the cultivation of Solanum tuberosum potatoes, has aroused the interest of the academic and scientific communities. This is due, at least in part, to the complexity of modeling and robustly controlling some dynamics inherent to nonlinear behaviors normally attributed to the different technologies associated with the movement of these autonomous vehicles and their nonholonomic constraints. The different nonlinear dynamics of mobile robots are usually represented by state-space models. However, given some equilibrium and stability characteristics, the implementation of effective controllers for the robust parametric tracking and variation problem requires techniques that allow the operability of robots around regions of stable equilibrium. Feedback linearization control is one such technique that attempts to mathematically eliminate nonlinear expressions from the plant model. However, this technique requires an observable and controllable mathematical model. If there is some relationship between the model inputs and a controlled output that allows the relative degree of the control law to be determined, the controller design and implementation are posed as a linear issue. Flat filters developed from the generalized proportional integral control approach are an alternative that could facilitate the design of controllers for these linearized systems. From these flat filters, it is possible to obtain the transfer function of a controller without relying on the derivatives of the system output. This work proposes the design of a controller via exact linearization and its equivalent flat filter for a robot inspector of the soil resource of S. tuberosum crops in the department of Cundinamarca, Colombia. The actuator motion constraints resulted in a robot with two degrees of mobility and one non-holonomic constraint. Numerical validation of this system suggests that it can be an effective solution to the problem of tracking control at changing references by providing a system capable of navigating through crop rows. The results suggest correct tracking for linear and circular trajectories. However, the control lacks the ability to track spiral-type trajectories. [ABSTRACT FROM AUTHOR]

Trajectory planning of robotic manipulators in complex environments involves generating smooth and collision-free paths, and key aspects to consider include dynamic environment perception, path planning, trajectory smoothing and optimization, and obstacle avoidance. This paper discusses different algorithms and finally proposes a fusion of the improved Dung Beetle Optimizer (IDBO) algorithm using chaotic mapping, sinusoidal random mutation, and nonlinear convergence strategies with the bidirectional Rapidly exploring Random Tree (RRT) algorithm to achieve manipulator trajectory planning and minimum jerk optimization trajectory. In the experiment, the IDBO algorithm was compared with other heuristic algorithms. The path obtained was shortened by 9.18% on average, and the calculation time was shortened by 33.81% on average. Then, the paper explored Cartesian coordinate space trajectory planning and joint space trajectory planning when the robot was working in 3D space, and verified the superiority of the latter. In controlling the movement of the robot, by combining the minimum angle jerk planning and the bidirectional RRT obstacle avoidance algorithm to drive the joint angle parameters obtained by the spatial trajectory planning, the trajectory of the robot manipulator can be smoother, more efficient, and avoid obstacles in complex 3D space. This research helps improve the performance, safety, and technical support of robots, and is of great research significance. [ABSTRACT FROM AUTHOR]

Biological motions of native muscle tissues rely on the nervous system to interface movement with the surrounding environment. The neural innervation of muscles, crucial for regulating movement, is the fundamental infrastructure for swiftly responding to changes in body tissue requirements. This study introduces a bioelectronic neuromuscular robot integrated with the motor nervous system through electrical synapses to evoke cardiac muscle activities and steer robotic motion. Serving as an artificial brain and wirelessly regulating selective neural activation to initiate robot fin motion, a wireless frequency multiplexing bioelectronic device is used to control the robot. Frequency multiplexing bioelectronics enables the control of the robot locomotion speed and direction by modulating the flapping of the robot fins through the wireless motor innervation of cardiac muscles. The robots demonstrated an average locomotion speed of ~0.52 ± 0.22 millimeters per second, fin-flapping frequency up to 2.0 hertz, and turning locomotion path curvature of ~0.11 ± 0.04 radians per millimeter. These systems will contribute to the expansion of biohybrid machines into the brain-to-motor frontier for developing autonomous biohybrid systems capable of advanced adaptive motor control and learning. Editor's summary: Typically, biohybrid machines built from cardiac muscle cells have relied on the involuntary, contractile actuation of the cells to continuously propel the robot. However, without the nervous system, this rhythmic pumping action could not be adjusted or controlled. To imbue control, Tetsuka et al. developed a biohybrid swimmer with a neurocardiac muscle junction. Acting as an artificial brain, a frequency multiplexing wireless system was used to stimulate the motor neurons, which in turn activated the cardiac muscle cells. The robot's speed could be controlled by modulating the frequency and pulse duration of the electrical signal, and direction was altered by transmitting signals to the left and right fins independently. —Melisa Yashinski [ABSTRACT FROM AUTHOR]

Legged soccer robots present a significant challenge in robotics owing to the need for seamless integration of perception, manipulation, and dynamic movement. While existing models often depend on external perception or static techniques, our study aims to develop a robot with dynamic and untethered capabilities. We have introduced a motion planner that allows the robot to excel in dynamic shooting and dribbling. Initially, it identifies and predicts the position of the ball using a rolling model. The robot then pursues the ball, using a novel optimization-based cycle planner, continuously adjusting its gait cycle. This enables the robot to kick without stopping its forward motion near the ball. Each leg is assigned a specific role (stance, swing, pre-kick, or kick), as determined by a gait scheduler. Different leg controllers were used for tailored tiptoe trajectory planning and control. We validated our approach using real-world penalty shot experiments (5 out of 12 successful), cycle adjustment tests (11 out of 12 successful), and dynamic dribbling assessments. The results demonstrate that legged robots can overcome onboard capability limitations and achieve dynamic mobility and manipulation. [ABSTRACT FROM AUTHOR]

The motion control system of a lower-limb exoskeleton rehabilitation robot (LLERR) is designed to assist patients in lower-limb rehabilitation exercises. This research designed a motion controller for an LLERR-based on the Twin Delayed Deep Deterministic policy gradient (TD3) algorithm to control the lower-limb exoskeleton for gait training in a staircase environment. Commencing with the establishment of a mathematical model of the LLERR, the dynamics during its movement are systematically described. The TD3 algorithm is employed to plan the motion trajectory of the LLERR's right-foot sole, and the target motion curve of the hip (knee) joint is deduced inversely to ensure adherence to human physiological principles during motion execution. The control strategy of the TD3 algorithm ensures that the movement of each joint of the LLERR is consistent with the target motion trajectory. The experimental results indicate that the trajectory tracking errors of the hip (knee) joints are all within 5°, confirming that the LLERR successfully assists patient in completing lower-limb rehabilitation training in a staircase environment. The primary contribution of this study is to propose a non-linear control strategy tailored for the staircase environment, enabling the planning and control of the lower-limb joint motions facilitated by the LLERR. [ABSTRACT FROM AUTHOR]

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The artificial potential field algorithm has been widely applied to mobile robots and robotic arms due to its advantage of enabling simple and efficient path planning in unknown environments. However, solving the local minimum problem is an essential task and is still being studied. Among current methods, the technique using the virtual hill concept is reliable and suitable for real-time path planning because it does not create a new local minimum and provides lower complexity. However, in the previous study, the shape of the obstacles was not considered in determining the robot's direction at the moment it is trapped in a local minimum. For this reason, longer or blocked paths are sometimes selected. In this study, we propose an enhanced virtual hill algorithm to reduce errors in selecting the driving direction and improve the efficiency of robot movement. In the local minimum area, a dead-end algorithm is also proposed that allows the robot to return without entering deeply when it encounters a dead end. [ABSTRACT FROM AUTHOR]

This paper introduces a compact end-effector ankle rehabilitation robot (CEARR) system for addressing ankle range of motion (ROM) rehabilitation. The CEARR features a bilaterally symmetrical rehabilitation structure, with each side possessing three degrees of freedom (DOF) driven by three independently designed actuators. The working intervals of each actuator are separated by a series connection, ensuring they operate without interference to accommodate the dorsiflexion/plantarflexion (DO/PL), inversion/eversion (IN/EV), and adduction/abduction (AD/AB) DOF requirements for comprehensive ankle rehabilitation. In addition, we integrated an actuator and foldable brackets to accommodate patients in varied postures. We decoded the motor intention based on the surface electromyography (sEMG) and torque signals generated by the subjects' ankle joints in voluntary rehabilitation. Besides, we designed a realtime voluntary-triggered control (VTC) strategy to enhance the rehabilitation effect, in which the root mean square (RMS) of sEMG was utilized to trigger and adjust the CEARR rehabilitation velocity support. We verified the consistency of voluntary movement with CEARR rehabilitation support output for four healthy subjects on a nonlinear sEMG signal with an R2 metric of approximately 0.67. We tested the consistency of triggering velocity trends with a linear torque signal for one healthy individual with an R2 metric of approximately 0.99. [ABSTRACT FROM AUTHOR]

Untethered magnetic soft robots show great potential for biomedical and small-scale micromanipulation applications due to their high flexibility and ability to cause minimal damage. However, most current research on these robots focuses on marine and reptilian biomimicry, which limits their ability to move in unstructured environments. In this work, we design a quadruped soft robot with a magnetic top cover and a specific magnetization angle, drawing inspiration from the common locomotion patterns of quadrupeds in nature and integrating our unique actuation principle. It can crawl and tumble and, by adjusting the magnetic field parameters, it adapts its locomotion to environmental conditions, enabling it to cross obstacles and perform remote transportation and release of cargo. [ABSTRACT FROM AUTHOR]

This article aims to address the in-orbit assembly needs of truss structures in space missions by designing a robot capable of moving on trusses and manipulating parts. To enhance the stability of the robot during movement and part manipulation, inspiration was drawn from the Dynastes Hercules beetle. Building upon detailed research on the Dynastes Hercules beetle, a biomimetic structure was designed for the robot system. Based on specific task requirements, the overall plan of the robot was developed, and its kinematic and dynamic models were derived. A prototype of the robot was created, which is capable of both movement and assembly functions, including handling spherical and rod-like objects. Through a series of experiments conducted with the robot, the research results demonstrated that the proposed design can effectively achieve the intended functions. [ABSTRACT FROM AUTHOR]

In open-field agricultural environments, the inherent unpredictable situations pose significant challenges for effective human–robot interaction. This study aims to enhance natural communication between humans and robots in such challenging conditions by converting the detection of a range of dynamic human movements into specific robot actions. Various machine learning models were evaluated to classify these movements, with Long Short-Term Memory (LSTM) demonstrating the highest performance. Furthermore, the Robot Operating System (ROS) software (Melodic Version) capabilities were employed to interpret the movements into certain actions to be performed by the unmanned ground vehicle (UGV). The novel interaction framework exploiting vision-based human activity recognition was successfully tested through three scenarios taking place in an orchard, including (a) a UGV following the authorized participant; (b) GPS-based navigation to a specified site of the orchard; and (c) a combined harvesting scenario with the UGV following participants and aid by transporting crates from the harvest site to designated sites. The main challenge was the precise detection of the dynamic hand gesture "come" alongside navigating through intricate environments with complexities in background surroundings and obstacle avoidance. Overall, this study lays a foundation for future advancements in human–robot collaboration in agriculture, offering insights into how integrating dynamic human movements can enhance natural communication, trust, and safety. [ABSTRACT FROM AUTHOR]

As the complexity of modern library management tasks increases, it is difficult for traditional mobile robots to meet the task of moving and classifying books. In order to design a mobile robot that can autonomously classify and transport books, the study realizes the tasks of book classification and transportation in libraries by fusing the differential speed algorithm and the robot motion model algorithm. First, the robot operating system is utilized to scan the books, classify the books, and obtain the category information of the books. Then, the differential speed algorithm is used to control the motion of the robot to ensure that the robot can accurately transport the books to the designated location. At the same time, combined with the robot motion model algorithm, the motion trajectory of the robot is planned to ensure that the robot can avoid obstacles and stably complete the book transportation task. Finally, the deep reinforcement learning algorithm is used to train the decision-making model of the robot to improve the intelligence level of the robot. The results of simulation experiments show that the research method has the highest accuracy, with an average accuracy of 99.98%, and the robot is able to accurately categorize the books and quickly avoid obstacles with strong stability. The results of the application experiments show that the research method has the shortest moving distance, with an average moving distance of 132 m and an average completion time of 34 seconds, which are lower than the remaining three types of robots. The research robot showed high accuracy in the task of returning books in four time periods within 10 days in the library, with an average accuracy of 99.58%. The experimental results validate the superiority of the research methodology and show that the robots are capable of accurately recognizing and classifying books and can autonomously perform transportation tasks in libraries. The research results help to improve the automation level and management efficiency of libraries and have important application value. [ABSTRACT FROM AUTHOR]

In recent years, with the widespread application of indoor inspection robots, high-precision, robust environmental perception has become essential for robotic mapping. Addressing the issues of visual–inertial estimation inaccuracies due to redundant pose degrees of freedom and accelerometer drift during the planar motion of mobile robots in indoor environments, we propose a visual SLAM perception method that integrates wheel odometry information. First, the robot's body pose is parameterized in SE(2) and the corresponding camera pose is parameterized in SE(3). On this basis, we derive the visual constraint residuals and their Jacobian matrices for reprojection observations using the camera projection model. We employ the concept of pre-integration to derive pose-constraint residuals and their Jacobian matrices and utilize marginalization theory to derive the relative pose residuals and their Jacobians for loop closure constraints. This approach solves the nonlinear optimization problem to obtain the optimal pose and landmark points of the ground-moving robot. A comparison with the ORBSLAM3 algorithm reveals that, in the recorded indoor environment datasets, the proposed algorithm demonstrates significantly higher perception accuracy, with root mean square error (RMSE) improvements of 89.2% in translation and 98.5% in rotation for absolute trajectory error (ATE). The overall trajectory localization accuracy ranges between 5 and 17 cm, validating the effectiveness of the proposed algorithm. These findings can be applied to preliminary mapping for the autonomous navigation of indoor mobile robots and serve as a basis for path planning based on the mapping results. [ABSTRACT FROM AUTHOR]

This paper presents a modular approach to motion planning with provable stability guarantees for robots that move through changing environments via periodic locomotion behaviors. We focus on dynamic walkers as a paradigm for such systems, although the tools developed in this paper can be used to support general compositional approaches to robot motion planning with dynamic movement primitives (DMPs). By formulating the planning process as a switching system with multiple equilibria (SSME), we prove that the system's evolution remains within explicitly characterized trapping regions in the state space under suitable constraints on the frequency of switching among the DMPs. These conditions encapsulate the low-level stability limitations in a form that can be easily communicated to the planner. Furthermore, we show how the available primitives can be safely composed online in a receding horizon manner to enable the robot to react to moving obstacles. The proposed framework can be applied in a wide class of 3D bipedal walking models, and offers a modular approach for integrating readily available low-level locomotion control and high-level planning methods. [ABSTRACT FROM AUTHOR]

Inspired by buckle-induced structural deformation, a new type of tensegrity robot using bending bars is proposed. First, a combination of numerical calculation and the finite-element simulation method was adopted to determine the influence of geometric parameters on equivalent stiffness, and the basic configuration parameters were determined. Then, the tensegrity dynamical model and the ground contact model were established using a nonlinear finite-element method, and four rolling gaits of the robot were realized by driving diagonal cables. Finally, an experimental prototype of the bending bar tensegrity robot was built for rolling gait experiments. The results show that the experimental rolling motion is in good agreement with the theoretical model. Through continuous curve path rolling, the minimum turning radius of the robot was determined, and the motion mode of the prismatic tensegrity robot was enriched. Practical Applications: Tensegrity structure is a self-balancing system composed of compression bars and tension cables, which has aroused numerous scholars' research interest in civil architecture, mechanical engineering, aerospace, biomedical, and other fields. The results cover model design, form-finding analysis, static equilibrium stability analysis, optimization design, dynamic response analysis, and so on. In fact, when the tensegrity mechanism is applied to a mobile robot, a change in shape can be achieved by shortening some cables with the actuator, so the system's stiffness contradicts the ability to deform. The focus of this work is to propose a new type of bending bar design that can replace the spring to meet the needs of large deformation and avoid weakening the structure's overall stiffness. This design realizes the rolling motion mode of the robot and can be applied to the motion mode of other prismatic tensegrity robots. [ABSTRACT FROM AUTHOR]

Collaborative robotic manipulators can stop in case of a collision, according to the ISO/TS 15066 and ISO 10218-1 standards. However, in a human-robot collaboration scenario where the robot and the human share the workspace, a better solution with concerns to production and operator safety would be to perceive in advance the presence of an obstacle and be able to avoid it, thereby completing the task without halting the robot. In this paper, an obstacle avoidance algorithm is tested using a sensor system for real-time human detection; the operator represents a potential dynamic obstacle that can interfere with the robot motion. The sensor system consists of three RGB-D cameras. A custom software framework has been developed in Python exploiting machine learning tools for human skeleton detection. The coordinates of the human body joints relative to the manipulator base are used as input to the obstacle avoidance algorithm. The use of multiple sensors makes it possible to limit the occlusion problem; in addition, the choice of non-wearable sensors goes in the direction of better operator comfort. A series of experimental tests were performed to verify the accuracy of skeleton detection and the ability of the system to avoid obstacles in real time. The human motion caption system, in particular, was validated through a comparison with a commercial system based on wearable IMU sensors widely used and validated in motion capture. There is an NRMSE of 3.23 % for the RGB-D camera-based skeleton detection system, against an NRMSE of 12.23 % for the IMU wearable sensor system. Test results confirm that the system is able to avoid collisions with the human body under various conditions, static or dynamic, ensuring a minimum safety distance to any part of the manipulator. In 44 tests in which the operator moves around the robot with possible collisions (at a speed typical of manufacturing operations), the minimum operator-robot distance averaged 208 mm , being 200 mm the limit safety distance set by algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Teleoperation systems find many applications from earlier search-and-rescue to more recent daily tasks. It is widely acknowledged that using external sensors can decouple the view of the remote scene from the motion of the robot arm during manipulation, facilitating the control task. However, this design requires the coordination of multiple operators or may exhaust a single operator as s/he needs to control both the manipulator arm and the external sensors. To address this challenge, our work introduces a viewpoint prediction model, the first data-driven approach that autonomously adjusts the viewpoint of a dynamic camera to assist in telemanipulation tasks. This model is parameterized by a deep neural network and trained on a set of human demonstrations. We propose a contrastive learning scheme that leverages viewpoints in a camera trajectory as contrastive data for network training. We demonstrated the effectiveness of the proposed viewpoint prediction model by integrating it into a real-world robotic system for telemanipulation. User studies reveal that our model outperforms several camera control methods in terms of control experience and reduces the perceived task load compared to manual camera control. As an assistive module of a telemanipulation system, our method significantly reduces task completion time for users who choose to adopt its recommendation. [ABSTRACT FROM AUTHOR]

Robots are moving from working in isolation to working with humans as a part of human-robot teams. In such situations, they are expected to work with multiple humans and need to understand and predict the team members' actions. To address this challenge, in this work, we introduce IMPRINT, a multi-agent motion prediction framework that models the interactional dynamics and incorporates the multimodal context (e.g., data from RGB and depth sensors and skeleton joint positions) to accurately predict the motion of all the agents in a team. In IMPRINT, we propose an Interaction module that can extract the intra-agent and inter-agent dynamics before fusing them to obtain the interactional dynamics. Furthermore, we propose a Multimodal Context module that incorporates multimodal context information to improve multi-agent motion prediction. We evaluated IMPRINT by comparing its performance on human-human and human-robot team scenarios against state-of-the-art methods. The results suggest that IMPRINT outperformed all other methods over all evaluated temporal horizons. Additionally, we provide an interpretation of how IMPRINT incorporates the multimodal context information from all the modalities during multi-agent motion prediction. The superior performance of IMPRINT provides a promising direction to integrate motion prediction with robot perception and enable safe and effective human-robot collaboration. [ABSTRACT FROM AUTHOR]

In this work, we present a method for personalizing human-robot interaction by using emotive facial expressions to generate affective robot movements. Movement is an important medium for robots to communicate affective states, but the expertise and time required to craft new robot movements promotes a reliance on fixed preprogrammed behaviors. Enabling robots to respond to multimodal user input with newly generated movements could stave off staleness of interaction and convey a deeper degree of affective understanding than current retrieval-based methods. We use autoencoder neural networks to compress robot movement data and facial expression images into a shared latent embedding space. Then, we use a reconstruction loss to generate movements from these embeddings and triplet loss to align the embeddings by emotion classes rather than data modality. To subjectively evaluate our method, we conducted a user survey and found that generated happy and sad movements could be matched to their source face images. However, angry movements were most often mismatched to sad images. This multimodal data-driven generative method can expand an interactive agent's behavior library and could be adopted for other multimodal affective applications. [ABSTRACT FROM AUTHOR]

This paper investigates the issue of observer-based dual-dynamic event-triggered scheme into networked control systems, focusing on its application to motion robots. Firstly, it establishes a mathematical model of the motion robot system based on Newtonian mechanics. Secondly, it introduces a dynamic event-triggering mechanism coupled with observer design to minimize data transmission and enhance communication efficiency. Thirdly, it implements a second dynamic event-triggering protocol for controller design, ensuring a positive lower bound for triggering event intervals, thus avoiding Zeno behavior. Fourthly, it constructs a Lyapunov function with internal dynamic variables for stability analysis using linear matrix inequalities. Finally, simulation studies validate the method's efficacy for motion robot systems. [ABSTRACT FROM AUTHOR]

The current research on obstacle avoidance path planning methods for power inspection robots has problems such as poor obstacle avoidance ability and poor inspection effectiveness. Therefore, a planning method for obstacle avoidance path of power inspection robots is proposed. By utilizing motion relationships and the potential field theorem of robot motion, a three-dimensional model of the power inspection robot's route is established to determine the direction of the robot's route when obtaining action tasks. The fuzzy support vector algorithm is used to plan obstacle avoidance paths for the initialized walking path, making the inspection robot intelligent. The experimental results show that the average success rates for avoiding static and dynamic obstacles are 98.37% and 96.12%, respectively. The average time for obstacle avoidance path planning is 1.56 seconds, and it has fast, efficient, and accurate obstacle avoidance and path planning capabilities, which can improve the robot's obstacle avoidance ability and path planning efficiency for dynamic and static obstacles. [ABSTRACT FROM AUTHOR]

Snake robots have broad application potential, but their motion-control and motion-planning problems are extremely challenging due to the high redundancy of degrees of freedom (DoFs), and the lack of complete system tools further hinders the research of snake robots. In this paper, a coordinate system and a kinematic model were established based on the D-H method for snake robots. The rhythm-generation model for multimodal motion gait and a novel sliding-window five-point interpolation-derivative model were proposed based on a bio-inspired central pattern generator (CPG) model. A prototype and simulator were constructed based on the designed snake robot models to achieve the multimodal motion gait for the snake robot and improve its environmental adaptability. Furthermore, a novel structure–drive–perception–control integration snake robot system (SnakeSys) was built based on the robot-operating system (ROS). Finally, the effectiveness, feasibility, and accuracy of the kinematic model and control model in motion control and information perception were verified through simulations and experiments. We open sourced SnakeSys so that relevant researchers or developers can directly utilize or further develop it. [ABSTRACT FROM AUTHOR]

Parcel transportation is a task that is expected to be highly automated with the development of application-specific mobile robots. This paper presents the design and implementation of a parcel transportation and delivery mechanism aimed at converting a general-purpose indoor omnidirectional robot into an indoor delivery robot. The design of this new mechanism has considered the best placement in the robot and the limitation of not exceeding the original robot diameter. The mechanism consists of a basket with a lid that allows for the manual loading and automatic unloading of parcels. Despite the space limitations imposed by the general-purpose robot design, the designed mechanism can transport up to 90% of the packages received in an educational building. The mechanism was empirically validated by conducting 125 static manual loading experiments, 150 static unloading experiments, and 50 complete parcel delivery experiments. Results show that the delivery robot can efficiently deliver 78% of the total packages received in the building: envelopes, very small parcels, and small parcels. In the case of medium parcels, the delivery was unsuccessful in 30% of cases, in which the parcel did not properly slide out of the basket. [ABSTRACT FROM AUTHOR]

Board games like chess serve as an excellent testbed for human–robot interactions, where advancements can lead to broader human–robot cooperation systems. This paper presents a chess-playing robotic system to demonstrate controlled pick and place operations using a 3-DoF manipulator with image and speech recognition. The system identifies chessboard square coordinates through image processing and centroid detection before mapping them onto the physical board. User voice input is processed and transcribed into a string from which the system extracts the current and destination locations of a chess piece with a word error rate of 8.64%. Using an inverse-kinematics algorithm, the system calculates the joint angles needed to position the end effector at the desired coordinates actuating the robot. The developed system was evaluated experimentally on the 3-DoF manipulator with a voice command used to direct the robot movement in grasping a chess piece. Consideration was made involving both the own pieces as well as capturing the opponent's pieces and moving the captured piece outside the board workspace. [ABSTRACT FROM AUTHOR]

For the system containing multiple adjustable degree-of-freedom modular units, a mathematical description of the modular robot topology including a standardized process and a kinematics parallel modeling method are proposed. Firstly, a library of the modular unit motion transfer is established, and a mathematical description of the modular robot topology based on the quadruple matrix with the standardized process is proposed. Then, based on the kinematics model of one modular unit, considering the coupling effect between the branches of the modular robot with multiple motion branches, a position level and speed-level kinematics model is established, which is suitable for chain modular robots. Subsequently, a kinematics parallel modeling method of modular robots is proposed. Both theoretical analytics and stimulation results show that this method can be adapted to the rapid modeling of modular robots with various types of topologies. The introduction of topology mathematical description and parallel computing reduces the difficulty of modular robot modeling and improves modeling efficiency. [ABSTRACT FROM AUTHOR]

This work describes the development of a tool for the teleoperation of robots. The tool is developed in a virtual environment using the Unity graphics engine. For the development of the application, a kinematic model and a dynamic model of a mobile manipulator are used. The mobile manipulator robot consists of an omnidirectional platform and an anthropomorphic robotic arm with 4 degrees of freedom (4DOF). The model is essential to emulate the movements of the robot and to facilitate the immersion in the virtual environment. In addition, the control algorithms are established and developed in MATLAB 2020 software, which improves the acquisition of knowledge to teleoperate robots and execute tasks of manipulation and transport of objects. This methodology offers a cheaper and safer alternative to real physical systems, as it reduces both the costs and risks associated with using a real robot for training. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of generating clear image frames and minimizing the loss of keyframes by a robot engaging in rapid large viewing angle motion. These issues often lead to detrimental consequences such as trajectory drifting and loss during the construction of curved motion trajectories. To tackle this, we proposed a novel visual simultaneous localization and mapping (SLAM) algorithm, TKO‐SLAM, which is based on time‐delay feature regression and keyframe position optimization. TKO‐SLAM uses a multiscale recurrent neural network to rectify object deformation and image motion smear. This network effectively repairs the time‐delay image features caused by the rapid movement of the robot, thereby enhancing visual clarity. Simultaneously, inspired by the keyframe selection strategy of the ORB‐SLAM3 algorithm, we introduced a grayscale motion‐based image processing method to supplement keyframes that may be omitted due to the robot's rapid large viewing angle motion. To further refine the algorithm, the time‐delay feature regression image keyframes and adjacent secondary keyframes were used as dual measurement constraints to optimize camera poses and restore robot trajectories. The results of experiments on the benchmark RGB‐D data set TUM and real‐world scenarios show that TKO‐SLAM algorithm achieves more than 10% better localization accuracy than the PKS‐SLAM algorithm in the rapid large viewing angle motion scenario, and has advantages over the SOTA algorithms. [ABSTRACT FROM AUTHOR]

Litter nowadays presents a significant threat to the equilibrium of many ecosystems. An example is the sea, where litter coming from coasts and cities via gutters, streets, and waterways, releases toxic chemicals and microplastics during its decomposition. Litter removal is often carried out manually by humans, which inherently lowers the amount of waste that can be effectively collected from the environment. In this paper, we present a novel quadruped robot prototype that, thanks to its natural mobility, is able to collect cigarette butts (CBs) autonomously, the second most common undisposed waste worldwide, in terrains that are hard to reach for wheeled and tracked robots. The core of our approach is a convolutional neural network for litter detection, followed by a time‐optimal planner for reducing the time needed to collect all the target objects. Precise litter removal is then performed by a visual‐servoing procedure which drives the nozzle of a vacuum cleaner that is attached to one of the robot legs on top of the detected CB. As a result of this particular position of the nozzle, we are able to perform the collection task without even stopping the robot's motion, thus greatly increasing the time‐efficiency of the entire procedure. Extensive tests were conducted in six different outdoor scenarios to show the performance of our prototype and method. To the best knowledge of the authors, this is the first time that such a design and method was presented and successfully tested on a legged robot. [ABSTRACT FROM AUTHOR]

Heavy‐duty hexapod robots have impressive stability, high load‐bearing capacity, and exceptional adaptability to rugged terrains. They are capable of working in challenging outdoor environments such as planetary exploration, disaster relief and mountain transportation. Their ability to traverse terrain requires effective motion planning and accurate force distribution, neither of which is currently at the level required for widespread practical applications. In this paper, the mechanical legs are divided into support and swing legs, and the adaptability of the hexapod robot to unknown rugged terrain is enhanced by introducing the Decomposition Quadratic Programming‐based Contact Force Distribution (DQP‐based CFD) method. Moreover, an efficient replanning strategy can handle accidental collisions between swinging legs and unmodelled obstacles. Extensive field experiments demonstrate the effectiveness of our proposed motion planning and contact force distribution methods. [ABSTRACT FROM AUTHOR]