Search results

The purpose of this paper is to study the adaptability of the tracked robot in complex working environment. It proposes an angle-changeable tracked robot with human–robot interaction in unstructured environment. The study aims to present the mechanical structure and human–robot interaction control system of the tracked robot and analyze the static stability of the robot working in three terrains, i.e. rugged terrain, sloped terrain and stairs.

The paper presents the mechanical structure and human–robot interaction control system of the tracked robot. To prevent the detachment of the tracks during obstacle navigation, a new type of passively adaptive device based on the relationship between the track’s variable angle and the forces is presented. Then three types of rough terrain are chosen to analyze the static stability of the tracked robot, i.e. rugged terrain, sloped terrain and stairs.

This paper provides the design method of the tracked robot. Owing to its appropriate dimensions, good mass distribution and limited velocity, the tracked robot remains stable on the complex terrains. The experimental results verify the effectiveness of the design method.

The theoretical analysis of this paper provides basic reference for the structural design of tracked robots.

Aiming at the problem that quadruped crawling robot is easy to collide and overturn when facing obstacles and bulges in the process of complex slope movement, this paper aims to propose an obstacle avoidance gait planning of quadruped crawling robot based on slope terrain recognition.

First, considering the problem of low uniformity of feature points in terrain recognition images under complex slopes, which leads to too long feature point extraction time, an improved ORB (Oriented FAST and Rotated BRIEF) feature point extraction method is proposed; second, when the robot avoids obstacles or climbs over bumps, aiming at the problem that the robustness of a single step cannot satisfy the above two motions at the same time, the crawling gait is planned according to the complex slope terrain, and a robot obstacle avoidance gait planning based on the artificial potential field method is proposed. Finally, the slope walking experiment is carried out in the Robot Operating System.

The proposed method provides a solution for the efficient walking of robot under slope. The experimental results show that the extraction time of the improved ORB extraction algorithm is 12.61% less than the original ORB extraction algorithm. The vibration amplitude of the robot’s centroid motion curve is significantly reduced, and the contact force is reduced by 7.76%. The time it takes for the foot contact force to stabilize has been shortened by 0.25 s. This fact is verified by simulation and test.

The method proposed in this paper uses the improved feature point recognition algorithm and obstacle avoidance gait planning to realize the efficient walking of quadruped crawling robot on the slope. The walking stability of quadruped crawling robot is tested by prototype.

This study aims to the complex and unpredictable terrain environment of the Qinghai-Tibet Plateau scientific research station, such as cement road, wetland, gravel desert, snowfield, ice surface, grassland, slimy ground, steep slope, step, etc., a reconfigurable walking mechanism based on two movement modes of wheel and triangular crawler was proposed.

By analyzing the deformation mechanism of the walking mechanism, a reconfigurable wheel-crawler-integrated walking mechanism and the configuration scheme are designed. The analysis of the kinematics and mechanical properties of the swing arm system and the deformation mechanism of the walking mechanism.

The reconfigurable wheel-crawler-integrated walking mechanism can be switched between the wheel and triangular crawler modes by driving the deformation mechanism. Through the numerical simulation of its movement process, and the trial production and experiment of the prototype, indicates the validity of the reconfigurable wheel-crawler-integrated walking mechanism design.

The work of this paper provides a reconfigurable wheel-crawler-integrated-walking mechanism, which can be used by robots in the Qinghai-Tibet Plateau scientific research station. It has excellent reconfigurability and can effectively improve the robot’s adaptability to complex terrain.

This paper aims to present a new method to analyze the robot’s obstacle negotiation based on the terramechanics, where the terrain physical parameters, the sinkage and the slippage of the robot are taken into account, to enhance the robot’s trafficability.

In this paper, terramechanics is used in motion planning for all-terrain obstacle negotiation. First, wheel/track-terrain interaction models are established and used to analyze traction performances in different locomotion modes of the reconfigurable robot. Next, several key steps of obstacle-climbing are reanalyzed and the sinkage, the slippage and the drawbar pull are obtained by the models in these steps. In addition, an obstacle negotiation analysis method on loose soil is proposed. Finally, experiments in different locomotion modes are conducted and the results demonstrate that the model is more suitable for practical applications than the center of gravity (CoG) kinematic model.

Using the traction performance experimental platform, the relationships between the drawbar pull and the slippage in different locomotion modes are obtained, and then the traction performances are obtained. The experimental results show that the relationships obtained by the models are in good agreement with the measured. The obstacle-climbing experiments are carried out to confirm the availability of the method, and the experimental results demonstrate that the model is more suitable for practical applications than the CoG kinematic model.

Comparing with the results without considering Terramechanics, obstacle-negotiation analysis based on the proposed track-terrain interaction model considering Terramechanics is much more accurate than without considering Terramechanics.

Snake-inspired robots are of great significance in many fields because of their great adaptability to the environment. This paper aims to systematically illustrate the research progress of snake-inspired robots according to their application environments. It classifies snake-inspired robots according to the numbers of degrees of freedom in each joint and briefly describes the modeling and control of snake-inspired robots. Finally, the application fields and future development trends of snake-inspired robots are analyzed and discussed.

This paper summarizes the research progress of snake-inspired robots and clarifies the requirements of snake-inspired robots for self-adaptive environments and multi-functional tasks. By equipping various sensors and tool modules, snake-inspired robots are developed from fixed-point operation in a single environment to autonomous operation in an amphibious environment. Finally, it is pointed out that snake-inspired robots will be developed in terms of rigid and flexible deformable structure, long endurance and multi-function and intelligent autonomous control.

Inspired by the modular and reconfigurable concepts of biological snakes, snake-inspired robots are well adapted to unknown and changing environments. Therefore, snake-inspired robots will be widely used in industrial, military, medical, post-disaster search and rescue applications. Snake-inspired robots have become a hot research topic in the field of bionic robots.

This paper summarizes the research status of snake-inspired robots, which facilitates the reader to be a comprehensive and systematic understanding of the research progress of snake-inspired robots. This helps the reader to gain inspiration from biological perspectives.

The accurate prediction of driving torque demand is essential for the development of motion controllers for mobile robots on complex terrains. This paper aims to propose a hybrid model of torque prediction, adaptive EC-GPR, for mobile robots to address the problem of estimating the required driving torque with unknown terrain disturbances.

An error compensation (EC) framework is used, and the preliminary prediction driving torque value is achieved using Gaussian process regression (GPR). The error is predicted using a continuous hidden Markov model to generate compensation for the prediction residual caused by terrain disturbances and uncertainties. As the final step, a gain coefficient is used to adaptively tune the significance of the compensation term through parameter resetting. The proposed model is verified on a sample set, including the driving torque of a mobile robot on three different sandy terrains with two driving modes.

The results show that the adaptive EC-GPR yields the highest prediction accuracy when compared with existing methods.

It is demonstrated that the proposed model can predict the driving torque accurately for mobile robots in an unconstructed environment without terrain identification.

Optimal navigation and trajectory planning are in high demand because of the rise in automated systems. This study aims to focus on implementing an intelligent regression-based chaotic Harris Hawk optimization (LR-CHHO) to achieve a globally optimal path free from collisions.

This study removes the drawbacks of the existing HHO model in terms of its exploration and exploitation behaviors. After the threat is encountered, the improved controller is activated. The LR tool, here, avoids the issue related to the sensitivity of the model. The virtual Hawks, as per the HHO technique, are generated and trained to enhance the diversity in Hawks population. The final controller then calculates the optimal turn angle for the humanoid to avoid threats before reaching the goal.

Model showed an overall improvement greater than 4% in the path and 9% in time compared with standard models in Terrains 1 and 2. Regarding energy efficiency, a significant improvement of more than 20% in the hip, 14% in the knee and 30% in the ankle was observed on both even and uneven terrains.

The originality of this study focuses on improving the diversity in the HHO population by introducing the LR-based model to help the humanoids find an optimal path to the goal. Although the basic model lacked an optimal solution because of sensitivity, less diversity, etc., the proposed model helped resolve the issue and achieve an optimal turning angle for the humanoids to trace the optimal path.

When the mobile manipulator is traveling on an unconstructed terrain, the external disturbance is generated. The load on the end of the mobile manipulator will be affected strictly by the disturbance. The purpose of this paper is to reject the disturbance and keep the end effector in a stable pose all the time, a control method is proposed for the onboard manipulator.

In this paper, the kinematics and dynamics models of the end pose stability control system for the tracked robot are built. Through the guidance of this model information, the control framework based on active disturbance rejection control (ADRC) is designed, which keeps the attitude of the end of the manipulator stable in the pitch, roll and yaw direction. Meanwhile, the control algorithm is operated with cloud computing because the research object, the rescue robot, aims to be lightweight and execute work with remote manipulation.

The challenging simulation experiments demonstrate that the methodology can achieve valid stability control performance in the challenging terrain road in terms of robustness and real-time.

This research facilitates the stable posture control of the end-effector of the mobile manipulator and maintains it in a suitable stable operating environment. The entire system can normally work even in dynamic disturbance scenarios and uncertain nonlinear modeling. Furthermore, an example is given to guide the parameter tuning of ADRC by using model information and estimate the unknown internal modeling uncertainty, which is difficult to be modeled or identified.

The Government of India is proposing the setting up of several new smart cities in the sub-continent. Being an over-populated country, space is at a premium. In congested areas high-rise buildings afford a solution. The purpose of this paper is to present new research involving architecture and computational fluid dynamics (CFDs) must be done at the screening stage of design plans before new cities are laid out. This is achieved in the present study involving a university residential campus with a population of 29,000 comprising of an assortment of high-rise buildings in complex terrain.

This paper uses a combination of instrument-fitted drone measurements – (equipped with a barometer, and sensors for obtaining temperature, relative humidity and altitude) along with a computational fluid dynamical analysis to yield deep insights into the ventilation patterns around an assortment of building forms.

This study was conducted in a residential complex in the campus of the Vellore Institute of Technology (VIT) India. Based on the deciphered wind velocity pattern, a human thermal comfort study was also conducted. It was concluded that the orientation of the buildings play a pivotal role in enhancing the ventilation rates inside a building. It was observed that a dominant eddy spanning a radius of approximate 34 meters was responsible for much of the air changes within the rooms – the smaller eddies had an insignificant role. This method of ascertaining eddy structures within a study area comprising of an assortment of buildings is essential for accurate prescriptions of glazing ratios on building facades.

The main research implications pertain to the use of smart ventilation methods in built up environments. The study shows how large eddies drive the momentum transfer and the air changes per hour with rooms in high-rise buildings in complex terrain. In monsoon-driven flows, there are well set preferred directions of wind flow and this enables the characterization of the fully eddy structure in the vicinity of tall buildings. Another research implication would be the development of new turbulence closure models for eddy structure resolution for flow around complex building forms.

This study introduces a novel protocol at the planning stage of the upcoming residential complexes in proposed smart cities in the sub-continent. The results may well inform architects and structural engineers and help position and orient buildings in confined spaces and also ascertain the optimal glazing ratio, which affects the ventilation pattern.

The results from this study can be used by town planners and architects in urban conurbations in the developing world. The results may well help lower heating ventilation and airconditioning loads. Energy-efficient buildings in developing countries are necessary because most of these have rapidly growing GDPs with a concomitant increase in energy consumption.

This novel study combining instrument mounted drone and CFDs shows for the first time how architects and town planners with a limited budget position and orient a group of buildings in a complex terrain.

Continuous and stable tracking of the low-altitude maneuvering targets is usually difficult due to terrain occlusion and Doppler blind zone (DBZ). This paper aims to present a non-myopic scheduling method of multiple radar sensors for tracking the low-altitude maneuvering targets. In this scheduling problem, the best sensors are systematically selected to observe targets for getting the best tracking accuracy under maintaining the low intercepted probability of a multi-sensor system.

First, the sensor scheduling process is formulated within the partially observable Markov decision process framework. Second, the interacting multiple model algorithm and the cubature Kalman filter algorithm are combined to estimate the target state, and the DBZ information is applied to estimate the target state when the measurement information is missing. Then, an approximate method based on a cubature sampling strategy is put forward to calculate the future expected objective of the multi-step scheduling process. Furthermore, an improved quantum particle swarm optimization (QPSO) algorithm is presented to solve the sensor scheduling action quickly. Optimization problem, an improved QPSO algorithm is presented to solve the sensor scheduling action quickly.

Compared with the traditional scheduling methods, the proposed method can maintain higher target tracking accuracy with a low intercepted probability. And the proposed target state estimation method in DBZ has better tracking performance.

In this paper, DBZ, sensor intercepted probability and complex terrain environment are considered in sensor scheduling, which has good practical application in a complex environment.