Research on Environment Perception System of Quadruped Robots Based on LiDAR and Vision

Chen, Guangrong; Liu, Hong

doi:10.3390/drones7050329

Cited by 10 publications

(4 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The data of the sensors differ in characteristics, allowing the sensors to compensate each other. As a result, the robot obtains better environmental characteristics for environment recognition [26] [27]. Zhang et al, proposed a visuallyassisted environment reconstruction and positioning method for quadruped robots, which combined the vision module with LiDAR positioning and IMU prediction to form a complete positioning and mapping system [28].…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Environment sensing architecture of quadruped robot and verification in typical scenarios

Tang,

wang,

Gao

et al. 2024

Preprint

View full text Add to dashboard Cite

In recent years, advancements in sensor technology, information processing, computer science, and artificial intelligence have significantly enhanced the autonomy of robots. Quadruped robots operating autonomously in unstructured environments greatly increase the opportunity to explore unknown environments, so accurately sensing the external environment through multi-source sensor information is essential for quadruped robots. In this paper, the hardware and software architecture of four-legged robot environment awareness is proposed, and the perception performance is verified in typical scenarios. The hardware system of the quadruped robot environment perception includes the environment sensor and its computing platform, and the software system includes the visual perception module, the tactile perception module, the LiDAR perception module and the inertial navigation module. In order to evaluate the practicability and performance of the proposed hardware-software environment sensing architecture for quadruped robots, experiments on quadruped robots were carried out in typical scenarios. The experimental results show that the proposed environment sensing software and hardware architecture of quadruped robot has good robustness and can realize accurate and reliable external environment sensing.

show abstract

Section: Introductionmentioning

confidence: 99%

RETRACTED: Environment sensing architecture of quadruped robot and verification in typical scenarios

Tang,

wang,

Gao

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Quadruped robots with manipulators combine the motion performance of quadruped robots and the operational capability of manipulators [1][2][3][4], providing innovative solutions for the transportation of supplies to rescue efforts in the environment after a disaster [5][6][7][8], as shown in Figure 1. In these scenarios, the limited payload capacity and dynamic environment place significant demands on the grasping ability of the mounted manipulator.…”

Section: Introductionmentioning

confidence: 99%

A Framework of Grasp Detection and Operation for Quadruped Robot with a Manipulator

Guo,

Chai,

Zhang

et al. 2024

Drones

View full text Add to dashboard Cite

Quadruped robots equipped with manipulators need fast and precise grasping and detection algorithms for the transportation of disaster relief supplies. To address this, we developed a framework for these robots, comprising a Grasp Detection Controller (GDC), a Joint Trajectory Planner (JTP), a Leg Joint Controller (LJC), and a Manipulator Joint Controller (MJC). In the GDC, we proposed a lightweight grasp detection CNN based on DenseBlock called DES-LGCNN, which reduced algorithm complexity while maintaining accuracy by incorporating UP and DOWN modules with DenseBlock. For JTP, we optimized the model based on quadruped robot kinematics to enhance wrist camera visibility in dynamic environments. We integrated the network and model into our homemade robot control system and verified our framework through multiple experiments. First, we evaluated the accuracy of the grasp detection algorithm using the Cornell and Jacquard datasets. On the Jacquard dataset, we achieved a detection accuracy of 92.49% for grasp points within 6 ms. Second, we verified its visibility through simulation. Finally, we conducted dynamic scene experiments which consisted of a dynamic target scenario (DTS), a dynamic base scenario (DBS), and a dynamic target and base scenario (DTBS) using an SDU-150 physical robot. In all three scenarios, the object was successfully grasped. The results demonstrate the effectiveness of our framework in managing dynamic environments throughout task execution.

show abstract

“…When a comprehensive view of the environment is required, networks of cameras have also been utilized. This is an example of research in the area of robotics that utilizes multiple sensors for environment perception, such as lidar and cameras [2] Path planning, the second crucial step, entails determining a collision-free path for the robot to traverse from its starting point to its desired destination. This involves executing a suitable path planning algorithm that computes a feasible path between the specified points.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Intelligent Navigation of Mobile Robots Using Reinforcement Learning with A Comparative Analysis of a modified Q-Learning Method and DQN in Simulated Gym Environment

Khlif

2024

Preprint

View full text Add to dashboard Cite

Purpose: The field of autonomous mobile robots (AMRs) has experienced significant growth in recent years, propelled by advancements in autonomous driving and unmanned aerial vehicles (UAVs). The integration of intelligence into robotic systems necessitates addressing various research challenges, with naviga- tion emerging as a pivotal aspect of mobile robotics. This paper explores the three fundamental questions central to the navigation problem: localization (determin- ing the robot’s position), mapping (creating a representation of the environment), and path planning (determining the optimal route to the destination). The pro- posed solution to the mobile robot navigation problem involves the seamless integration of these three foundational navigation components. Methods: Our comparative analysis between the Q-learning modified method and a deep Q-network (DQN) in simulated gym pathfinding tasks reveals the efficacy of this approach. The modified Q-learning algorithm consistently outperforms DQN, demonstrating its superior ability to navigate complex environments and achieve optimal solutions. The transition from a definite environment to a simulated gym environment serves as a valuable validation of the method’s applicability in real-world scenarios. By rigorously evaluating our algorithm in a controlled setting, we can ensure its robustness and effectiveness across a broader range of applications. Results: In essence, our study establishes the modified Q-learning algorithm as a promising new approach to addressing the exploration-exploitation dilemma in reinforcement learning. Its superior performance in simulated gym environments suggests its potential for real-world applications in various domains, including robotics, autonomous navigation, and game development. Conclusion: The paper furnishes a comprehensive overview of research on autonomous mobile robot navigation. It begins with a succinct introduction to the diverse facets of navigation, followed by an examination of the roles of machine learning and reinforcement learning in the realm of mobile robotics. Subsequently, the paper delves into various path planning techniques. In the end, this paper presents a comparative analysis of two path planning methods for mobile robots: Q-learning with an enhanced exploration strategy and Deep Q-Network (DQN). Through a comprehensive simulation study in a gym environment, the superior performance of the proposed Q-learning approach is firmly established.

show abstract

Research on Environment Perception System of Quadruped Robots Based on LiDAR and Vision

Cited by 10 publications

References 36 publications

RETRACTED: Environment sensing architecture of quadruped robot and verification in typical scenarios

RETRACTED: Environment sensing architecture of quadruped robot and verification in typical scenarios

A Framework of Grasp Detection and Operation for Quadruped Robot with a Manipulator

A Comprehensive Review of Intelligent Navigation of Mobile Robots Using Reinforcement Learning with A Comparative Analysis of a modified Q-Learning Method and DQN in Simulated Gym Environment

Contact Info

Product

Resources

About