Coordinating multiple mobile robots for obstacle avoidance using cloud computing

Song, Kai‐Tai; Sun, Yu

doi:10.1002/asjc.2431

Cited by 9 publications

(3 citation statements)

References 20 publications

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“…Equation ( 3) defines the nearby obstacle definition for the robot. Equation (4) defines the assigned velocity for robots [24,25]. Equation ( 5) defines the formula for finding the shortest path shown in Algorithm 3.…”

Section: Robot Path Planning Using Cloud Computing Platformmentioning

confidence: 99%

Development of Cloud Autonomous System for Enhancing the Performance of Robots’ Path

et al. 2023

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With the development of computer technology and artificial intelligence (AI), service robots are widely used in our daily life. At the same time, the manufacturing cost of the robots is too expensive for almost all small companies. The greatest technical limitations are the design of the service robot and the resource sharing of the robot groups. Path planning for robots is one of the issues playing an important role in every application of service robots. Path optimization, fast computation, and minimum computation time are required in all applications. This paper aims to propose the Google Cloud Computing Platform and Amazon Web Service (AWS) platforms for robot path planning. The aim is to identify the effect and impact of using a cloud computing platform for service robots. The cloud approach shifts the computation load from robots to the cloud server. Three different path-planning algorithms were considered to find the path for robots using the Google Cloud Computing Platform, while with AWS, three different types of environments, namely dense, moderate, and sparse, were selected to run the path-planning algorithms for robots. The paper presents the comparison and analysis of the results carried out for robot path planning using cloud services with that of the traditional approach. The proposed approach of using a cloud platform performs better in this case. The time factor is crucially diagnosed and presented in the paper. The major advantage derived from this experiment is that as the size of the environment increases, the respective relative delay decreases. This proves that increasing the scale of work can be beneficial by using cloud platforms. The result obtained using the proposed methodology proves that using cloud platforms improves the efficiency of path planning. The result reveals that using the cloud computing platform for service robots can change the entire perspective of using service robots in the future. The main advantage is that with the increase in the scale of services, the system remains stable, while the traditional system starts deteriorating in terms of performance.

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Section: Robot Path Planning Using Cloud Computing Platformmentioning

confidence: 99%

Development of Cloud Autonomous System for Enhancing the Performance of Robots’ Path

et al. 2023

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“…The concept of collision avoidance control [4][5][6] and trajectory tracking control [7,8] are not new, and they were used in target intercept [9][10][11][12][13], pursuit-evasion problems [14][15][16], and so on. The vast majority of previous studies of such tracking problems have been pure tracking problems, without consideration of avoidance.…”

Section: Introductionmentioning

confidence: 99%

Adaptive robust control for unmanned combat platform: Obstacles avoiding and dynamic trajectory tracking

Sun,

Chen,

Wang

et al. 2023

Asian Journal of Control

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This paper proposes an adaptive robust control scheme to deal with the uncertainty problem of unmanned combat platform. Under the constraint of the controller, the rescue vehicle aims to follow the trajectory of the reconnaissance vehicle and avoid obstacles (e.g., craters and destroyed tanks), while they eventually reach the defined target location (an accident area). First, for the control objective, the original constraints are innovatively separated into two parts: arrival constraint and tracking‐avoidance constraint. An approximate constraint‐following control substitutes for the triple problem, and the constraint‐following error is subsequently established. Second, for the control strategy, a ‐measure is introduced to measure the constraint‐following error. To ensure the tracking‐avoidance constraint and the arrival constraint can be fully satisfied by the system, an adaptive robust control is designed. Finally, for the fast time‐varying uncertainty with unknown boundary, an adaptive law is introduced to evaluate the triple tracking‐avoidance‐arrival performance. In conclusion, the main contribution of this paper is to achieve triple tracking‐avoidance arrival for unmanned combat platform, and the problem of military detection and rescue on complex battlefields can be eliminated.

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“…In addition, the authors of [ 10 ] present an algorithm for creating a feasible task migration path, taking into account the migration distance between UAVs, the load condition of UAVs, and environmental characteristics. The authors of [ 11 ] describe a cloud-based motion planning method for coordinating the safe movement of a large number of mobile robots.…”

Section: Introductionmentioning

confidence: 99%

Path Planning and Formation Control for UAV-Enabled Mobile Edge Computing Network

Choutri

Lagha

Meshoul

et al. 2022

Sensors

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Recent developments in unmanned aerial vehicles (UAVs) have led to the introduction of a wide variety of innovative applications, especially in the Mobile Edge Computing (MEC) field. UAV swarms are suggested as a promising solution to cope with the issues that may arise when connecting Internet of Things (IoT) applications to a fog platform. We are interested in a crucial aspect of designing a swarm of UAVs in this work, which is the coordination of swarm agents in complicated and unknown environments. Centralized leader–follower formations are one of the most prevalent architectural designs in the literature. In the event of a failed leader, however, the entire mission is canceled. This paper proposes a framework to enable the use of UAVs under different MEC architectures, overcomes the drawbacks of centralized architectures, and improves their overall performance. The most significant contribution of this research is the combination of distributed formation control, online leader election, and collaborative obstacle avoidance. For the initial phase, the optimal path between departure and arrival points is generated, avoiding obstacles and agent collisions. Next, a quaternion-based sliding mode controller is designed for formation control and trajectory tracking. Moreover, in the event of a failed leader, the leader election phase allows agents to select the most qualified leader for the formation. Multiple possible scenarios simulating real-time applications are used to evaluate the framework. The obtained results demonstrate the capability of UAVs to adapt to different MEC architectures under different constraints. Lastly, a comparison is made with existing structures to demonstrate the effectiveness, safety, and durability of the designed framework.

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Coordinating multiple mobile robots for obstacle avoidance using cloud computing

Cited by 9 publications

References 20 publications

Development of Cloud Autonomous System for Enhancing the Performance of Robots’ Path

Development of Cloud Autonomous System for Enhancing the Performance of Robots’ Path

Adaptive robust control for unmanned combat platform: Obstacles avoiding and dynamic trajectory tracking

Path Planning and Formation Control for UAV-Enabled Mobile Edge Computing Network

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