Switching formation strategy with the directed dynamic topology for collision avoidance of a multi‐robot system in uncertain environments

Tran, Vu Phi; Garratt, Matthew A.; Petersen, Ian R.

doi:10.1049/iet-cta.2020.0502

Cited by 22 publications

(2 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chen et al [13] made new methods for human-robot interaction by combining two channels, 3D gesture interaction and natural language command, to help the robot and human to achieve efficient interaction, thus making the robot serve people more fully. Tran et al [14] proposed an integrated controller for self-driving vehicles, which can properly control the robot when there is a sudden obstacle on the road. The controller is able to respond appropriately to emergency situations when sudden obstacles appear on the road.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Object Avoidance Method Design of Railroad Inspection Robot Based on Particle Swarm Algorithm

Xiaoxin Guo,

Xintai Liu

2023

Journal of Computers

View full text Add to dashboard Cite

<p>In order to make the railroad inspection robot better adapt to its complex working environment, it is especially important to study the robot object avoidance algorithm. The WOA algorithm has simple and understandable structure and strong optimization ability but is prone to local convergence. IWOA-PSO is used for railway inspection robots. The performance of IWOA-PSO in the experimental results is better than that of WOA and PSO, and the average accuracy and standard deviation of the IWOA-PSO can better reach the theoretical optimal value in the function tests, and it has performance close to the theoretical value. In the simple environment object avoidance route planning, the minimum path length of IWOA-PSO is 850 mm, which is 53.6% less than that of the PSO algorithm, and the search time is 13.12 seconds, which is 5.11 seconds less than that of PSO algorithm; in the ordinary environment object avoidance route planning, the minimum path length of IWOA-PSO is 830 mm, while the path length of PSO algorithm is 1339 mm, the former is 38% less than the latter, and the search time of IWOA-PSO is 14.05 seconds less than PSO algorithm, so the method has better effect on object avoidance.</p> <p> </p>

show abstract

Section: Related Workmentioning

confidence: 99%

Intelligent Object Avoidance Method Design of Railroad Inspection Robot Based on Particle Swarm Algorithm

Xiaoxin Guo,

Xintai Liu

2023

Journal of Computers

View full text Add to dashboard Cite

show abstract

“…Multi-agent systems have attracted much attention in recent years [1][2][3]. As can be seen from previous research, navigation [4] and obstacle avoidance [5,6] deserve to be studied. However, the existing avoidance mechanisms [3,[7][8][9] have at least one of the foundations, which are efficient interactions among agents, a clean environment, and full knowledge of the surroundings.…”

Section: Introductionmentioning

confidence: 99%

Collision and obstacle avoidance for agents based on velocity programming in unknown environments under kinematical constraints

Xiong

Luo

Liu

et al. 2023

IET Control Theory & Appl

View full text Add to dashboard Cite

This paper conducts research on obstacle and collision avoidance under kinematical constraints in unknown environments, while communication and simultaneous localization and mapping (SLAM) are unavailable to agents. Then a strategy based on mixed-integer programming is proposed, in which velocity constraints are established with a modified Barrier function for obstacles that can be completely detected. As for obstacles that cannot be completely detected, a feasible set is built for the velocity programming based on the convex theory, and the contradicted constraints are addressed with the logic metric method. Besides, the actuator saturation is avoided by converting kinematical constraints into the restrictions on the magnitude, the restrictions on the direction, and the negative correlations between the components of the velocity respectively. Given that invalid nominal velocity leads to collisions, a virtual goal is estimated for nominal velocity improvement. In addition, the local extremum brought by empty programming space due to multiple constraints is repaired by fixing the velocity constraints. The feasibility of the proposed strategy is analyzed, and numerical simulations are provided to verify the effectiveness of the proposed strategy.

show abstract

Active Sensing Strategy: Multi‐Modal, Multi‐Robot Source Localization and Mapping in Real‐World Settings With Fixed One‐Way Switching

Tran,

Perera,

Garratt

et al. 2024

Journal of Field Robotics

View full text Add to dashboard Cite

This paper introduces a state‐machine model designed for a multi‐modal, multi‐robot environmental sensing algorithm tailored to dynamic real‐world settings. The multi‐modal algorithm uniquely combines two distinct exploration strategies for gas source localization and mapping tasks: (1) an initial exploration phase using multi‐robot coverage path planning with variable formations, providing early gas field indication; and (2) a subsequent active sensing phase employing multi‐robot swarms for precise field estimation. The state machine provides the logic for the transition between these two sensing algorithms. In the exploration phase, a coverage path is generated, maximizing the visited area while measuring gas concentration and estimating the initial gas field at pre‐defined sample times. Subsequently, in the active sensing phase, mobile robots moving in a swarm collaborate to select the next measurement point by broadcasting potential positions and reward values, ensuring coordinated and efficient sensing for a multi‐robot swarm system. System validation involves hardware‐in‐the‐loop experiments and real‐time experiments with a radio source emulating a gas field. The proposed approach is rigorously benchmarked against state‐of‐the‐art single‐mode active sensing and gas source localization techniques. The comprehensive evaluation highlights the multi‐modal switching approach's capacity to expedite convergence, adeptly navigate obstacles in dynamic environments, and significantly enhance the accuracy of gas source location predictions. These findings highlight the effectiveness of our approach, showing significant improvements: a 43% reduction in turnaround time, a 50% increase in estimation accuracy, and enhanced robustness of multi‐robot environmental sensing in cluttered scenarios without collisions. These advancements surpass the performance of conventional active sensing strategies, the partial differential equation model, and geometrical localization approaches, underscoring the efficacy of our method.

show abstract

Switching formation strategy with the directed dynamic topology for collision avoidance of a multi‐robot system in uncertain environments

Cited by 22 publications

References 37 publications

Intelligent Object Avoidance Method Design of Railroad Inspection Robot Based on Particle Swarm Algorithm

Intelligent Object Avoidance Method Design of Railroad Inspection Robot Based on Particle Swarm Algorithm

Collision and obstacle avoidance for agents based on velocity programming in unknown environments under kinematical constraints

Active Sensing Strategy: Multi‐Modal, Multi‐Robot Source Localization and Mapping in Real‐World Settings With Fixed One‐Way Switching

Contact Info

Product

Resources

About