Development and analysis of a novel obstacle avoidance strategy for a multi-robot system inspired by the Bug-1 algorithm

Kandathil, Jom J; Mathew, Robins; Hiremath, Somashekhar S.

doi:10.1177/0037549720930082

Cited by 5 publications

(4 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is essential to decide on an appropriate critical metric for resolving certain driving and navigation issues in various driving situations. In their article 124 authors attempted to use an integrated algorithm for predicting obstacles and estimating the state of a self-driving vehicle. The authors claim in their article 125 that the TA system performance will be stimulated by a decision-making scheme that will define the vehicle's next plan of action.…”

Section: Threat Assessment (Ta)mentioning

confidence: 99%

“…Using a rigorous mathematical framework, authors 142 formulate and discuss the optimization algorithm for the solution and examine the main details of the implementation of the multi-vehicle motion planning problem. In the article 124 , the authors propose a new way of thinking in which agents learn collision as a single agent and then avoid multiple collisions by reversing the trained policy. Major research using quadratic mixed-integer programming (MIQP) has been conducted 143 , with others implementing B-splines 144 , polynomials 145 , elastic bands 146 , and potential fields 147 , in route planning strategies 148 .…”

Section: Dynamic and Static Threat Assessment (Dsta)mentioning

confidence: 99%

See 1 more Smart Citation

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Muzahid

Kamarulzaman

Rahman

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Prospective customers are becoming more concerned about safety and comfort as the automobile industry swings toward automated vehicles (AVs). A comprehensive evaluation of recent AVs collision data indicates that modern automated driving systems are prone to rear-end collisions, usually leading to multiple-vehicle collisions. Moreover, most investigations into severe traffic conditions are confined to single-vehicle collisions. This work reviewed diverse techniques of existing literature to provide planning procedures for multiple vehicle cooperation and collision avoidance (MVCCA) strategies in AVs while also considering their performance and social impact viewpoints. Firstly, we investigate and tabulate the existing MVCCA techniques associated with single-vehicle collision avoidance perspectives. Then, current achievements are extensively evaluated, challenges and flows are identified, and remedies are intelligently formed to exploit a taxonomy. This paper also aims to give readers an AI-enabled conceptual framework and a decision-making model with a concrete structure of the training network settings to bridge the gaps between current investigations. These findings are intended to shed insight into the benefits of the greater efficiency of AVs set-up for academics and policymakers. Lastly, the open research issues discussed in this survey will pave the way for the actual implementation of driverless automated traffic systems.

show abstract

Section: Threat Assessment (Ta)mentioning

confidence: 99%

Section: Dynamic and Static Threat Assessment (Dsta)mentioning

confidence: 99%

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Muzahid

Kamarulzaman

Rahman

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…(2) Compared with existing results of path planning algorithms [34,39,40], a shorter path and less time consumption are obtained using the proposed algorithm…”

Section: Introductionmentioning

confidence: 99%

Path Planning and Control of a Quadrotor UAV Based on an Improved APF Using Parallel Search

Huang

Qin

et al. 2021

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

Control and path planning are two essential and challenging issues in quadrotor unmanned aerial vehicle (UAV). In this paper, an approach for moving around the nearest obstacle is integrated into an artificial potential field (APF) to avoid the trap of local minimum of APF. The advantage of this approach is that it can help the UAV successfully escape from the local minimum without collision with any obstacles. Moreover, the UAV may encounter the problem of unreachable target when there are too many obstacles near its target. To address the problem, a parallel search algorithm is proposed, which requires UAV to simultaneously detect obstacles between current point and target point when it moves around the nearest obstacle to approach the target. Then, to achieve tracking of the planned path, the desired attitude states are calculated. Considering the external disturbance acting on the quadrotor, a nonlinear disturbance observer (NDO) is developed to guarantee observation error to exponentially converge to zero. Furthermore, a backstepping controller synthesized with the NDO is designed to eliminate tracking errors of attitude. Finally, comparative simulations are carried out to illustrate the effectiveness of the proposed path planning algorithm and controller.

show abstract

“…Experimental results demonstrated that the enhanced algorithm effectively overcomes these issues. Kandathil et al [6] implemented the BUG-1 algorithm across a group of robots to move them from a start location to a target location, significantly reducing the time required for robot movement. Huang et al [7] introduced a novel approach called the navigation strategy with path priority (NSPP) for multiple robots moving within a large flat space.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Swarm Intelligence for Obstacle Avoidance with Multi-Strategy and Improved Dung Beetle Optimization Algorithm in Mobile Robot Navigation

Li,

Liu,

Shao

et al. 2023

Electronics

View full text Add to dashboard Cite

The Dung Beetle Optimization (DBO) algorithm is a powerful metaheuristic algorithm that is widely used for optimization problems. However, the DBO algorithm has limitations in balancing global exploration and local exploitation capabilities, often leading to getting stuck in local optima. To overcome these limitations and address global optimization problems, this study introduces the Multi-Strategy and Improved DBO (MSIDBO) Algorithm. The MSIDBO algorithm incorporates several advanced computational techniques to enhance its performance. Firstly, it introduces a random reverse learning strategy to improve population diversity and mitigate early convergence or local stagnation issues present in the DBO algorithm. Additionally, a fitness-distance balancing strategy is employed to better manage the trade-off between diversity and convergence within the population. Furthermore, the algorithm utilizes a spiral foraging strategy to enhance precision, promote strong exploratory capabilities, and prevent being trapped in local optima. To further enhance the global search ability and particle utilization of the MSIDBO algorithm, it combines the Optimal Dimension-Wise Gaussian Mutation strategy. By minimizing premature convergence, population diversity is increased, and the convergence of the algorithm is accelerated. This expansion of the search space reduces the likelihood of being trapped in local optima during the evolutionary process. To demonstrate the effectiveness of the MSIDBO algorithm, extensive experiments are conducted using benchmark test functions, comparing its performance against other well-known metaheuristic algorithms. The results highlight the feasibility and superiority of MSIDBO in solving optimization problems. Moreover, the MSIDBO algorithm is applied to path planning simulation experiments to showcase its practical application potential. A comparison with the DBO algorithm shows that MSIDBO generates shorter and faster paths, effectively addressing real-world application problems.

show abstract

Development and analysis of a novel obstacle avoidance strategy for a multi-robot system inspired by the Bug-1 algorithm

Cited by 5 publications

References 23 publications

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Path Planning and Control of a Quadrotor UAV Based on an Improved APF Using Parallel Search

Enhancing Swarm Intelligence for Obstacle Avoidance with Multi-Strategy and Improved Dung Beetle Optimization Algorithm in Mobile Robot Navigation

Contact Info

Product

Resources

About