An iterative optimization approach for multi-robot pattern formation in obstacle environment

Zhang, Fangfang; Wang, Tingting; Li, Qiyan; Xin, Jianbin

doi:10.1016/j.robot.2020.103645

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…When the motion control for multirobot navigation is used in large-scale dynamic scenarios, a personalized route algorithm was presented by introducing the Polychromatic Sets (PS) for users to obtain real-time route that meet their travel preferences [4], all obstacle features can be integrated into a scalar potential field to make decisions [30], an online adaptive replanning strategy for multiple drones flying in an urban environment with a number of dynamic changes [5], and a reliable routing optimization scheme based on the Manhattan mobility model is presented [31] for UAV real-time planning in a vehicular ad hoc networks (VANETs). While a real-time tracking method was designed [32] that combines A * algorithm with dynamic window to guarantee the ability of obstacle avoidance and path smoothness. Thus, it has been observed that the heuristics are more robust and conduct well in scenarios when compared to other algorithms.…”

Section: Solving Algorithmsmentioning

confidence: 99%

“…It is impossible to strictly follow this path in the process, because the motion parameters such as speed and acceleration at the turning point will change unless it is assumed that the speed and acceleration at the turning point are reduced to 0, which will, of course, reduce the motion efficiency of motions. Then, a polynomial-based trajectory optimization method was presented by minimizing jerk (snap) to ensure that the generated trajectory polynomial is continuous and smooth in both speed and acceleration [32][33][34]. The continuous trajectories between two ends in a two-dimensional plane can be described by the analytic polynomials xðtÞ and YðtÞ about time t, taking the x-axis as an example, the polynomial expression is as equation (5).…”

Section: Trajectory Optimizationmentioning

confidence: 99%

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Parallel Swarm Intelligent Motion Planning with Energy‐Balanced for Multirobot in Obstacle Environment

Zhao

Wang

2021

Wireless Communications and Mobile Computing

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Multirobot motion planning is always one of the critical techniques in edge intelligent systems, which involve a variety of algorithms, such as map modeling, path search, and trajectory optimization and smoothing. To overcome the slow running speed and imbalance of energy consumption, a swarm intelligence solution based on parallel computing is proposed to plan motion paths for multirobot with many task nodes in a complex scene that have multiple irregularly-shaped obstacles, which objective is to find a smooth trajectory under the constraints of the shortest total distance and the energy-balanced consumption for all robots to travel between nodes. In a practical scenario, the imbalance of task allocation will inevitably lead to some robots stopping on the way. Thus, we firstly model a gridded scene as a weighted MTSP (multitraveling salesman problem) in which the weights are the energies of obstacle constraints and path length. Then, a hybridization of particle swarm and ant colony optimization (GPSO-AC) based on a platform of Compute Unified Device Architecture (CUDA) is presented to find the optimal path for the weighted MTSPs. Next, we improve the A ∗ algorithm to generate a weighted obstacle avoidance path on the gridded map, but there are still many sharp turns on it. Therefore, an improved smooth grid path algorithm is proposed by integrating the dynamic constraints in this paper to optimize the trajectory smoothly, to be more in line with the law of robot motion, which can more realistically simulate the multirobot in a real scene. Finally, experimental comparisons with other methods on the designed platform of GPUs demonstrate the applicability of the proposed algorithm in different scenarios, and our method strikes a good balance between energy consumption and optimality, with significantly faster and better performance than other considered approaches, and the effects of the adjustment coefficient q on the performance of the algorithm are also discussed in the experiments.

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Section: Solving Algorithmsmentioning

confidence: 99%

Section: Trajectory Optimizationmentioning

confidence: 99%

Parallel Swarm Intelligent Motion Planning with Energy‐Balanced for Multirobot in Obstacle Environment

Zhao

Wang

2021

Wireless Communications and Mobile Computing

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Suboptimal Relational Tree Configuration and Robust Control Based on the Leader-follower Model for Self-organizing Systems Without GPS Support

Xiong,

Luo,

Liu

et al. 2024

Int. J. Control Autom. Syst.

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SymSwarm: A Two-Staged Decentralized Symmetrical Pattern Formation Paradigm for a Swarm of Mobile Robots

Istiak,

Ullah,

Ashraf

et al. 2023

Arab J Sci Eng

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For several decades, cooperative control of autonomous mobile robots has been an active research area with potential applications in dynamic areas such as agriculture, industrial plants, and the military. Although numerous research studies on pattern formation using multiple mobile agents have been conducted, no algorithm for symmetric pattern formation that is independent of the number of agents and prioritizes user control has been introduced. This article demonstrates an attempt to manifest an end-to-end symmetrical pattern in a swarm of mobile robots. Here, a two-staged paradigm named SymSwarm is developed, which incorporates the novelty of forming symmetrical formations around a point within a coveted area via decentralized communication among the swarm. SymSwarm is built on a novel concept of a bio-inspired state model called CPSM (Compete and Point State Model), which enables smarter and more robust computation, and self-regulation. While most bio-inspired models focus on the characteristics of bees, ants, cats, chimps, and a variety of other animals, this paradigm focuses on the competitive sentiment and leadership ability of human beings to shape the CPSM mentioned above. The algorithm for acquiring symmetrical points is developed using the Circle Polygon analogy in cartesian geometry. The following sections propose a decentralized communication mechanism for adjacent consecutive robots, as well as a SymSwarm: A Two-Staged Decentralized Symmetrical Pattern heuristic nearest point finder algorithm. The proposed paradigm is evaluated using a robotics simulator (V-REP), and the results indicate that it is consistently successful and has a low rate of pattern formation error in the presence or absence of obstacles in the environment.

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An iterative optimization approach for multi-robot pattern formation in obstacle environment

Cited by 6 publications

References 8 publications

Parallel Swarm Intelligent Motion Planning with Energy‐Balanced for Multirobot in Obstacle Environment

Parallel Swarm Intelligent Motion Planning with Energy‐Balanced for Multirobot in Obstacle Environment

Suboptimal Relational Tree Configuration and Robust Control Based on the Leader-follower Model for Self-organizing Systems Without GPS Support

SymSwarm: A Two-Staged Decentralized Symmetrical Pattern Formation Paradigm for a Swarm of Mobile Robots

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