Path Planning of Multi-Robot Systems With Boolean Specifications Based on Simulated Annealing

Shi, Weijie; He, Zuoli; Tang, Wei; Liu, Weifeng; Ma, Ziyue

doi:10.1109/lra.2022.3165184

Cited by 29 publications

(6 citation statements)

References 28 publications

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“…People proposed different versions of distributed control with SA based on a variety of objective functions. One similar work is to minimize the total travel distance under Boolean specification using SA (Shi et al, 2022). Their scenario is similar to that in Travelling salesman problem (TSP), which enumerates the trajectories.…”

Section: Optimization Methodsmentioning

confidence: 99%

Comparison of Stochastic Optimization Strategies in Multi-Robot Multi-Target Tracking Scenarios

Xin,

Dames

2024

Preprint

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This paper conducts a comparative study of stochastic optimization strategies to enable multi-robot systems to search for and track an unknown number of targets. Each robot is equipped with a noisy sensor that has a limited Field of View (FoV). The robots use a distributed version of the Probability Hypothesis Density (PHD) filter to estimate both the number and states of the targets. This online target estimate is used by the different search strategies to select actions for each robot. We compare Lloyd's algorithm, a traditional method for distributed search, with four stochastic optimization techniques: Particle Swarm Optimization (PSO), Simulated Annealing (SA), Ant Colony Optimization (ACO), and Artificial Immune Systems (AIS). Each of the methods is adapted from the traditional case of finding a single global optimum to locate all the local maxima (\ie targets). We demonstrate through extensive simulations that these techniques offer superior coverage of the search area and more accurate target localization compared to the baseline Lloyd's algorithm. We also discuss the strengths and limitations of each method, assisting practitioners in the selection of the most appropriate strategy based on specific operational factors, such as the communication load in a multi-robot system.

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Section: Optimization Methodsmentioning

confidence: 99%

Comparison of Stochastic Optimization Strategies in Multi-Robot Multi-Target Tracking Scenarios

Xin,

Dames

2024

Preprint

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“…Different methodologies have been employed alongside cell decomposition algorithms, including the Dijkstra algorithm and the Simulated Annealing (SA) approach. In [61], A* and potential field [67], A* and reinforcement learning [63], A* and the Dynamic Window Algorithm [66], Theta* and dipole field with the dynamic window approach [64] are explored. Other studies concentrated on merging various optimization strategies to address the complexities in path planning, for instance, integrating the Wolf Swarm Algorithm with the artificial potential field (WSA-APF) [62], the kidneyinspired algorithm and Sine-Cosine Algorithm (KA-SCA) [70], Artificial Bee Colony and Evolutionary Programming (ABC-EP) [71], the Modified Hyperbolic Gravitational Search Algorithm and Dynamic Window Approach (MGSA-DWA) [72], the Self-Organizing Migrating Algorithm and Particle Swarm Optimization (SOMA-PSO) [73], the Grey Wolf Optimizer and Whale Optimizer Algorithm (GWO-WOA) [69], and the Dynamic Window Approach (DWA) and Teaching-Learning-Based Optimization (TLBO) [68].…”

Section: Hybrid Approachesmentioning

confidence: 99%

“…UGVs operate on land, traversing diverse terrain types including urban environments, off-road landscapes, and structured indoor spaces [61,73]. Path planning for UGVs must contend with obstacles such as buildings, vehicles, pedestrians, and natural terrain features like slopes and rough terrain.…”

Section: Ugvsmentioning

confidence: 99%

Path Planning Techniques for Real-Time Multi-Robot Systems: A Systematic Review

AbuJabal,

Rabie,

Baziyad

et al. 2024

Electronics

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A vast amount of research has been conducted on path planning over recent decades, driven by the complexity of achieving optimal solutions. This paper reviews multi-robot path planning approaches and presents the path planning algorithms for various types of robots. Multi-robot path planning approaches have been classified as deterministic approaches, artificial intelligence (AI)-based approaches, and hybrid approaches. Bio-inspired techniques are the most employed approaches, and artificial intelligence approaches have gained more attention recently. However, multi-robot systems suffer from well-known problems such as the number of robots in the system, energy efficiency, fault tolerance and robustness, and dynamic targets. Deploying systems with multiple interacting robots offers numerous advantages. The aim of this review paper is to provide a comprehensive assessment and an insightful look into various path planning techniques developed in multi-robot systems, in addition to highlighting the basic problems involved in this field. This will allow the reader to discover the research gaps that must be solved for a better path planning experience for multi-robot systems.

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“…Traditional path planning algorithms include simulated annealing algorithm, artificial potential field method, fuzzy logic algorithm, tabu search algorithm, etc. [14][15][16][17]. Fuzzy control algorithm is a classical algorithm of path planning.…”

Section: Introductionmentioning

confidence: 99%

Local Path Planning via Improved Fuzzy and Q(λ)-learning Algorithms for the Mobile Robot

Li Song,

Li Song

2023

Journal of Computers

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<p>With the complexity of the robot operating environment increases, there becoming higher demands on the optimal path planning for robots. Most of the path planning is performed in known environments and static models. However, there are still challenges for robots to perform path planning in complex unknown or dynamic environments, which will suffer from deadlock problems and obstacle avoidance failures. Reinforcement learning (RL) can help fuzzy algorithm to optimize the strategy. However, the difficulty of designing the rewards in RL makes the algorithm require a large number of samples to learn the strategy, resulting in computational complexity. To solve these problems, a new local path planning based on the improved fuzzy and Q(λ)-learning algorithms is proposed, aiming to plan the shortest path and avoid obstacles. For solving the problems of breaking through and avoiding obstacles, a fuzzy controller is designed. The distance of nearest obstacle in front of the mobile robot and the distance between the obstacles in the two breakout directions are regarded as the two inputs for this controller. And the two fuzzy quantities of the mobile robot’s running angle and the safe step length are outputted. In the path planning, the Q(λ)-learning algorithm are used to optimize the weights of the running angle and the safe step, obtaining a more accurate robot position and speeding up path planning efficiency. Furthermore, to solve the overlap problems among the starting point, end point, and obstacles, a safer running environment is designed considering radiuses of these objects. Besides, the mobile robot breakout scheme and sustainable obstacle avoidance scheme are designed to solve the deadlock problem and “large obstacle” avoidance problem, respectively. Simulation results in the sparse and complex operating environment show that our proposed algorithm can plan a relatively optimal and safe path, improving the success rate of path planning.</p> <p> </p>

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Path Planning of Multi-Robot Systems With Boolean Specifications Based on Simulated Annealing

Cited by 29 publications

References 28 publications

Comparison of Stochastic Optimization Strategies in Multi-Robot Multi-Target Tracking Scenarios

Comparison of Stochastic Optimization Strategies in Multi-Robot Multi-Target Tracking Scenarios

Path Planning Techniques for Real-Time Multi-Robot Systems: A Systematic Review

Local Path Planning via Improved Fuzzy and Q(λ)-learning Algorithms for the Mobile Robot

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