An improved gravitational search algorithm and its performance analysis for multi-robot path planning

Panda, Mohit Ranjan; Das, Pradipta Kumar; Pradhan, Sarthak; Behera, Himansu Sekhar

doi:10.1109/mami.2015.7456577

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…Thus, the first experiment was performed with two static unmapped obstacles that are the two blue cylinders. The proposed solution is compared to the TEB approach from the package teb local planner 4 , which was configured to follow the constraints from Table I.…”

Section: A Simulationsmentioning

confidence: 99%

“…This is due to the fact that the HRVO formulation performs a choice on which side to pass based on the speed estimates and on the RVO centerline. TLHRVO causes less collisions than LHRVO because it is 4 Christoph Rösmann. teb local planner http://wiki.ros.org/teb local planner.…”

Section: A Simulationsmentioning

confidence: 99%

“…This category tends to produce smoother resulting trajectory and give results closer to optimality. For multi-robot motion planning, many trajectory generation approaches are centralized and use optimization [4], [5], [6]. However, the robustness relies on the central intelligence and on the communication between this intelligence and the robots.…”

Section: Introductionmentioning

confidence: 99%

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A modified Hybrid Reciprocal Velocity Obstacles approach for multi-robot motion planning without communication

Catherine

Lucet

2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: A Simulationsmentioning

confidence: 99%

Section: A Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A modified Hybrid Reciprocal Velocity Obstacles approach for multi-robot motion planning without communication

Catherine

Lucet

2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…The method tries to find the shortest, the safest, and the smoothest path based on the Firefly Algorithm (FA). Panda et al [22] presented a hybrid algorithm for multi-robot PP problem in the static environments. The authors combined FA algorithm with Invasive Weed Optimization (IWO) to overcome the limitation of slow convergence of the FA algorithm in large space problems and provided a balance between exploration and exploitation in the workspace.…”

Section: Related Workmentioning

confidence: 99%

“…Considering the sensor ranges, if the distance to the nearest obstacle is greater than Sensor Threshold, we set the MDO(c) value to Sensor Threshold. The safety fitness is estimated by mean of path cells MDO, according to (22). We set the Sensor Threshold to 5 in our experiments.…”

Section: E Path Post Processingmentioning

confidence: 99%

Multi-Robot Path Planning Based on Multi-Objective Particle Swarm Optimization

Thabit

Mohades

2019

IEEE Access

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In this paper, a new method is proposed for the path planning of multi-robots in unknown environments. The method is inspired by multi-objective particle swarm optimization (MOPSO) and is named multi-robot MOPSO. It considers shortness, safety, and smoothness. Due to the obscurity of the environment, the robots should decide the moving direction based on the information gathered by sensors only such that the optimal path between the start and goal positions can be found at the end of the algorithm. Sharing knowledge among the navigating robots is necessary to achieve this aim. So, a new concept, named the probabilistic window, is introduced in this paper. It combines the current information obtained through the robot sensors and experiences of the previous robots to select the paths that seem more likely to achieve higher fitness in the mentioned objectives. The proposed method has an outstanding performance on different complex benchmarks, and the results have shown that it is more effective and efficient compared with the classic and the state-of-the-art methods.

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Multi-robot path planning using a hybrid dynamic window approach and modified chaotic neural oscillator-based hyperbolic gravitational search algorithm in a complex terrain

Vikas

Parhi

2023

Intel Serv Robotics

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An improved gravitational search algorithm and its performance analysis for multi-robot path planning

Cited by 7 publications

References 19 publications

A modified Hybrid Reciprocal Velocity Obstacles approach for multi-robot motion planning without communication

A modified Hybrid Reciprocal Velocity Obstacles approach for multi-robot motion planning without communication

Multi-Robot Path Planning Based on Multi-Objective Particle Swarm Optimization

Multi-robot path planning using a hybrid dynamic window approach and modified chaotic neural oscillator-based hyperbolic gravitational search algorithm in a complex terrain

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