Autonomous foraging with a pack of robots based on repulsion, attraction and influence

Ordaz-Rivas, Erick; Rodríguez-Liñán, Angel; Torres-Treviño, Luis M.

doi:10.1007/s10514-021-09994-5

Cited by 10 publications

(6 citation statements)

References 31 publications

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“…Additionally, Table 2 displays papers about remote sensing navigation, focusing on LiDAR, laser ranger, and infrared sensors. The tasks include exploration [ 63 , 64 , 66 , 71 ], wheelchair [ 78 ], transport [ 65 , 73 , 74 , 75 ], fire services or search and rescue [ 70 ], floor cleaning [ 68 ], tracking [ 76 ], education [ 72 ], and caring and service [ 79 ]. Some approaches are combined with path planning algorithms, including metaheuristic algorithm, potential field, and A* for navigation [ 66 , 68 , 70 , 78 ].…”

Section: Discussionmentioning

confidence: 99%

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Bioinspired Perception and Navigation of Service Robots in Indoor Environments: A Review

2023

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Biological principles draw attention to service robotics because of similar concepts when robots operate various tasks. Bioinspired perception is significant for robotic perception, which is inspired by animals’ awareness of the environment. This paper reviews the bioinspired perception and navigation of service robots in indoor environments, which are popular applications of civilian robotics. The navigation approaches are classified by perception type, including vision-based, remote sensing, tactile sensor, olfactory, sound-based, inertial, and multimodal navigation. The trend of state-of-art techniques is moving towards multimodal navigation to combine several approaches. The challenges in indoor navigation focus on precise localization and dynamic and complex environments with moving objects and people.

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Section: Discussionmentioning

confidence: 99%

“…A swarm behavior algorithm was described based on influence, attraction, and repulsion with a pack of robots and sensory limitations in [ 74 ]. The robots used ultrasonic, infrared sensors, and light-dependent resistance.…”

Section: Remote Sensing Navigationmentioning

confidence: 99%

Bioinspired Perception and Navigation of Service Robots in Indoor Environments: A Review

2023

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“…Demonstrating this technique can increase the overall efficiency of the system. [32] Develop an algorithm for controlling swarm robots that adjusts their turn probability based on the attraction and repulsion signals they sense from other robots.…”

Section: Releated Workmentioning

confidence: 99%

A Bat Social call inspired Algorithm for Multi-Agent Foraging

Jamshidpour¹,

Sadeghzadeh²

2022

Preprint

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This paper proposes a new method to solve the problem of foraging in swarm robotics. This research approach is inspired by the collective behaviours of some species of bats that communicate the location of food or nests to other group using sound signals. A mechanism has been established a mechanism for more effective communication between agents. Within the framework of a self-organizing system without a central controller and through coordination mechanisms in individuals, we propose a way to prevent overcrowding at the point of exploitation and continue exploration. In this research, homogeneous agents, independently , with limited perception and processing ability, can determine and change their role in the group, and according to their role, they perform various tasks and cooperate and interact with other agents, and change and determine the roles of the agents according to rules, social or threshold time is done according to a probabilistic model. We tested the proposed method in a simulation environment and showed the performance of a group of individual agents with minimal cognitive and processing abilities.

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“…Object transportation is where a robot manipulates an object to a goal, and it is applied to various fields, such as logistics [ 1 , 2 ], exploration [ 3 , 4 ], retrieval tasks [ 5 ], and foraging [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Deep-Reinforcement-Learning-Based Object Transportation Using Task Space Decomposition

Eoh

2023

Sensors

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This paper presents a novel object transportation method using deep reinforcement learning (DRL) and the task space decomposition (TSD) method. Most previous studies on DRL-based object transportation worked well only in the specific environment where a robot learned how to transport an object. Another drawback was that DRL only converged in relatively small environments. This is because the existing DRL-based object transportation methods are highly dependent on learning conditions and training environments; they cannot be applied to large and complicated environments. Therefore, we propose a new DRL-based object transportation that decomposes a difficult task space to be transported into simple multiple sub-task spaces using the TSD method. First, a robot sufficiently learned how to transport an object in a standard learning environment (SLE) that has small and symmetric structures. Then, a whole-task space was decomposed into several sub-task spaces by considering the size of the SLE, and we created sub-goals for each sub-task space. Finally, the robot transported an object by sequentially occupying the sub-goals. The proposed method can be extended to a large and complicated new environment as well as the training environment without additional learning or re-learning. Simulations in different environments are presented to verify the proposed method, such as a long corridor, polygons, and a maze.

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Autonomous foraging with a pack of robots based on repulsion, attraction and influence

Cited by 10 publications

References 31 publications

Bioinspired Perception and Navigation of Service Robots in Indoor Environments: A Review

Bioinspired Perception and Navigation of Service Robots in Indoor Environments: A Review

A Bat Social call inspired Algorithm for Multi-Agent Foraging

Deep-Reinforcement-Learning-Based Object Transportation Using Task Space Decomposition

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