ArticleSensors (Basel, Switzerland)2023
Can Genetic Algorithms Be Used for Real-Time Obstacle Avoidance for LiDAR-Equipped Mobile Robots?
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Toward Autonomous UAV Swarm Navigation: A Review of Trajectory Design Paradigms.Sensors (Basel, Switzerland) · 2025Review
- A Versatile Approach to Polygonal Object Avoidance in Indoor Environments with Hardware Schemes Using an FPGA-Based Multi-Robot.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
Despite significant progress in robot hardware, the number of mobile robots deployed in public spaces remains low. One of the challenges hindering a wider deployment is that even if a robot can build a map of the environment, for instance through the use of LiDAR sensors, it also needs to calculate, in real time, a smooth trajectory that avoids both static and mobile obstacles. Considering this scenario, in this paper we investigate whether genetic algorithms can play a role in real-time obstacle avoidance. Historically, the typical use of genetic algorithms was in offline optimization. To investigate whether an online, real-time deployment is possible, we create a family of algorithms called GAVO that combines genetic algorithms with the velocity obstacle model. Through a series of experiments, we show that a carefully chosen chromosome representation and parametrization can achieve real-time performance on the obstacle avoidance problem.
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Registered trials
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