2021/12/07 by Serena Raju, Sherin Shibu, Raju, Serena +5
Computer Science · Engineering · #Advanced Manufacturing and Logistics Optimization #Artificial Intelligence (cs.AI) #F.2.0 #FOS: Computer and information sciences #I.2.9 #Modular Robots and Swarm Intelligence #Robotic Path Planning Algorithms #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2112.03577
openalex publication_date 2021/12/07 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
In this paper, pragmatic implementation of an indoor autonomous delivery system that exploits Reinforcement Learning algorithms for path planning and collision avoidance is audited. The proposed system is a cost-efficient approach that is implemented to facilitate a Raspberry Pi controlled four-wheel-drive non-holonomic robot map a grid. This approach computes and navigates the shortest path from a source key point to a destination key point to carry out the desired delivery. Q learning and Deep-Q learning are used to find the optimal path while avoiding collision with static obstacles. This work defines an approach to deploy these two algorithms on a robot. A novel algorithm to decode an array of directions into accurate movements in a certain action space is also proposed. The procedure followed to dispatch this system with the said requirements is described, ergo presenting our proof of concept for indoor autonomous delivery vehicles.