Abstract
Obstacle avoidance is a key performance of mobile robots. However, its experimental verification is rather difficult, due to the probabilistic behaviors of both the robots and the obstacles. This paper presents the Markov Decision Process based probabilistic formal models for three obstacle-avoidance strategies of a mobile robot in an uncertain dynamic environment. The models are employed to make analyses in PRISM, and the correctness of the analysis results is verified by MATLAB simulations. Finally, the minimum time and the energy consumption are determined by further analyses in PRISM, which prove to be useful in finding the optimal strategy. The present work provides a foundation for the probabilistic formal verification of more complicated obstacle-avoidance strategies.
Highlights
IntroductionObstacle avoidance is a key technique for the design and applications of mobile robots, because static barriers and even dynamic obstacles frequently exist in their paths
This paper presents the Markov Decision Process based probabilistic formal models for three obstacle-avoidance strategies of a mobile robot in an uncertain dynamic environment
Obstacle avoidance is a key technique for the design and applications of mobile robots, because static barriers and even dynamic obstacles frequently exist in their paths
Summary
Obstacle avoidance is a key technique for the design and applications of mobile robots, because static barriers and even dynamic obstacles frequently exist in their paths. The sensor and actuator noises, the time delay of network, and other factors may still lead to inaccurate operations of the robots themselves [8]. Due to these reasons, probability-based obstacle-avoidance algorithms have been put forward in recent years [9,10,11], which models both the movements of the obstacles and the operations of the robots as probabilistic events
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