Application of Recent Approximate Dynamic Programming Methods for Navigation Problems

Abstract

Navigation problems include the task of determining the control input under various constraints for systems such as mobile robots subject to uncertain disturbance. Such tasks can be modeled as constrained stochastic control problems. In order to solve these control problems, one may try to utilize the dynamic programming(DP) methods which rely on the concept of optimal value function. However, in most real-world problems, this trial would give us many difficulties; for examples, the exact system model may not be known; the computation of the optimal control policy may be impossible; and/or a huge amount of computing resource may be in need. As a strategy to overcome the difficulties of DP, one can utilize ADP(approximate dynamic programming) methods, which find suboptimal control policies resorting to approximate value functions. In this paper, we apply recently proposed ADP methods to a class of navigation problems having complex constraints, and observe the resultant performance characteristics.

Vision Sensor-Based Driving Algorithm for Indoor Automatic Guided Vehicles

“…The use of vision systems has a significant effect on the development of systems that are flexible and efficient, with low maintenance costs, especially mobile equipment. AGV systems that utilize a vision-based sensor and computer image processing for navigation can improve the flexibility of system because more environmental information can be observed, which makes the system robust [13][14][15].…”