Multiobjective Mission Route Planning Problem: A Neural Network-Based Forecasting Model for Mission Planning

• Published in: IEEE transactions on intelligent transportation systems Vol. 22; no. 1; pp. 430 - 442
• Main Authors: Biswas, Sumana, Anavatti, Sreenatha G., Garratt, Matthew A.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Barriers; Collision avoidance; Completion time; Dynamic environments; Forecasting; forecasting model; Mathematical models; Mission planning; Missions; multiobjective optimization; Multiple objective analysis; Neural networks; Obstacle avoidance; Optimization; Planning; Prediction models; Predictive models; Robots; Route planning; route planning problem; Task analysis; task assignment; Trajectory planning; Vehicle dynamics; Vehicles
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2019.2960057

This paper presents a three-layered approach for the mission route planning problems involving a team of autonomous vehicles where they have to collectively navigate to a number of target locations in an environment with both static and dynamic obstacles. The first layer computes the maximum distance that need to be traveled to complete a mission by a team of vehicles. We have developed a nearest-neighbor-search based approach to assign closely located tasks to each vehicle in the team. We developed a stochastic optimization based path planning algorithm that can compute the collision-free (with both static and dynamic obstacles) trajectory for a vehicle to navigate from start to the target location. By combining task assignment with path planning algorithm, we can estimate the maximum traveled distance for a mission with a team of vehicles. The second layer determines the optimal number of vehicles required for a mission based on any user defined constraint by casting it as a multiobjective optimization problem with two competing objectives, i.e. time vs cost. The methods derived in layer one are utilized to evaluate the objective functions in layer two. Finally, we have proposed a data driven neural network-based prediction model that will forecast the mission completion time with a reasonable accuracy which will utilize the historical information of the previous missions. The forecasting model is intended to facilitate the effective planning of parallel and subsequent missions. We have demonstrated the effectiveness of our approach with numerical simulation results for every layer mentioned above.