Fleet Organization and Decision Making in Heterogeneous Vehicle Teams to Meet Task Requirements in Uncertain Environments

Recent advances in robotic sensing and control have enabled the application of robotic systems in a variety of unstructured natural environments, including robotic agriculture, underwater exploration, and search and rescue in caves. In the meantime, the progress in multi-robot decision-making algorithms and the growth in computational power allow robot swarms to work together to complete missions. Despite the achievements made by these decision-making frameworks, most of them are applied to a team of homogeneous robots for one specific task and require a structured known environment. As robotic systems start to operate on more complex missions, e.g. a military mission that contains several scout, breach, and delivery tasks at different locations, it is important to consider uncertainty in the environment and heterogeneity of the robots and tasks.

In this project we aim to develop a general feedback teaming framework that dynamically characterize the heterogeneities and uncertainties in energy, time, vehicle states, and task states; and configure robust and agile teams and schedules despite the heterogeneities and uncertainties.

[1] B. Fu, W. Smith, D. Rizzo, M. Castanier, and K. Barton, “Heterogeneous vehicle routing and teaming with Gaussian distributed energy uncertainty,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020, pp. 4315-4322 [PDF] [Presentation]

[2] Article: Assembling the Right Team for the Mission

RaD-VIO: Rangefinder-aided Downward Visual-Inertial Odometry

State-of-the-art forward facing monocular visual-inertial odometry algorithms are often brittle in practice, especially whilst dealing with initialization and motion in directions that render the state unobservable. In such cases having a reliable complementary odometry algorithm enables robust and resilient flight. Using the common local planarity assumption, we present a fast, dense, and direct frame-to-frame visual-inertial odometry algorithm for downward facing cameras that minimizes a joint cost function involving a homography based photometric cost and an IMU regularization term. Via extensive evaluation in a variety of scenarios we demonstrate superior performance than existing state-of-the-art downward facing odometry algorithms for Micro Aerial Vehicles (MAVs).

[1] B. Fu, K. S. Shankar, N. Michael, “Rad-VIO: Rangefinder-aided downward visual-inertial odometry,” in 2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019, pp. 1841-1847. [PDF]

Control Strategy for 4-Wheel-Drive Plug-in Hybrid Electric Vehicles

We developed a rule-based control strategy, which achieved a 24.41% fuel consumption decrease in simulation compared to result of the original internal combustion engine vehicle; optimized strategy parameters based on genetic algorithm and achieved an additional 1.53% fuel consumption reduction; and conducted extensive hardware-in-the-loop test of hybrid control unit to prove the function, reliability, robustness.

[1] J. Hao, Z. Yu, Z. Zhao, X. Zhan, B. Fu, and P. Shen, “Development and optimization of energy management strategy for four-wheel-drive plug-in hybrid electric vehicle,” Mechatronics Journal, 2018, no. 8, pp.12-19, 30.