There are usually multiple criteria in a high-level task. Conflict and incomparability among criteria leads to a multi-objective path-planning problem.
Obtaining a set of Pareto optimal paths in a path planning problem with multiple objectives helps the planner finding the desired path. Inspired by the decomposition approach in multi-objective optimization, we introduce a forest that consists of a set of RRT* trees to find a set of Pareto optimal paths.
A path topology is a natural and efficient expression in high-level intent. Introducing high-level information to a robot's task with objective generates a topology-based optimal path-planning problem. We adapt homotopy class for classifying path topologies and look at finding the optimal paths of a set of homotopy classes.
We propose an algorithm that returns the optimal paths of different homotopy classes simultaneously.
A language interface helps a robot receive natural information from untrained people. We are developing algorithms that convert a language instruction into a path by integrating language understanding with path-planning. The path-planner should be capable of dealing with the ambiguity in language.
Daqing Yi and Michael A. Goodrich, Supporting task-oriented collaboration in human-robot teams using semantic-based path planning. Proc. SPIE 9084, Unmanned Systems Technology XVI, 90840D, June 2014.[Paper] [Preprint]
Modeling the dynamics of optimization algorithm into discrete-time system allows the import of the nonlinear system analysis. We decompose the PSO into networked component and introduce input-to-state stability analysis to understand the condition of the algorithm convergence.
In the collaborative search task, the robot's motion is expected to maximize the information while be constrained by a human's motion. By utilizing the submodularity in a coverage model and converting the human's constraint into a multi-partite graph structure, we proposed the algorithm that finds the optimal path given enough run time.
Michael A. Goodrich and Daqing Yi, Toward Task-Based Mental Models of Human-Robot Teaming: A Bayesian Approach. Virtual Augmented and Mixed Reality. Designing and Developing Augmented and Virtual Environments, July 2013. [Paper] [Preprint]
Random jitter imposes the noise to the neuron system. However, it recovers the information lost in the conversion. Analyses explain how the random jitter actually enhances the human's visual system and how it can be introduced to improve the performance of any visual sensory system.
Daqing Yi, Ping Jiang, Edward Mallen, Xiaonian Wang and Jin Zhu, Enhancement of image luminance resolution by imposing random jitter. Neural Computing & Applications, vol. 20, 2011. [Paper]
Daqing Yi, Ping Jiang and Jin Zhu, A Simple Neural Network for Enhancement of Image Acuity by Fixational Instability. Advances in Neural Networks-ISNN 2009, Lecture Notes in Computer Science, May 2009. [Paper]
In a planar motion control with arbitrarily mounted camera, the observation model is defined by an unknown homography matrix. We proposed the control algorithm, which consists of the Nussbaum gain probing (coarse tuning) and the iterative learning (fine tuning). Given any expected trajectory, the algorithm could learn the system input by the iterative trial process without deployment information.
Daqing Yi, Jian Wu and Ping Jiang, Iterative learning control for visual servoing with unknown homography matrix. ICCA 2007,May 2007. [Paper]