Associate Research Fellow  |  Liu, Jing-Sin  
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Research Descriptions

      Design and developement of a sensor-based autonomous navigation system  requires that a mobile robot is capable of exploring (interacting with its environment) and planning how to travel through an uncertain environment and executing the plan to accomplish the missions, e.g. mapping a region, gathering data, surveillance (target seeking, following or pursuit, and border patrol) in potentially hostile environment. To meet the requirement, the robot needs to solve the path/motion planning problem, i.e. generate a static or dynamic obstacle-avoiding path/trajectory that is optimal with respect to one or more kinematic or dynamic criteria.

 The following are some of our aims:

1. proof-of-concepts experiments for mobile robot navigation on unknown indoor environments

--SLAM  is the problem that the robot needs to build the map of the exploring environment and localize itself simultaneously. Accurate localization requires an accurate map, and an accurate map cannot be built without accurate localization. We develop a 3D mapping system using a rotating laser range finder based on DT +linear simplex for map alignment

--Waypoint navigation system  in polar coordinate for limit cycle avoidance in unknown environment with nonconvex obstacles (e.g. U-shape or deadend) to complement goal-directed reactive navigation  

--surveillance mobile robot performing smoother wall following and human detection using interval type-2 fuzzy logic.The system has two primary components, i.e. wall following based on interval type-2 fuzzy logic control and sensing of human in executing trajectories or actions.The human detection sensor allows the effective detection of a close-in human in the wall-following route, thus enabling wall following could be performed in a human friendly manner.

--The harmonic potential field (HPF) is efficient for mobile robots to generate smooth path without local minima for obstacle avoidance. We implemented the integration of the pure pursuit algorithm and HPF-based streamline paths compatible with the curvature constraint to develop an obstacle avoidance system. The system has been demonstrated works well in an environment with sparsely distributed circular obstacles that make possible the search of streamlines with curvatures within the vehicle''s nonholonomic constraint.

2. Path planning algorithms search for the optimal or near-optimal paths linking the given initial and target configurations that optimize an objective function (eg the path length) and satisfy the hard and soft constraints imposed on the vehicle, such as avoiding obstacles or response to changes in the environment. Now we develop soft computing based approaches for mobile robot path planning for data gethering in wireless sensor networkand navigation.We develop an efficient evolutionary path planner for data-collection problem using data mule in wsn or using uav for mapping a region, which  is a variant of Traveling Salesman Problem with Neighborhood (TSPN). We develop an open source code of a very scalable clustering-based genetic algorithm as TSPN solver,

which is very scalable and can deal with the general case of neighborhoods with varying radii and various amount of overlaps.


3.The ability to plan a path and then to follow the planned path on flat and non-flat terrain in an optimal fashion is central to indoor and outdoor mobile robots navigation. Such a planner requires an accurate geometric 3D environment map that encodes real terrain geometry. We propose a geodesic path planner based on gradient-descent of an energy function that is model independent, analytical and operate on the terrain modeled as a parametric surface or smooth manifold to compute a length minimizing path on a given surface. It generates continuously differentiable locally geodesic paths based on nonlinear geodesic equations determined by surface primitives on which the mobile robot moves. The planner developed is implemented in a planning application and validated through a simulated uav flight that follows the shortest length path towards the goal position.

4.streamline-based path planning

Harmonic potentail function (HPF) is the solution to two-point boundary value problem of Laplace equation in a closed domain. The streamlines, defined as the gardient of HPF at each point of the domain, serve as the guidance vector field for navigation without trapped in local minima. We develop a HPF-based obsatcle avoidance system that integrates with pure pursuit  for transition to another streamline for curvature-constrained mobile robot.To enhance the accuracy of gradient computation, log-space harmonic function is adopted.

5. Time-optimal trajectory.

-Simulation-based evaluation of the effect of geometric path characteristics on time-optimal velocity along symmetric parametric polynomial lane-change curves followed by a unicyle subject to the velocity and acceleration constraints is studied on a path basis. We verify that a lane-change path that is shorter in length and lower in maximum curvature is faster.

-Time-optimal trajectory of Dubins'' vehicle in an environment with or without obstacle is studied via Hamilton-Jacobi equation. Numerical methods based on fast sweeping methods (semi--Lagrangian and Godunov numerical Hamiltonian) are employed to obtain viscolsity solutions with comparison to analytical Dubins path in terms of accuracy and convergence.