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 for limit cycle avoidance in nonconvex obstacles (e.g. U-shape or deadend) in polar coordinate 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.
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 clustering-based genetic algorithm as TSPN solver https://github.com/shao-you/CBGA, where the neighborhood could be circles of different size.
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.