Dynamic Path Planning and Replanning for Mobile Robots using RRT*

It is necessary for a mobile robot to be able to efficiently plan a path from its starting, or current, location to a desired goal location. This is a trivial task when the environment is static. However, the operational environment of the robot is rarely static, and it often has many moving obstacles. The robot may encounter one, or many, of these unknown and unpredictable moving obstacles. The robot will need to decide how to proceed when one of these obstacles is obstructing it's path. A method of dynamic replanning using RRT* is presented. The robot will modify it's current plan when an unknown random moving obstacle obstructs the path. Various experimental results show the effectiveness of the proposed method

Roller Chain-Like Robot for Steel Bridge Inspection

This paper presents a novel design of steel bridge/structure inspection robot. Compared to most existing robots designed to work on particular surface contour of steel structures such as flat or curving, the proposed roller chain-like robot can implement and transfer smoothly on many kind of steel surfaces. The developed robot can be applied to inspection tasks for steel bridges with complicated structures. The robot is able to carry cameras, sensors for visual and specialized examination. Rigorous analysis of robot kinematics, adhesion force and turn-over failure has been conducted to demonstrate the stability of the proposed design. Mechanical and magnetic force analysis together with turn-over failure investigation can serve as an useful framework for designing various steel climbing robots in the future. Experimental results and field deployments prove the adhesion, climbing, inspection capability of the developed robot

Autonomous Robotic System using Non-Destructive Evaluation methods for Bridge Deck Inspection

Bridge condition assessment is important to maintain the quality of highway roads for public transport. Bridge deterioration with time is inevitable due to aging material, environmental wear and in some cases, inadequate maintenance. Non-destructive evaluation (NDE) methods are preferred for condition assessment for bridges, concrete buildings, and other civil structures. Some examples of NDE methods are ground penetrating radar (GPR), acoustic emission, and electrical resistivity (ER). NDE methods provide the ability to inspect a structure without causing any damage to the structure in the process. In addition, NDE methods typically cost less than other methods, since they do not require inspection sites to be evacuated prior to inspection, which greatly reduces the cost of safety related issues during the inspection process. In this paper, an autonomous robotic system equipped with three different NDE sensors is presented. The system employs GPR, ER, and a camera for data collection. The system is capable of performing real-time, cost-effective bridge deck inspection, and is comprised of a mechanical robot design and machine learning and pattern recognition methods for automated steel rebar picking to provide realtime condition maps of the corrosive deck environments

Cooperative Control, Learning and Sensing in Mobile Sensor Networks

Mobile sensor networks (MSNs) have great potential in many applications including environment exploring and monitoring; search and rescue; cooperative detection of toxic chemicals, etc. Motivated by the broad and important applications of MSNs and inspired by the cooperative ability and the intelligence of fish schools and bird flocks, this dissertation develops cooperative control, learning and sensing algorithms in a distributed fashion for MSNs to realize coordinated motion control and intelligent situational awareness. The proposed algorithms can allow MSNs to track a moving target efficiently in cluttered environments and even when only a very small subset of the sensor nodes know the information of the target; adjust their size (shrink/recover) in order to adapt to complex environments while maintaining the network connectivity and topology; form a lattice structure and maintain the cohesion even when the measurements are corrupted by noise; track multiple moving targets simultaneously and efficiently in a dynamic fashion; learn to evade the enemy (predators) in a distributed fashion while maintaining the network connectivity and topology; estimate and build the map of a scalar field. We conducted several experiments using both simulation and real mobile robots to show the effectiveness of the proposed algorithms. We also extended our framework to cooperative and active sensing in which the mobile sensors have the ability to adjust their movements to adapt to the environments in order to improve the sensing performance in a distributed fashion.School of Electrical & Computer Engineerin