{"id":102,"date":"2014-01-05T15:23:16","date_gmt":"2014-01-05T19:23:16","guid":{"rendered":"https:\/\/sites.bu.edu\/msl\/?page_id=102"},"modified":"2014-10-01T17:19:14","modified_gmt":"2014-10-01T21:19:14","slug":"persistmonitor","status":"publish","type":"page","link":"https:\/\/sites.bu.edu\/msl\/research\/persistmonitor\/","title":{"rendered":"Persistent Monitoring of Dynamic Environments"},"content":{"rendered":"

Background:<\/strong><\/p>\n

There are some tasks in real world that require to use a sensing robot to estimate field in a dynamic environment. The dynamic field can be ocean temperature, dust accumulation, forest density, oil and nuclear leakage and so on, see Figure 1. But the sensor usually has limited sensing range and the environment is huge. This requires the robot to move around in the environment and motivates the need to plan trajectories for the sensing agent. A trajectory, along which the robot can gather more information and minimize the uncertainties, is preferred to other trajectories.<\/p>\n

Research Goal:<\/strong><\/p>\n

Planning trajectories for a sensing robot to estimate field as accurate as possible in a dynamic environment, that is, balance estimation uncertainties in space and time.<\/p>\n

 <\/p>\n

\"MonitorTemp\"<\/a>
Figure 1: A persistent monitoring task using a underwater glider to estimate ocean temperature in an area of interest.<\/figcaption><\/figure>\n

Method:<\/strong><\/p>\n

Kalman filter is used to estimate the field. Path planning algorithms are used to plan trajectories to control the evolution of the estimation error covariance matrix.<\/p>\n

 <\/p>\n

Method 1:<\/strong><\/p>\n

Based on sampling-based motion planning algorithms, like RRT and RRT*, we proposed one randomized algorithm called\u00a0 Rapidly-exploring Random Cycles (RRC), see video below.<\/p>\n