1. Activity 1: Multi-sensor based Navigation of Intelligent Wheelchairs Theo Theodoridis and Huosheng Hu University of Essex 27 January 2012 Ecole Centrale de Lille Part-financed by the European Regional Development Fund

2. Part-financed by the European Regional Development Fund I.Outline of the task within the context of the project  This activity is focused on the development of tools for autonomous navigation of the wheelchair.  To deal with the problem of the prototyping of autonomous and intelligent wheelchairs, transporting people inside and outside buildings.  To localize itself in an a priori unknown environment, to detect and avoid the obstacles.  To be driven manually, semi-automatically or automatically depending on the state of the user.  The movement orders can come either from the user or the embedded system. The wheelchair can also have specific functionalities depending on the user. 2

3. Part-financed by the European Regional Development Fund 1.Outline of the task within the context of the project Activities at Essex has focused on the following major tasks: 1)Instrumentation (sensors, actuators, computer interface) and data fusing algorithms for real-time localisation and obstacle identification of intelligent wheelchairs. 2)Development of the navigation strategy in order to avoid the collision between the intelligent wheelchair and obstacles and to guarantee a safe functioning. 3)Numerical implementation, tests and validation of the algorithms on the prototype wheelchair. 3

4. Part-financed by the European Regional Development Fund II. Main results 4 1) Kinect Sensor for Obstacle Detection General Specs: · RGB and Depth Sensor · Resolution: 640x480 (307,200 depth sensors) · Accuracy: ~2mm · Capturing: 6m (max 14m) · Low Cost (£120), Low Power (830mA) · USB2 Connectivity · Microsoft Drivers Kinect Functions: · Depth maps · Silhouette extraction · Distance estimation · Planning and control · Tracking Works With: · Java · C# · C / C++

5. Part-financed by the European Regional Development Fund II. Main results Kinect data 5 Video show

6. Part-financed by the European Regional Development Fund II. Main results (a) Path planning and obstacle avoidance in a depth map, (b) 3D map rendering. 6 TimeStep 1: TimeStep 2: TimeStep 3: TimeStep 4: TimeStep 5:

7. Part-financed by the European Regional Development Fund II. Main results 2) GPS for outdoor navigation General Specs: Longitude and Latitude in Signed Decimal Degree format Position Accuracy (best case) ~2.5m CEP Works when Satellite Fix "Hot Starts" Remembering Previous Positions Signal Re-Acquisition < 1 second USB Access Interface 7 GPS Return Params: · Longitude · Latitude · Altitude · Heading · Velocity · Time · Date Works With: · Java · C# · C / C++ · LabView · Python · Visual Basic · Ruby

8. Part-financed by the European Regional Development Fund II. Main results 8 GPS data collection for navigation. Route: Car-park to Essex hotel (the main scenario for future tests).

9. Part-financed by the European Regional Development Fund Methodology: Tangent-Node Localisation Description: Incorporates: · Discrete node-based localisation · Sensor fusion · Assignment of N nodes · Obstacle avoidance · Reset coords at every node · Landmark-based feature detection Benefits: · Significant reduction of the odometry error · Problem decomposition in multiple directed goals · Computationally inexpensive and implementationaly easy · Robust navigation in N-dimensional and unstructured environments Applications: · Mobile robots; Wheelchairs · Flying robots; · Fish robots The robot’s actual path, implemented in 6 predefined node locations (n).

10. Part-financed by the European Regional Development Fund II. Main results Stateful Genetic Programming (GP) Traditional GP GP: A GA with a tree-based representation Individuals (trees) are constructed recurrently Solutions induce equations, algorithms Trees return single values, arrays, or data structures Stateful GP SGP: Introduces memory (state) to trees Stateful representations; Mental Model Metal models model the environment Read/Write operators are used To estimate a solution Decision making is more representative 10

11. Part-financed by the European Regional Development Fund II. Main results Stateful Localisation SGP is used here for localisation and modelling Environment is modeled via indexed memory (state) Decision making is accomplished by a GP tree Evolutionary process is utilised to construct adequate localizers 11

12. Part-financed by the European Regional Development Fund III. Future challenges and the work to be done Activities at Essex will focus on the following major tasks: 1)To develop SLAM algorithms for real-time localisation both indoor (US beacons+IMU) and outdoor (GPS+IMU). 2)Using Kinect sensor to develop the navigation strategy in order to avoid the collision between the intelligent wheelchair and obstacles for a safe travelling. 3)To deploy A* search algorithm and build statistical models for path conditions for the development of optimal path planning. 12