Bounding on Rough Terrain with the LittleDog Robot

A motion planning algorithm is described for bounding over rough terrain with the LittleDog robot. Unlike walking gaits, bounding is highly dynamic and cannot be planned with quasi-steady approximations. LittleDog is modeled as a planar five-link system, with a 16-dimensional state space; computing a plan over rough terrain in this high-dimensional state space that respects the kinodynamic constraints due to underactuation and motor limits is extremely challenging. Rapidly Exploring Random Trees (RRTs) are known for fast kinematic path planning in high-dimensional configuration spaces in the presence of obstacles, but search efficiency degrades rapidly with the addition of challenging dynamics. A computationally tractable planner for bounding was developed by modifying the RRT algorithm by using: (1) motion primitives to reduce the dimensionality of the problem; (2) Reachability Guidance, which dynamically changes the sampling distribution and distance metric to address differential constraints and discontinuous motion primitive dynamics; and (3) sampling with a Voronoi bias in a lower-dimensional “task space” for bounding. Short trajectories were demonstrated to work on the robot, however open-loop bounding is inherently unstable. A feedback controller based on transverse linearization was implemented, and shown in simulation to stabilize perturbations in the presence of noise and time delays.United States. Defense Advanced Research Projects Agency. Learning Locomotion Program (AFRL contract # FA8650-05-C-7262

The Manchester Color Wheel: validation in secondary school pupils

<p>Abstract</p> <p>Background</p> <p>As part of our research programme into facilitating improved ways of communicating with patients, especially about more sensitive clinical issues, we have been investigating whether there are any non-verbal methods that might aid this process. One such approach is to ask patients to choose a color in response to a particular question, for instance about health or psychological status, and for this purpose we developed the Manchester Color Wheel (MCW). This instrument consists of positive, neutral and negative colors and its validation in normal adults and those with anxiety or depression showed that it is responsive to change and reproducible. It also has the capacity to identify a positive frame of mind. We concluded that it might be a particularly useful instrument in adolescents and therefore this study aimed to validate it in a secondary school.</p> <p>Methods</p> <p>620 pupils (aged 11–17 years, mean age 14.0 years, 298 (48.1%) males, 322 (51.9%) females) at Sale Grammar School in Greater Manchester were asked to relate their mood to a MCW color and also complete the Hospital Anxiety Depression (HAD) questionnaire. To give these pupils an experience in science, 197 were divided into four subgroups for an ‘experiment’ to ascertain whether, compared to controls, a change in mood color choice could be induced by participation in sport, music or art activities.</p> <p>Results</p> <p>Although mood color and HAD depression score are unlikely to be measuring exactly the same psychological state, a negative mood color was chosen by 62.5% of HAD depressed compared to only 14.5% of HAD normal pupils (p < 0.001). In contrast, a positive mood color was chosen by 48.9% of normal and only 18.8% of depressed pupils (p < 0.001). In the ‘experiment’, compared to controls, all activities resulted in an increased choice of positive mood colors which reached significance for sport and music.</p> <p>Conclusion</p> <p>This study confirms the potential utility of the MCW to rapidly and easily assess a variety of health issues in large populations, including adolescents. Some of our results should also be of interest to educationalists.</p