Path Planning for Shepherding a Swarm in a Cluttered Environment using Differential Evolution

Shepherding involves herding a swarm of agents (\emph{sheep}) by another a control agent (\emph{sheepdog}) towards a goal. Multiple approaches have been documented in the literature to model this behaviour. In this paper, we present a modification to a well-known shepherding approach, and show, via simulation, that this modification improves shepherding efficacy. We then argue that given complexity arising from obstacles laden environments, path planning approaches could further enhance this model. To validate this hypothesis, we present a 2-stage evolutionary-based path planning algorithm for shepherding a swarm of agents in 2D environments. In the first stage, the algorithm attempts to find the best path for the sheepdog to move from its initial location to a strategic driving location behind the sheep. In the second stage, it calculates and optimises a path for the sheep. It does so by using \emph{way points} on that path as the sequential sub-goals for the sheepdog to aim towards. The proposed algorithm is evaluated in obstacle laden environments via simulation with further improvements achieved

Program evaluation of a community-based model for driver off-road assessment in post-acute ABI

Background: Social conditions from the pandemic forced changes to outpatient rehabilitation programs, necessitating pre-driving assessment be administered via telehealth, an alternative model of OT service-delivery. As a result, Shepherd Pathways adapted the clinical pre-driving program for telehealth service delivery. Program outcomes had not been formally assessed. Purpose: The purpose of this Capstone project was to conduct a formal program evaluation to collect, analyze, and use data to evaluate effectiveness and efficiency of a clinical return to drive program delivered via telehealth (CDC, n.d.; The university of Kansas toolbox, n.d.). The program evaluation examined the OT process and scope of services for driver off-road assessment at Shepherd Pathways, in Atlanta, Georgia, and addressed the following objectives. Does Shepherd Pathways clinical pre-driving program meet the needs of acquired brain injury (ABI) clients in a traditional, hybrid, or telehealth model? What are the current practices (assessments and interventions) used within an OT led clinical pre-driving program delivered via telehealth? Does an OT clinical pre-driving program delivered via telehealth result in an on-road driving evaluation (ORDE) outcome of unrestricted driving for ABI clients? Theoretical Framework. A pragmatic research approach (Creswell & Creswell, 2018) and Canadian model of occupational performance-engagement (CMOP-E) and the Ecology of Human Performance (EHP) (Dunn, 1984) influenced the approach. Methods. Program evaluation identified outcomes of a telehealth approach for return to driving. A logic model was used to identify main components of the program and showed relationships among the telehealth pre-driving program goals, objectives, activities, and outcome measurements (Adu, 2017). Results. Research questions were answered-the needs of individuals with ABI were met in the program. OT telehealth clinical standards of practice for the pre-driving program were identified and successful ORDE outcomes indicated a positive relationship between use of telehealth and return to drive after ABI. Conclusions: A logic model illustrated the processes developed for OT driver-off road assessment during the COVID-19 pandemic from March 2020 - December 2020 at Shepherd Pathways. Results indicated a high percentage of program effectiveness, ORDE referrals, and ORDE passing rates for participants with ABI