Department Publications

This research paper presents a novel track based stair-climbing wheelchair design with improved stability and performance. The proposed design has an adaptable arrangement to the staircase especially at the top of the staircase. In the existing wheelchair designs, this particular feature has not been adequately addressed. The proposed design consists of body frame and supporting rubber track arms. Of the two main operations; ascending and descending, the most critical operations is at the top of the staircase, which is smoothly controlled by following the staircase profile. This motion is achieved by properly synchronizing the rubber track arms according to a pre-planned motion profile. As a result, user does not feel sudden motions in position and attitude. Furthermore, the proposed wheelchair is adaptable to any staircase.

Reinforcement learning algorithms have been successfully trained for games like GO, Atari, and Chess in simulated environments. However, in cue sport-based games like Carrom, real world is unpredictable unlike in Chess and GO due to the stochastic nature of the gameplay as well as the effect of external factors such as friction combined with multiple collisions. Hence, solely training in a simulated platform for games like Billiard and Carrom, which need precise execution of a shot, would not be ideal in actual gameplay. This paper presents a real-time vision based efficient robotic system to play Carrom against a proficient human opponent. We demonstrate the challenges of adopting a reinforcement learning algorithm beyond simulations in implementing a strategic gameplay for the robotic system. We currently achieve an overall shot accuracy of 70.6% by combining heuristic and reinforcement learning …