M Aditya Sharma supervised by Prof. K Madhava Krishna received his Master of Science in Computer Science and Engineering (CSE). Here’s a summary of his research work on Trajectory Planning for On-Road and Off-Road Scenarios:
Trajectory planning plays a critical role in autonomous navigation, where the emphasis lies on planning smooth, collision-free trajectories that comply with the robot’s kinematic constraints. It finds itself in various applications, such as autonomous driving, search and rescue, construction, and forestry. In the case of autonomous driving, the idea is to mimic human driving, which involves complex decisions like merging and overtaking, along with lower-level commands. However, standard approaches fail to capture the multi-modal characteristic of human driving. Although autonomous driving finds itself in structured, smooth, planar roads, applications like search and rescue and construction involve uneven terrains. Thus, there should be a distinction in the way trajectory optimization is approached in these scenarios, away from 2D approaches. The first study introduces two methods, which accomplish the task of accomplishing a given high-level driving task. The first method achieves the task of generating trajectories for multiple goals, by parallelizing existing state-of-the-art solvers using multiple CPU threads. The second method introduces a Multi-Convex MPC that is based on reformulating the collision avoidance and kinematic constraints and is solved using the Alternating Minimization(AM) technique. The second study introduces an approach to planning trajectories on uneven terrains. It leverages the digital terrain information, represented as a point cloud. The pose of the robot is represented in 6dof. The wheel terrain interaction and pose are expressed as a non-linear problem. The resulting pose prediction, along with Sampling based Model Predictive Control (SMPC), are used to sample smooth and stable trajectories. These studies collectively help advance the understanding of trajectory planning and its implementation in different scenarios.
July 2024