Jeel Chatrola

We propose a novel, multi-layered planning approach for computing paths that satisfy both kinodynamic and spatiotemporal constraints. Our three-part framework first establishes potential sequences to meet spatial constraints, using them to calculate a geometric lead path. This path then guides an asymptotically optimal sampling-based kinodynamic planner, which minimizes an STL-robustness cost to jointly satisfy spatiotemporal and kinodynamic constraints. In our experiments, we test our method with a velocity-controlled Ackerman-car model and demonstrate significant efficiency gains compared to prior art. Additionally, our method is able to generate complex path maneuvers, such as crossovers, something that previous methods had not demonstrated.

In this paper, we propose a computationally efficient symbolic controller synthesis technique for multi-agent systems. The paper focuses on synthesizing distributed controllers enforcing local temporal logic specifications along with global safety specifications for multi-agent systems. To solve the problem in a computationally efficient way we leverage the concept of control barrier functions. In particular, we use a three-step bottom-up approach: first, the symbolic controllers for individual agents are synthesized to enforce local temporal logic specifications, then we use a notion of control barrier functions for symbolic models to compose controlled agent systems by removing unsafe transitions, and finally, we synthesize controller for the reduced composed system to ensure the satisfaction of local temporal logic specifications while ensuring global safety specification. The effectiveness of our approach is demonstrated on a multi-robot system by comparing it with the conventional monolithic symbolic control approach

Autonomous exploration in an unknown environment is widely studied and many exploration strategies exist. However, most of the works jeopardize the safety of the robot, the environment or both. The reason behind the violation of safety by most of the exploration algorithms in real-world applications is the ignorance of the following factors (i) the dynamics and dimensions of the robot, (ii) practical constraints on states (like constrained steering angle) and inputs (like actuator saturation), and (iii) hardware constraints such as sampling time, sensor noise, modelling uncertainties, etc. In this work, we propose an autonomous exploration framework for the 2-D exploration problem that considers the factors above to provide safety guarantees for the robot and the environment. The effectiveness of this method is shown in a high-fidelity simulation of different robots with onboard sensors in different simulation environments and real-world implementation.