Predicting the motion of observed entities benefits humans almost seamlessly. The same benefits can be proliferated to mobile autonomous systems if we have a reliable, real-time solution to predict the motion of any object of interest, be it the host’s own motion or that of an observed foreign object. In this work, a novel Multi-Variable State Prediction (MVSP) methodology is devised for real-time trajectory prediction. MVSP incorporates cascaded stages of HMM with Viterbi algorithm and probabilistic quantization for accurately predicting the motion characteristics of the moving object. The overall scheme is employed to predict the motion of moving objects in a 3D space. The proposed approach is verified on both synthetically generated data sequences and data-sets captured from real-life experiments. For a practical scenario, the experiments resulted in an RMS error of 0.6m for a predicted distance of ~18m demonstrating the effectiveness and accuracy of the proposed methodology.

Path planning and smooth trajectory generation are critical capabilities for efficient navigation of mobile robots operating in challenging and cluttered environments. For real time and autonomous operations of mobile robots, intelligent algorithms, efficient and light-weight compute, and smooth trajectory are key components. In this work, we propose an intelligent, probabilistic Gaussian mixture model driven Bi-RRT (PG-RRT) algorithm which generates nodes in the most probable regions for faster convergence. The proposed algorithm is tested in various simulated environments including highly cluttered obstacles. The experimental results of PG-RRT are compared with state-of-the-art path planning algorithms. The results show significant improvement in the number of iterations (up to 26X) and runtime (up to 17.5X) demonstrating the superiority of the proposed PG-RRT algorithm.

Optimal path planning and smooth trajectory planning are critical for effective navigation of mobile robots working towards accomplishing complex missions. For autonomous, real time and extended operations of mobile robots, the navigation capability needs to be executed at the edge. Thus, efficient compute, minimum memory utilization and smooth trajectory are the key parameters that drive the successful operation of autonomous mobile robots. Traditionally, navigation solutions focus on developing robust path planning algorithms which are complex and compute/memory intensive. Bidirectional-RRT(Bi-RRT) based path planning algorithms have gained increased attention due to their effectiveness and computational efficiency in generating feasible paths. However, these algorithms neither optimize memory nor guarantee smooth trajectories. To this end, we propose a kinematically constrained Bi-RRT (KB-RRT) algorithm, which restricts the number of nodes generated without compromising on the accuracy and incorporates kinodynamic constraints for generating smooth trajectories, together resulting in efficient navigation of autonomous mobile robots. The proposed algorithm is tested in a highly cluttered environment on an Ackermannsteering vehicle model with severe kinematic constraints. The experimental results demonstrate that KB-RRT achieves three times (3 X) better performance in terms of convergence rate and memory utilization compared to a standard Bi-RRT algorithm.