We consider an optimal stopping formulation of the mission monitoring problem, where a monitor vehicle must remain in close proximity to an autonomous robot that stochastically follows a predicted trajectory. This problem arises in a diverse range of scenarios, such as autonomous underwater vehicles supervised by surface vessels, pedestrians monitored by aerial vehicles, and animals monitored by agricultural robots. The key problem characteristics we consider are that the monitor must remain stationary while observing the robot, robot motion is modelled in general as a stochastic process, and observations are modelled as a spatial probability distribution. We propose a resolution-complete algorithm that runs in polynomial time. The algorithm is based on a sweep-plane approach and generates a motion plan that maximises the expected observation time and value. A variety of stochastic models may be used to represent the robot trajectory. We present results with data drawn from real AUV missions, a real pedestrian trajectory dataset and Monte Carlo simulations. Our results demonstrate the performance and behaviour of our algorithm, and relevance to a variety of applications. Index Terms-path planning, planning under uncertainty, optimal stopping, combinatorial optimisation, mission monitoring, marine robotics, pedestrian tracking, agricultural robotics.
Classifying objects in complex unknown environments is a challenging problem in robotics and is fundamental in many applications. Modern sensors and sophisticated perception algorithms extract rich 3D textured information, but are limited to the data that are collected from a given location or path. We are interested in closing the loop around perception and planning, in particular to plan paths for better perceptual data, and focus on the problem of planning scanning sequences to improve object classification from range data. We formulate a novel time-constrained active classification problem and propose solution algorithms that employ a variation of Monte Carlo tree search to plan non-myopically. Our algorithms use a particle filter combined with Gaussian process regression to estimate joint distributions of object class and pose. This estimator is used in planning to generate a probabilistic belief about the state of objects in a scene, and also to generate beliefs for predicted sensor observations from future viewpoints. These predictions consider occlusions arising from predicted object positions and shapes. We evaluate our algorithms in simulation, in comparison to passive and greedy strategies. We also describe similar experiments where the algorithms are implemented online, using a mobile ground robot in a farm environment. Results indicate that our non-myopic approach outperforms both passive and myopic strategies, and clearly show the benefit of active perception for outdoor object classification.
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