Searching for targets in 3D environments can be formulated as submodular maximization problems with routing constraints. However, it involves solving two NP-hard problems: the maximal coverage problem and the traveling salesman problem. Since the time constraint is critical for search problems, this research proposes a Computation-Aware Search for Multiple Objects (CASMO) algorithm to further consider the computational time in the cost constraints. Due to the submdularity, the greedy algorithm achieves $\frac{1}{2}\left( {1 - \frac{1}{e}} \right)\overline {OPT} $, where $\overline {OPT} $ is the approximate optimum. The experiment results show that the proposed algorithm outperforms state-of-the-art approaches in multi-object search.

Efficient task allocation is a challenge for multirobot search. The multi-robot search problem is reformulated as submodular maximization subject to intersection system constraints. The objective function is submodular and consists of a coverage function to cover environments and a balancing function to efficiently dispatch robots. The intersection system is composed of routing and clustering constraints. The experiment results show that the proposed approach outperforms state-ofthe-art methods in multi-robot search.

Estimation of people tracking may become divergent in the presence of occlusion. Since the interactions between people and environments can be mathematically modeled and probabilistically estimated, stream field based tracking provides the solution where the state of the occluded people is estimated by inferring the interactive force between the virtual goal of a person and environmental features. Such tracker suffers from high computation complexity because of the multi-hypotheses of the person's goal and feature-based map. Therefore, this paper proposes a novel virtual force field (VFF) based tracking algorithm that can be realized with a single hypothesis for the person's goal and grid-based map. The occupied grids generate repulsive forces while the person's goal generates attractive force in the virtual force field. Since the virtual force field based tracking integrates map, person, and the person's goal, the position of the person sheltered by the environment can be accurately estimated in unknown environments. Compared with the Kalman filter with constant acceleration (CA) model and stream field based algorithms, our proposed scheme significantly improves the tracking accuracy in case of occlusion.

Self-localization and tracking a moving object is a key technology for service robot interactive applications. Most tracking algorithms focus on how to correctly estimate the acceleration, velocity, and position of the moving objects based on the prior states and sensor information. What has not been studied so far is tracking the partially observable moving object which is often hidden from a robot's view using lasers. Applying the traditional tracking algorithms will lead to the divergent estimation of the object's position. Therefore, in this paper, we propose a novel laser based partially observable moving object tracking and self-localization algorithm. We adopt stream functions and Rao-Blackwellised particle filter (RBPF) to predict where the partially observable moving object will go in previously mapped environmental features. Moreover, a robot can localize itself and track such a moving object according to stream field. Our experimental results show the proposed algorithm can localize itself and track the partially observable moving object effectively.