We develop a localization method to enable a team of mobile robots to search for multiple unknown transient radio sources. Because of signal source anonymity, short transmission durations, and dynamic transmission patterns, robots cannot treat the radio sources as continuous radio beacons. Moreover, robots do not know the source transmission power and have limited sensing ranges. To cope with these challenges, we pair up robots and develop a cooperative sensing model using signal strength ratios from the paired robots. We formally prove that the joint conditional posterior probability of source locations for the m-robot team can be obtained by combining the pairwise joint posterior probabilities, which can be derived from signal strength ratios. Moreover, we propose a pairwise ridge walking algorithm (PRWA) to coordinate the robot pairs based on the clustering of high-probability regions and the minimization of local Shannon entropy. We have implemented and validated the algorithm under both the hardware-driven simulation and physical experiments. Experimental results show that the PRWA-based localization scheme consistently outperforms the other four heuristics.

We develop a decentralized algorithm to coordinate a group of mobile robots to search for unknown and transient radio sources. In addition to limited mobility and ranges of communication and sensing, the robot team has to deal with challenges from signal source anonymity, short transmission duration, and variable transmission power. We propose a two-step approach: first, we decentralize belief functions that robots use to track source locations using checkpoint-based synchronization, and second, we propose a decentralized planning strategy to coordinate robots to ensure the existence of checkpoints. We analyze memory usage, data amount in communication, and searching time for the proposed algorithm. We have implemented the proposed algorithm and compared it with two heuristics. The experiment results show that our algorithm successfully trades a modest amount of memory for the fastest searching time among the three methods.

We report system and algorithm developments that utilize a single mobile robot to simultaneously localize multiple unknown transient radio sources. Because of signal source anonymity, short transmission durations, and dynamic transmission patterns, the robot cannot treat the radio sources as continuous radio beacons. To deal with this challenging localization problem, we model the radio source behaviors using a novel spatiotemporal probability occupancy grid that captures transient characteristics of radio transmissions and tracks posterior probability distributions of radio sources. As a Monte Carlo method, a ridge walking motion planning algorithm is proposed to enable the robot to efficiently traverse the high-probability regions to accelerate the convergence of the posterior probability distribution. We also formally show that the time to find a radio source is insensitive to the number of radio sources, and hence, our algorithm has great scalability. We have implemented the algorithms and extensively tested them in comparison with two heuristic methods: a random walk and a fixed-route patrol. The localization time of our algorithms is consistently shorter than that of the two heuristic methods.

We develop a localization method enabling a team of mobile robots to search for multiple unknown transient radio sources. Due to signal source anonymity, short transmission durations, and dynamic transmission patterns, robots cannot treat the radio sources as continuous radio beacons. Moreover, robots do not know the source transmission power and have limited sensing ranges. To cope with these challenges, we pair up robots and develop a sensing model using the signal strength ratio from the paired robots. We formally prove that the sensed conditional joint posterior probability of source locations for the m-robot team can be obtained by combining the pairwise joint posterior probabilities, which can be derived from signal strength ratios. Moreover, we propose a pairwise ridge walking algorithm (PRWA) to coordinate the robot pairs based on the clustering of high probability regions and the minimization of local Shannon entropy. We have implemented and validated the algorithm under hardware-driven simulation.

A mobile robot with a limited sensing range is deployed to search for a stationary target that intermittently emits short duration signals. The searching mission is accomplished as soon as the robot receives a signal from the target. We propose the expected searching time (EST) as a primary metric to evaluate different robot motion plans under different robot configurations. To illustrate the proposed metric, we present two case studies. In the first case, we analyze two common motion plans: a slap method (SM) and a random walk (RW). The EST analysis shows that the SM is asymptotically faster than the RW when the searching space size increases. In the second case, we compare a team ofnhomogeneous low-cost robots with a super robot that has the sensing range equal to that of the summation of thenrobots. Our analysis shows that the low-cost robot team takes Θ(1/n) time, while the super robot takes Θ(1/√n) time asn→ ∞. Our metrics successfully demonstrate their ability in assessing the searching performance. The analytical results are also confirmed in simulation and physical experiments.

We report our system and algorithm developments that enable a single mobile robot equipped with a directional antenna to simultaneously localize multiple unknown transient radio sources. Due to signal source anonymity, short transmission durations, and dynamic transmission patterns, the robot cannot treat the radio sources as continuous radio beacons.We model the radio source behaviors using a novel spatiotemporal probability occupancy grid (SPOG) that captures transient characteristics of radio transmissions and tracks the spatiotemporal posterior probability distribution of the radio transmissions. As a Monte Carlo method, we propose a ridge walking motion planning algorithm that enables the robot to efficiently traverse the high probability regions to accelerate the convergence of the posterior probability distribution. We have implemented the algorithms and the experiment results show that our method consistently outperforms methods such as a random walk or a fixed-route patrol mechanism.