Multi-objective exploration and search for autonomous rescue robots

An Integrated Strategy for Autonomous Exploration of Spatial Processes in Unknown Environments

Exploration of spatial processes, such as radioactivity or temperature is a fundamental task in many robotic applications. In the literature, robotic exploration is mainly carried out for applications where the environment is a priori known. However, for most real life applications this assumption often does not hold, specifically for disaster scenarios. In this paper, we propose a novel integrated strategy that allows a robot to explore a spatial process of interest in an unknown environment. To this end, we build upon two major blocks. First, we propose the use of GP to model the spatial process of interest, and process entropy to drive the exploration. Second, we employ registration algorithms for robot mapping and localization, and frontier-based exploration to explore the environment. However, map and process exploration can be conflicting goals. Our integrated strategy fuses the two aforementioned blocks through a trade-off between process and map exploration. We carry out extensive evaluations of our algorithm in simulated environments with respect to different baselines and environment setups using simulated GP data as a process at hand. Additionally, we perform experimental verification with a mobile holonomic robot exploring a simulated process in an unknown labyrinth environment. Demonstrated results show that our integrated strategy outperforms both frontier-based and GP entropy-driven exploration strategies.

A sampling-based multi-tree fusion algorithm for frontier detection

Autonomous exploration is a key step toward real robotic autonomy. Among various approaches for autonomous exploration, frontier-based methods are most commonly used. One efficient method of frontier detection exploits the idea of the rapidly-exploring random tree and uses tree edges to search for frontiers. However, this method usually needs to consume a lot of memory resources and searches for frontiers slowly in the environments where random trees are not easy to grow (unfavorable environments). In this article, a sampling-based multi-tree fusion algorithm for frontier detection is proposed. Firstly, the random tree’s growing and storage rules are changed so that the disadvantage of its slow growing under unfavorable environments is overcome. Secondly, a block structure is proposed to judge whether tree nodes in a block play a decisive role in frontier detection, so that a large number of redundant tree nodes can be deleted. Finally, two random trees with different growing rules are fused to speed up frontier detection. Experimental results in both simulated and real environments demonstrate that our algorithm for frontier detection consumes fewer memory resources and shows better performances in unfavorable environments.

Spiking Elementary Motion Detector in Neuromorphic Systems

Apparent motion of the surroundings on an agent's retina can be used to navigate through cluttered environments, avoid collisions with obstacles, or track targets of interest. The pattern of apparent motion of objects, (i.e., the optic flow), contains spatial information about the surrounding environment. For a small, fast-moving agent, as used in search and rescue missions, it is crucial to estimate the distance to close-by objects to avoid collisions quickly. This estimation cannot be done by conventional methods, such as frame-based optic flow estimation, given the size, power, and latency constraints of the necessary hardware. A practical alternative makes use of event-based vision sensors. Contrary to the frame-based approach, they produce so-called events only when there are changes in the visual scene. We propose a novel asynchronous circuit, the spiking elementary motion detector (sEMD), composed of a single silicon neuron and synapse, to detect elementary motion from an event-based vision sensor. The sEMD encodes the time an object's image needs to travel across the retina into a burst of spikes. The number of spikes within the burst is proportional to the speed of events across the retina. A fast but imprecise estimate of the time-to-travel can already be obtained from the first two spikes of a burst and refined by subsequent interspike intervals. The latter encoding scheme is possible due to an adaptive nonlinear synaptic efficacy scaling. We show that the sEMD can be used to compute a collision avoidance direction in the context of robotic navigation in a cluttered outdoor environment and compared the collision avoidance direction to a frame-based algorithm. The proposed computational principle constitutes a generic spiking temporal correlation detector that can be applied to other sensory modalities (e.g., sound localization), and it provides a novel perspective to gating information in spiking neural networks.

Complete and optimal visibility-based pursuit-evasion

This paper computes a minimum-length pursuer trajectory that solves a visibility-based pursuit-evasion problem in which a single pursuer moving through a simply-connected polygonal environment seeks to locate an evader which may move arbitrarily fast, using an omni-directional field-of-view that extends to the environment boundary. We present a complete algorithm that computes a minimum-cost pursuer trajectory that ensures that the evader is captured, or reports in finite time that no such trajectory exists. This result improves upon the known algorithm of Guibas, Latombe, LaValle, Lin, and Motwani, which is complete but makes no guarantees about the quality of the solution. Our algorithm employs a branch-and-bound forward search that considers pursuer trajectories that could potentially lead to an optimal pursuer strategy. The search is performed on an exponential graph that can generate an infinite number of unique pursuer trajectories, so we must conduct meticulous pruning during the search to quickly discard pursuer trajectories that are demonstrably suboptimal. We describe an implementation of the algorithm, along with experiments that measure its performance in several environments with a variety of pruning operations.

Efficient frontier detection for robot exploration

Frontier-based exploration is the most common approach to exploration, a fundamental problem in robotics. In frontier-based exploration, robots explore by repeatedly detecting (and moving towards) frontiers, the segments which separate the known regions from those unknown. A frontier detection sub-process examines map and/or sensor readings to identify frontiers for exploration. However, most frontier detection algorithms process the entire map data. This can be a time-consuming process, which affects the exploration decisions. In this work, we present several novel frontier detection algorithms that do not process the entire map data, and explore them in depth. We begin by investigating algorithms that represent two approaches: Wavefront Frontier Detector (WFD), a graph-search-based algorithm which examines only known areas, and Fast Frontier Detector (FFD), which examines only new laser readings data. We analytically examine the complexity of both algorithms, and discuss their correctness. We then improve by combining elements of both, to create two additional algorithms, called WFD-INC and WFD-IP . We empirically evaluate all algorithms, and show that they are all faster than a state-of-the-art frontier detector implementation (by several orders of magnitude). We additionally contrast them with each other and demonstrate the FFD and WFD-IP are faster than the others by one additional order of magnitude.

A Behavior-Based Strategy for Single and Multi-Robot Autonomous Exploration

In this paper, we consider the problem of autonomous exploration of unknown environments with single and multiple robots. This is a challenging task, with several potential applications. We propose a simple yet effective approach that combines a behavior-based navigation with an efficient data structure to store previously visited regions. This allows robots to safely navigate, disperse and efficiently explore the environment. A series of experiments performed using a realistic robotic simulator and a real testbed scenario demonstrate that our technique effectively distributes the robots over the environment and allows them to quickly accomplish their mission in large open spaces, narrow cluttered environments, dead-end corridors, as well as rooms with minimum exits.

Improving the Efficiency of Clearing with Multi-agent Teams

We present an anytime algorithm for coordinating multiple autonomous searchers to find a potentially adversarial target on a graphical representation of a physical environment. This problem is closely related to the mathematical problem of searching for an adversary on a graph. Prior methods in the literature treat multi-agent search as either a worst-case problem (i.e. clear an environment of an adversarial evader with potentially infinite speed), or an average-case problem (i.e. minimize average capture time given a model of the target’s motion). Both of these problems have been shown to be NP-hard, and optimal solutions typically scale exponentially in the number of searchers. We propose treating search as a resource allocation problem, which leads to a scalable anytime algorithm for generating schedules that clear the environment of a worst-case adversarial target and have good average-case performance considering a non-adversarial motion model. Our algorithm yields theoretically bounded average-case performance and allows for online and decentralized operation, making it applicable to real-world search tasks. We validate our proposed algorithm through a large number of experiments in simulation and with a team of robot and human searchers in an office building.

Efficient Multi-robot Search for a Moving Target

This paper examines the problem of locating a mobile, non-adversarial target in an indoor environment using multiple robotic searchers. One way to formulate this problem is to assume a known environment and choose searcher paths most likely to intersect with the path taken by the target. We refer to this as the multi-robot efficient search path planning (MESPP) problem. Such path planning prob lems are NP-hard, and optimal solutions typically scale exponentially in the number of searchers. We present an approximation al gorithm that utilizes finite-horizon planning and implicit coordination to achieve linear scalability in the number of searchers. We prove that solving the MESPP problem requires maximizing a non-decreasing, submodular objective function, which leads to theoretical bounds on the performance of our approximation algorithm. We extend our analysis by considering the scenario where searchers are given noisy non-line-of-sight ranging measurements to the target. For this scenario, we derive and integrate online Bayesian measurement updating into our framework. We demonstrate the performance of our framework in two large-scale simulated environments, and we further validate our results using data from a novel ultra-wideband ranging sensor. Finally, we provide an analysis that demonstrates the relationship between MESPP and the intuitive average capture time metric. Results show that our proposed linearly scalable approximation algorithm generates searcher paths that are competitive with those generated by exponential algorithms.