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The Pioneer All-Terrain Robot platform is used in the Rescue Robot league and Virtual Robot competition. The SICK laser range scanner on top and the sonar rings are clearly visible. The stereo camera on top of the real robot was not used in this research. On the right a simulated ComStation is given.
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This article investigates the effect of incorporating knowledge about the communication possibilities in an exploration algorithm used to map an unknown environment. The mission is to explore a hypothetical disaster site with a small team of robots. The challenge faced by the robot team is to coordinate their actions such that they efficiently expl...
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... the wireless communication. All information between the robots has to go via Wireless Communication Server, which performs a sanity check and drops messages and connection when they are not longer feasible. Also the information for the operator is directed via the Wireless Communication Server. Only the information that reaches a ComStation (see Fig. 1(c)) which is physically present in the environment may be used by the operator. Only the physical layer of communication is simulated, protocol and routing issues are ...
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Citations
... This constraint is especially important in the context of search and rescue applications, where valuable real-time information, such as image streaming, is pivotal [22][23][24]. Other approaches treat communication of acquired information to the base station periodically, in a discontinuous re-connection fashion, which can be implemented either synchronously [25] or asynchronously [26]. In the latter case, periodic connectivity may not be enforced as a hard constraint; for example, in the role-based distributed strategy [27], robots are free to explore the unknown environment with no communication restrictions. ...
In this work, we develop a reactive algorithm for autonomous exploration of indoor, unknown environments for multiple autonomous multi-rotor robots. The novelty of our approach rests on a two-level control architecture comprised of an Artificial-Harmonic Potential Field (AHPF) for navigation and a low-level tracking controller. Owing to the AHPF properties, the field is provably safe while guaranteeing workspace exploration. At the same time, the low-level controller ensures safe tracking of the field through velocity commands to the drone’s attitude controller, which handles the challenging non-linear dynamics. This architecture leads to a robust framework for autonomous exploration, which is extended to a multi-agent approach for collaborative navigation. The integration of approximate techniques for AHPF acquisition further improves the computational complexity of the proposed solution. The control scheme and the technical results are validated through high-fidelity simulations, where all aspects, from sensing and dynamics to control, are incorporated, demonstrating the capacity of our method in successfully tackling the multi-agent exploration task.
... Specifically, the expected information gain is the expected logarithmic ratio of the posterior probability density function (pdf) and the prior pdf of the unknown random parameters (Kullback and Leibler 1951). The expected information gain has been widely used as a measurement of information in engineering and sciences, for example, seismology (Long et al. 2015b), combustion (Bisetti et al. 2016), tomography (Long et al. 2013), biology (Dehideniya et al. 2019), nonlinear dynamical systems (Busetto et al. 2009;Huan and Marzouk 2013) and active learning (Visser and Slamet 2008). ...
One of the well-known challenges in optimal experimental design is how to efficiently estimate the nested integrations of the expected information gain. The Gaussian approximation and associated importance sampling have been shown to be effective at reducing the numerical costs. However, they may fail due to the non-negligible biases and the numerical instabilities. A new approach is developed to compute the expected information gain, when the posterior distribution is multimodal-a situation previously ignored by the methods aiming at accelerating the nested numerical integrations. Specifically, the posterior distribution is approximated using a mixture distribution constructed by multiple runs of global search for the modes and weighted local Laplace approximations. Under any given probability of capturing all the modes, we provide an estimation of the number of runs of searches, which is dimension independent. It is shown that the novel global-local multimodal approach can be significantly more accurate and more efficient than the other existing approaches, especially when the number of modes is large. The methods can be applied to the designs of experiments with both calibrated and uncalibrated observation noises.
... It is also worth noting, that the proposed criteria set is not exhaustive and can be extended by the robot operator by considering other factors that are important in specific SAR missions. The proposed criteria set is constructed from the two subsets-the first subset includes three criteria that were derived from the commonly applied candidate assessment and selection strategies [5,12,14,27,28]. Namely, the amount of new information that could be obtained after reaching the candidate frontier (expressed as the estimated length of a frontier), the cost of reaching the candidate frontier (expressed as the estimated time needed to reach the frontier), and the ability to transmit information from the candidate location to the robot control station (expressed as the Euclidean distance between the candidate and robot control station). ...
... By applying the CF strategy, the decision on where the robot should move next is based only on the estimated time that is needed to reach the candidate frontier. The SIG strategy is based on the multi-criteria decision-making approach and is derived from previously discussed candidate assessment strategies [5,12,14,27,28]. The criteria that define the SIG strategy along with their optimums and weights are presented in Table 3. ...
In this research, a novel adaptive frontier-assessment-based environment exploration strategy for search and rescue (SAR) robots is presented. Two neutrosophic WASPAS multi-criteria decision-making (MCDM) method extensions that provide the tools for addressing the inaccurate input data characteristics are applied to measure the utilities of the candidate frontiers. Namely, the WASPAS method built under the interval-valued neutrosophic set environment (WASPAS-IVNS) and the WASPAS method built under the m-generalised q-neutrosophic set environment (WASPAS-mGqNS). The indeterminacy component of the neutrosophic set can be considered as the axis of symmetry, and neutrosophic truth and falsity membership functions are asymmetric. As these three components of the neutrosophic set are independent, one can model the input data characteristics applied in the candidate frontier assessment process, while also taking into consideration uncertain or inaccurate input data obtained by the autonomous robot sensors. The performed experiments indicate that the proposed adaptive environment exploration strategy provides better results when compared to the baseline greedy environment exploration strategies.
... In this process and especially in the case of search and rescue missions, however, it is usually important to introduce a criterion related to the probability of establishing communication between the robot from the location r and the base station (P r ð Þ), so that the information can be forwarded as soon as possible to be further used. The introduction of this criterion was, for the first time, explicitly considered in paper [19] in the form of the following function (3): ...
An autonomous robot is often in a situation to perform tasks or missions in an initially unknown environment. A logical approach to doing this implies discovering the environment by the incremental principle defined by the applied exploration strategy. A large number of exploration strategies apply the technique of selecting the next robot position between candidate locations on the frontier between the unknown and the known parts of the environment using the function that combines different criteria. The exploration strategies based on Multi-Criteria Decision-Making (MCDM) using the standard SAW, COPRAS and TOPSIS methods are presented in the paper. Their performances are evaluated in terms of the analysis and comparison of the influence that each one of them has on the efficiency of exploration in environments with a different risk level of a "bad choice" in the selection of the next robot position. The simulation results show that, due to its characteristics related to the intention to minimize risk, the application of TOPSIS can provide a good exploration strategy in environments with a high level of considered risk. No significant difference is found in the application of the analyzed MCDM methods in the exploration of environments with a low level of considered risk. Also, the results confirm that MCDM-based exploration strategies achieve better results than strategies when only one criterion is used, regardless of the characteristics of the environment. The famous D* Lite algorithm is used for path planning.
... Specifically, the expected information gain is the expected logarithmic ratio of the posterior probability density function (pdf) and the prior pdf of the unknown random parameters [19]. The expected information gain has been widely used as a measurement of information in engineering and sciences, for example, seismology [22], combustion [7], tomography [23], biology [13], nonlinear dynamical systems [8] [17] and active learning [38]. ...
One of the well-known challenges in optimal experimental design is how to efficiently estimate the nested integrations of the expected information gain. The Gaussian approximation and associated importance sampling have been shown to be effective at reducing the numerical costs. However, they may fail due to the non-negligible biases and the numerical instabilities. A new approach is developed to compute the expected information gain, when the posterior distribution is multimodal - a situation previously ignored by the methods aiming at accelerating the nested numerical integrations. Specifically, the posterior distribution is approximated using a mixture distribution constructed by multiple runs of global search for the modes and weighted local Laplace approximations. Under any given probability of capturing all the modes, we provide an estimation of the number of runs of searches, which is dimension independent. It is shown that the novel global-local multimodal approach can be significantly more accurate and more efficient than the other existing approaches, especially when the number of modes is large. The methods can be applied to the designs of experiments with both calibrated and uncalibrated observation noises.
... Eventhough, multi robots teams with different cooperation strategies can rescue same amount of victims in the same period of time, they will consume different resources such as fuel or electrical power. [61][62][63][64][65] proposed an exploration strategy taking information gain and distance cost into account. [63] proposed a novel method controlling each robot based on a utility function that takes information gain and distance cost into consideration. ...
Humans can selectively focus on different information based on different tasks requirements, other people's abilities and availability. Therefore, they can adapt quickly to a completely different and complex environments. If, like people, robot could obtain the same abilities, then it would greatly increase their adaptability to new and unexpected situations. Recent efforts in Heterogeneous Multi Robots Teaming have try to achieve this ability, such as the methods based on communication and multi-modal information fusion strategies. However, these methods will not only suffer from the exponential explosion problem with the increase of robots number but also need huge computational resources. To that end, we introduce an inner attention actor-critic method that replicates aspects of human flexibly cooperation. By bringing attention mechanism on computer vision, natural language process into the realm of multi-robot cooperation, our attention method is able to dynamically select which robots to attend to. In order to test the effectiveness of our proposed method, several simulation experiments have been designed. And the results show that inner attention mechanism can enable flexible cooperation and lower resources consuming in rescuing tasks.
... Tuning a parameter, the mission planner is able to specify strategies ranging from a completely greedy exploration, with no returns to the control station, to an exploration ensuring the maximum update frequency. The reader is referred to [6,24,33,56] for other examples of strategies enforcing periodic connectivity constraints. ...
... Communication links in C t can always be pre-computed by means of a linkdetection mechanism which, given u, v ∈ V , determines whether (u, v) ∈ C t . Link-detection mechanisms can range from conservative line-of-sight criteria [32,51] to more sophisticated approaches where estimates of the signal strengths can be considered in building a connectivity graph [56]. Moreover, the presence of communication links could also be estimated as the exploration mission unfolds (Section 5 deals exactly with this matter). ...
... Typically, it is assumed that two robots are able to communicate if P > −93 dBm. The validity of this empirical model is corroborated by works in which Eq. (1) is used to estimate signal power in order to assign robots to locations where they can communicate with the base station, and to arrange rendezvous between robots [21,26]. The above systems consider communication maps built according to communication models (line of sight, circle, signal power) that basically depend on the distance between two locations. ...
... Since we assume that our multigraph represents a physical environment, we impose that the generalized triangle inequality holds, namely that the cost d(u, v) associated with a physical edge (u, v) ∈ E is not greater than that of any other (u, v)-path computed on E. The second set of edges, C, encodes the communication topology, namely, the availability of a communication link between a pair of locations. Communication links in C can be pre-computed by means of a link-detection mechanism which, given u, v ∈ V , determines whether (u, v) ∈ C. Linkdetection mechanisms can range from conservative visibilitybased criteria [16] to more sophisticated approaches where estimates of the signal strengths can be considered in building a connectivity graph [20]. For now, we make the assumption (as done in [14] and [16]) that C is static (not changing over time) and not affected by false positives (in Section VI we relax this assumption). ...
In several multirobot applications in which communication is limited, the mission could require the robots to iteratively take coordinated joint decisions on how to spread out in the environment and on how to reconnect with each other to share data and compute plans. Exploration and surveillance are examples of these applications. In this paper, we consider the problem of computing robots’ paths on a graph-represented environment for restoring connections at minimum traveling cost. We call it the multirobot reconnection problem, we show its NP-hardness and hardness of approximation on some important classes of graphs, and we provide optimal and heuristic algorithms to solve it in practical settings. The techniques we propose are then exploited to derive a new efficient planning algorithm for a relevant connectivity-constrained multirobot planning problem addressed in the literature, the multirobot informative path planning with periodic connectivity problem.
... In some cases, connections are established as the result of an emergent behavior of the algorithms. For example, Visser and Slamet (2008) include a criterion in the exploration strategy that takes the communication probability into account in order to favor locations in which it is high. de Hoog et al. (2009) propose the so-called Role-Based exploration, a distributed strategy in which robots are allowed to explore without considering communication limits. ...
During several applications, such as search and rescue, robots must discover new information about the environment and, at the same time, share operational knowledge with a base station through an ad hoc network. In this paper, we design exploration strategies that allow robots to coordinate with teammates to form such a network in order to satisfy recurrent connectivity constraints—that is, data must be shared with the base station when making new observations at the assigned locations. Current approaches lack in flexibility due to the assumptions made about the communication model. Furthermore, they are sometimes inefficient because of the synchronous way they work: new plans are issued only once all robots have reached their goals. This paper introduces two novel asynchronous strategies that work with arbitrary communication models. In this paper, ‘asynchronous’ means that it is possible to issue new plans to subgroups of robots, when they are ready to receive them. First, we propose a single-stage strategy based on Integer Linear Programming for selecting and assigning robots to locations. Second, we design a two-stage strategy to improve computational efficiency, by separating the problem of locations’ selection from that of robot-location assignments. Extensive testing both in simulation and with real robots show that the proposed strategies provide good situation awareness at the base station while efficiently exploring the environment.
