Abstract
Micro aerial vehicles (MAVs) have been acknowledged as an influential technology for indoor search and rescue operations. The time constraint is a crucial factor in most search and rescue operations. The employed MAVs in indoor environments are characterized by short endurance flight time and limited payload weights. Hence, adding more batteries to extend the flight time is practically not feasible. Typically, most of the indoor missions’ environments might not be accessed and remain unknown. Working in such environments requires effective exploration and information gathering to save time and maximize the coverage area. Furthermore, due to the dynamism of such environments, choosing the least risky trajectory is an important task. This paper proposes a real-time active exploration technique which is capable of efficiently generating paths that minimize the vehicle’s risk and maximize the coverage area. Furthermore, it accomplishes real-time monitoring of sudden changes in the estimated map, due to the dynamic objects, by reevaluating at real-time the destination and trajectory to minimize the risk on the chosen path and simultaneously preserving the maximization of the coverage area. Ultimately, recording the implemented trajectory of the vehicle also assists in time-saving as the vehicle depends on this trajectory during the exit process. The performance of the technique is studied under static and dynamic environments and is also compared with different algorithms.
Highlights
Exploration of unknown environments using robots, such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs), is a problem of acquiring local knowledge to proceed along investigating and discovering novel places in the exploring environment [1,2]
Many algorithms and methods, such as Dijkstra [10,11], A-star (A*) [12,13], artificial potential field (APF) [14], rapidly-exploring random tree (RRT) [15,16], and bidirectional RRT [17,18], have been developed for path planning problem which focuses on finding the optimum route to the destination
To evaluate the efficiency of the proposed path generation method, the proposed method is compared with A*, APF, RRT, and bidirectional RRT using simulated and real datasets in static and dynamic environments
Summary
Exploration of unknown environments using robots, such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs), is a problem of acquiring local knowledge to proceed along investigating and discovering novel places in the exploring environment [1,2]. Many algorithms and methods, such as Dijkstra [10,11], A-star (A*) [12,13], artificial potential field (APF) [14], rapidly-exploring random tree (RRT) [15,16], and bidirectional RRT [17,18], have been developed for path planning problem which focuses on finding the optimum route to the destination Such techniques, do not consider efficiency regarding the exploration of the area, such as maximizing the visited area. Ju. Gt esoi-gInnf.a2l018, 7, x FOR PEER REVIEW n: number of points in the average i: inSde:xthoefitnhpeustisginganlal n: number of points in the average i: Oinndceex othf ethAe *siaglngaol rithm generates a path, the algorithm is turned to be idle until interruption takes plOacnec.eTthheisAin*tealrgrourpitthiomn goecnceurrasteesitaheprawthh, ethnethalegovreihthicmleisretaucrhneesdtthoebceuirdrelentudnteisl tiinntaetrirounp,tioornthe gentearkaetsedplpacaet.hTihs iasbirnuteprtrluypcthioanngoecdcu, rfsoreiitnhsetranwcheednytnhaemviechoicblejercetabclhoecsktshtehecugrernenetradteesdtipnaattiho.nW, ohretnheever thegMenAeVrapteednpeatrtahtiessatbhreupcitrlcylcehoafnagcecde,pfotarnincestsauncrreoduynndaimngictohbejedcet sbtlioncaktsiothnepgoeinnetr,attheedaplagtohr.iWthhmensetavretrs to gentehreaMteAaVnepwenpeatrtahtetsotaheneciwrcldeeosftiancacteiopnta.nce surrounding the destination point, the algorithm starts toAgfetneerrfiatneisahninewg tphaethretqouairneedwmdeissstinoant,iothne. Gaussian kernels of the implemented trajectory, which are usedAtfotermfainxiismhiinzgetthheerveqisuitireeddamreisasidounr,itnhge Gthaeuessxipanlokraertinoenls, oafrethuetimlizpeldemtoenctreedatreaajecretoturyr,nwphaicth by invaerretinugsetdhteoirmcoaxstimviazleuethsetoviastitreadcatrtehaedvuerhinicglethtoe uexspeliotrdatuiorinn,garteheuteilxiiztepdrtoocecrsesa. te a return path by inverting their cost values to attract the vehicle to use it during the exit process
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