Mobile robot path planning: a multicriteria approach Juan A. Fernandez a , J. Gonzalez a , L. Mandow b, *, J.L. PeÂrez-de-la-Cruz b a Dpto. IngenierõÂa de Sistemas y Automa Âtica, Universidad de Ma Âlaga/Campus Teatinos, P.O.B. 4114, 29080, Ma Âlaga, Spain b Dpto. Lenguajes y Ciencias de la Computacio Ân, Universidad de Ma Âlaga/Campus Teatinos, P.O.B. 4114, 29080, Ma Âlaga, Spain Received 1 July 1997; accepted 1 February 1999 Abstract This paper addresses the problem of searching paths in a graph-based model of the environment for mobile robot navigation. Unlike conventional approaches, where just a scalar cost (or a scalar function combining several costs) is to be optimized, this paper proposes a multicriteria path planner that provides an ecient and natural way of both de®ning and solving problems in which con¯icting criteria are involved. In particular, the multicriteria METAL-A  algorithm is used as the core of a mobile robot global path planner. This algorithm has been implemented and tested in the RAM-2 mobile robot for indoor navigation. The results presented demonstrate the performance of the algorithm when dealing with energy-consumption, temporal, and clearance restrictions on the paths. # 1999 Elsevier Science Ltd. All rights reserved. Keywords: Path planning; Mobile robots; Multicriteria decision theory; Goal satisfaction 1. Introduction To achieve an autonomous navigation capability, a mobile robot must be able to plan a suitable path between a start and a destination in the environment. In large-scale space, i.e. environments where spatial structures are on a signi®cantly larger scale than the sensory horizon of the observer (i.e. buildings) (Kuipers et al., 1993), this process involves two dier- ent problems: ®rst, to provide a global path in terms of intermediate subgoals and, second, to travel between consecutive subgoals while avoiding unex- pected and moving obstacles along the way. The latter problem has received great attention in the literature, with important contributions ranging from completely reactive techniques (Khatib, 1986; Borenstein and Koren, 1991) to planned trajectories that take into consideration dierent constraints: non-holonomic kin- ematics (Canny, 1988; Lozano-PeÂrez, 1983), dynamics (Latombe, 1991), time (Hu et al., 1993), etc. On the other hand, the problem of global path gener- ation is tightly related to that of decision-making. Provided that a graph-based model of the environment is available, this problem is usually addressed on the basis of some kind of optimal graph search by using a certain cost function (Hart et al., 1968; Dijkstra, 1959). Usually, the cost function depends on a unique cost variable, e.g. the distance travelled or the time spent. More sophisticated planners combine these with other factors; e.g. energy consumption, safety, or the robust- ness of the path indicated by its clearance (Hu and Brady, 1997; Stentz and Hebert, 1995), the probability of encountering moving obstacles (Fujimura, 1995; Simmons et al., 1997), etc. These approaches suer from the following limitations: . The set of factors or variables to be considered for the cost function are of such diering natures that, in practice, they cannot be combined in an easy and intuitive way. Typically, the function is built up as a weighted linear combination of them. The weights are chosen on an ad-hoc basis, and cannot be applied to a wide range of situations. . In many missions it is not important for the mobile robot to optimize the value of the cost function, but to satisfy some restrictions on the cost variables, for Engineering Applications of Arti®cial Intelligence 12 (1999) 543±554 0952-1976/99/$ - see front matter # 1999 Elsevier Science Ltd. All rights reserved. PII: S0952-1976(99)00018-4 www.elsevier.com/locate/engappai * Corresponding author. Fax: +34-5-213-13-97. E-mail address: lawrence@lcc.uma.es (L. Mandow)