Smart wheelchairs give people with disabilities not only mobility but also the necessary help and support to handle daily living activities. The smart wheelchair combines a variety of research fields, such as machine vision [1], robot navigation and positioning [2], pattern recognition [3], multi-sensor fusion [4] and human-machine interface [5]. Especially in automatic navigation, accurate path planning results will greatly improve the performance of a smart wheelchair [6]. It is desirable to use reliable path planning methods to enhance awareness of the status of contemporary smart wheelchair technology, and ultimately increase the functional mobility and productivity of users. Intelligent optimization algorithms, which are simple, efficient and adaptive, have been introduced to solve path planning problems, especially in infrastructures and facilities for healthcare [7–۹]. Ant colony optimization is an intelligent search algorithm developed by Marco Dorigo’s doctoral thesis from a long-term observation of ant colony foraging behaviors [10]. Different from other path planning techniques, for instance, heuristic search or potential fields, it is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Hence ant colony algorithm has widely used in transportation, logistics and distribution, network analysis, pipeline and other fields in recent years [11,12]. At present, a large number of scholars are doing applied research on the ant colony algorithm. For instance, Xia et al. studied the issues of dynamic nature, instability andmulti QoS property restrictions of Web service in the process of services combinatorial  optimization, and proposed a multiple pheromone dynamically updated ant colony algorithm [13]. Sheng et al. proposed a credible service discovery method based on the improved ant colony algorithm for the service discovery problems in the unstructured P2P networks [14]. Luo et al. proposed an improved ant colony algorithm based on dynamic node planning for the problem of selection of optimal measuring points for analog circuit [15]. Shan et al. employed the ant colony algorithm to the smart wheelchair path planning method to solve the problems of local optimal in the path search process for the smart wheelchair [16]. Mohamed et al. proposed multi-division vehicle routing problems based on the hybrid ant colony algorithmby combining local searchandbasic ant colony algorithm [17]. Although the ant colony algorithm is widely used, and reflects good search features during the path optimization, it has shortcomings of likeliness to fall into local optimum and long search time, etc [18,19].