A route planning framework for smart wearable assistive navigation systems

Abstract

Route planning (RP) enables individuals to navigate in unfamiliar environments. Current RP methodologies generate routes that optimize criteria relevant to the traveling distance or time, whereas most of them do not consider personal preferences or needs. Also, most of the current smart wearable assistive navigation systems offer limited support to individuals with disabilities by providing obstacle avoidance instructions, but often neglecting their special requirements with respect to the route quality. Motivated by the mobility needs of such individuals, this study proposes a novel RP framework for assistive navigation that copes these open issues. The framework is based on a novel mixed 0–1 integer nonlinear programming model for solving the RP problem with constraints originating from the needs of individuals with disabilities; unlike previous models, it minimizes: (1) the collision risk with obstacles within a path by prioritizing the safer paths; (2) the walking time; (3) the number of turns by constructing smooth paths, and (4) the loss of cultural interest by penalizing multiple crossovers of the same paths, while satisfying user preferences, such as points of interest to visit and a desired tour duration. The proposed framework is applied for the development of a system module for safe navigation of visually impaired individuals (VIIs) in outdoor cultural spaces. The module is evaluated in a variety of navigation scenarios with different parameters. The results demonstrate the comparative advantage of our RP model over relevant state-of-the-art models, by generating safer and more convenient routes for the VIIs.

Emotion-Aware Brainstorm Optimization

“…To this end, future work includes the test of EBO in larger scale optimization problems, such as in 50 -100 -1000 dimensions, and the comparison of the proposed algorithm with other effective state-of-the-art algorithms. Also, the discretization of the algorithm for solving discrete optimization problems, such as integer programming problems [41], will be investigated with emphasis on multiobjective transportation problems [68][69][70], combined even with fuzzy logic [71][72][73].…”

Path Planning in Case of Swarm Unmanned Surface Vehicles for Visiting Multiple Targets

“…Robotic vehicles are integrated into modern style of life to undertake challenging tasks, such as monitoring or navigation assistance [1]. Unmanned Surface Vehicle (USV) is a type of autonomous robotic vehicle with various application including ocean monitoring [2,3], safety and rescuing [4] and swarm approaches combined with UAVs and/or UGVs for monitoring.…”

“…Path planning problems can be found in various domains, such as Air Transportation and UAVs [6][7][8][9], robotic vehicles and USV [5] and even for smart assistive systems for individuals with disabilities [1,10]. To address the USV path planning problem in complex and dynamic environments, multiple factors/objectives should be considered for generating an optimal path.…”

A Swarm Intelligence Graph-Based Pathfinding Algorithm Based on Fuzzy Logic (SIGPAF): A Case Study on Unmanned Surface Vehicle Multi-Objective Path Planning

“…However, trends in robotic motion and computer vision allow autonomous robotic vehicles to move in complex and dynamic environments. To this end, new approaches have been developed to cope with multi-objective path planning problems where an equilibrium among conflicting objectives/criteria should be found [10]. Conventional approaches for addressing multi-objective (MO) path planning problems include the weighted sum method (WSM) or Pareto optimality.…”