Supervisory Control for Turnover Prevention of a Teleoperated Mobile Agent with a Terrain-Prediction Sensor Module

Teleoperated mobile agents (or vehicles) play an important role especially in hazardous environments such as inspecting underwater structures (Lin, 1997), demining (Smith, 1992), and cleaning nuclear plants (Kim, 2002). A teleoperated agent is, in principle, maneuvered by an operator at a remote site, but should be able to react autonomously to avoid dangerous situations such as collisions with obstacles and turnovers. Many studies have been conducted on collision avoidance of mobile agents (Borenstein, 1989; Borenstein, 1991a; Borenstein, 1991b; Howard, 2001; Niwa, 2004; Singh et al., 2000). In this research, however, we will focus on turnover prevention of mobile agents moving on uneven terrain because a turnover can cause more fatal damage to the agents. Here, we adopt the term ‘turnover’ as a concept which includes not only a rollover but also a pitchover. Extensive studies have been conducted on motion planning problems of mobile agents traveling over sloped terrain in the robotics research community (Shiller, 1991). Shiller presented optimal motion planning for an autonomous car-like vehicle without a slip and a rollover. The terrain was represented by a B-spline patch and the vehicle path was represented by a B-spline curve, where the terrain and vehicle path were given in advance. With the models of the terrain and the path, the translational velocity limit of the vehicle was determined to avoid a slip and a rollover. Also, many studies have been conducted on rollover prevention of heavy vehicles like trucks and sports utility vehicles in the vehicular research community. Takano analyzed various dynamic outputs of large vehicles, such as the lateral acceleration, yaw rate, roll angle, and roll rate, in the frequency domain for predicting rollovers (Takano, 2001). Chen developed the time-torollover (TTR)-based rollover threat index in order to predict rollovers of sports utility vehicles (Chen, 1999). This intuitive measure TTR was computed from the simple model and then corrected by using an artificial neural network. Nalecz et al. suggested an energy-based function called the rollover prevention energy reserve (RPER) (Nalecz, 1987; Nalecz, 1991; Nalecz, 1993). RPER is the difference between the energy needed to bring the vehicle to its rollover position and the rotational kinetic energy, which can be transferred into the gravitational potential energy to lift the vehicle. RPER is positive for