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This study proposes an autonomous navigation system for hexapod robots, promising in complex rescue scenarios. The system is tested in simulations under three environments: rocky, cracked flooring, and inclined surfaces. utilizing light detection and ranging (LiDAR) and simultaneous localization and mapping (SLAM), the robot recognizes positions and constructs environmental maps. Implemented via robot operating system, the research successfully applies navigation and mapping using hectorₛlam. LiDAR mapping yields satisfactory accuracy, with average errors of 0. 21% for general mapping and 5. 34% for circular paths. Within a 2-meter range, navigation achieves good accuracy, averaging 1. 2% error on the x-axis and 0. 011% on the y-axis during linear motion. Navigational repeatability improves, with reliable results showing an average error of 4. 33 cm on the x-axis and 0. 5 cm on the y-axis when returning to starting points. Arena testing with varied obstacles demonstrates successful obstacle traversal. However, in the second test, limitations in hardware, notably the Raspberry Pi 4 CPU usage reaching 97% during navigation, hindered reaching the third target.
Budiyarto et al. (Sat,) studied this question.