In this paper, we address the challenge of memory consumption in mobile robot navigation, particularly when using grid-based maps for accurate environment representation. While grid-based maps are widely used, their large memory requirements make them unsuitable for embedded systems. To overcome this limitation, we propose a novel path planning algorithm that rapidly generates secure paths using a hybrid map. This hybrid map significantly reduces memory usage compared to grid-based maps while maintaining the efficiency of a topological map. Experimental results demonstrate that the proposed method requires only 1.5% of the computation time needed for grid-based path planning, ensuring faster and more efficient navigation. Furthermore, the approach provides robust and secure paths, making it ideal for mobile robot applications. This method holds promise for advancing autonomous navigation in resource-constrained environments.

When cyber–physical systems (CPSs) are connected to the Internet or other CPSs with connectivity, external adversaries can potentially gain access to the CPS and attempt to control the electronic control units (ECUs). In particular, the lack of confidentiality and integrity in the controller area networks (CANs) of CPSs makes it difficult to distinguish malicious data from legitimate data. The security vulnerabilities of CPSs, which are frequently exposed to adversaries, pose the risk of destabilizing the lives of humans. Therefore, we propose a real-time adaptive and lightweight anomaly detection (RALAD) mechanism that efficiently and securely detects anomalies within a given virtual group though verification of the data integrity and key management of stateless synchronization based on a chaotic system while driving. These characteristics prevent an adversary from authenticating maliciously modified messages even though it captures legitimate messages on the CAN bus. RALAD shows a clear difference from others in terms of (1) its unique secret key-sharing method and approach to secret key generation for each message, (2) safe controlling support after detecting anomalies, and (3) its software-based solution that eliminates the need for hardware secure modules. It leads to freedom from the issues of additional cost, weight, and wiring in CPSs. The proposed method achieves real-time anomaly detection, and the experiment results show a 100% detection rate for all attacks. This demonstrates that RALAD maintains high reliability and efficiency, even under various bus load conditions and attack rates.

The grid map, representing area information with the number of cells, is a widely used mapping scheme for mobile robots and simultaneous localization and mapping (SLAM) processes. However, the tremendous amount of cells in a grid map for a detailed map representation results in overheads for memory space and computing paths in mobile robots. Therefore, to overcome the overhead of the grid map, this study proposes a new low overhead mapping scheme which the authors call as the Rmap that represents an area with variable sizes of rectangles instead of the number of cells in the grid map. This mapping scheme also provides an exploration path for obtaining new information for the unknown area. This study evaluated the performance of the Rmap in real environments as well as in simulation environments. The experiment results show that the Rmap can reduce the overhead of a grid map. In one of our experimental environments, the Rmap represented an area with 85% less memory than the grid map. The Rmap also showed better coverage performance compared with other previous algorithms.