Fault-Tolerant and Terrain Adaptive Locomotion for a Four-Wheeled Planetary Exploration Rover with Active Suspension

This thesis presents the development and validation of a compact, resource-constrained planetary rover designed for lunar surface exploration and infrastructure construction. It focuses on achieving fault-tolerant, terrain-adaptive, and resilient locomotion using minimal hardware and intelligent actuator feedback processing. The proposed system integrates active suspension, one-way clutch mechanisms, real-time terrain estimation using resistive force theory (RFT), and realtime anomaly detection. Through a combination of experimental validation, simulation, and analytical modeling, the research provides a comprehensive framework for enabling autonomous, robust mobility under the challenging conditions of planetary terrains. The Moon’s surface poses unique challenges to robotic mobility due to its loosely compacted regolith, harsh terrain features, and the absence of gravity. Traditional rover designs typically rely on passive suspensions and hardware redundancy to manage faults and irregular surfaces. However, such systems become impractical when strict constraints on mass, power, and volume are imposed, such as in infrastructure construction missions or pathfinder scouting operations. This work addresses this gap by proposing a novel rover system developed under the Japanese Moonshot Research and Development Program, Goal 3, which emphasizes open design principles, low resource utilization, and the use of integrated sensor feedback. The rover platform developed in this study features a four-legged configuration, with one wheel at the end of each leg, resulting in a cross-shaped layout. Each wheel is actuated independently with three degrees of freedom: drive, steering, and suspension, for a total of 12 actuators. The modularity and symmetry of this system enable differential active suspension control and advanced locomotion modes. Each actuator is a HEBI X8-16 with built-in encoders and IMUs, which provide feedback on torque, velocity, current, position, and other parameters. The use of integrated feedback eliminates the need for external sensors, reducing system complexity and power consumption. The first contribution of this thesis is the implementation of a fault-tolerant framework that combines passive mechanical resilience with active anomaly detection and reconfiguration. To address wheel actuator failure scenarios, a oneway clutch mechanism was integrated into each wheel. This passive system permits free forward rotation while restricting backward motion. In the event of actuator failure, the affected wheel transitions to a free rolling mode, conserving energy and preserving operational capability. This mechanism, while unidirectional, introduces no additional power demand, making it ideal for long-duration missions. On the software side, fault detection is realized through an autoencoder-based anomaly detection system, trained using real actuator feedback data under both nominal and faulty conditions. The system monitors velocity, current, torque, and posture signals from all wheels and dynamically identifies anomalies in real time. A dynamic thresholding approach improves detection reliability by adapting to mission phases and terrain conditions. Experimental results show that the system can detect both terrain-induced and internal faults with high sensitivity, making it suitable for autonomous anomaly detection on future lunar rovers. The second core contribution is the development of an in-situ terrain property estimation method using resistive force theory (RFT). RFT divides the contact surface of a wheel into elements and estimates the total resistive force by summing contributions from each component. By analyzing actuator feedback—torque, velocity, slip ratio—during controlled experiments, terrain parameters are derived in situ. The derived parameters enable real-time estimation of soil data, eliminating the need for vision or external instruments. This is especially advantageous in lunar settings where dust and lighting variations compromise optical sensors. To validate this method, experiments were conducted using lunar regolith simulants. The feedback data were compared against RFT-based model outputs, showing a good correlation. This terrain estimation framework supports environment-aware decision-making, improving both mobility performance and safety margins during exploration tasks. The third significant contribution lies in the development of adaptive locomotion strategies based on terrain feedback and mechanical configuration. A series of inching locomotion techniques were devised to handle high-sinkage or lowtraction environments. Unlike traditional forward-only inching, the proposed system supports omnidirectional inching by coordinating steering angles and suspension postures. This enables lateral movement and extraction maneuvers without requiring full-body rotation. Experiments demonstrated that the rover could maintain mobility even with partial actuator failures, and the theoretical displacement per cycle closely matched experimental values, indicating robust kinematic predictability. The rover’s active suspension system supports three height configurations—low, nominal, and high—which were leveraged to optimize ground clearance, stability, and traction. Differential active suspension control was evaluated by commanding asymmetric suspension postures and comparing mobility performance over uneven terrain. Results demonstrated that the rover could redistribute loads and dynamically adapt its body posture, reducing slip and enhancing obstacle negotiation. This capability, when integrated with the terrain estimation framework, provides a good technique for resilient mobility. Collectively, the experimental campaigns across Chapters 2 to 4 confirm that the rover can autonomously detect faults, adapt its configuration to terrain properties, and execute resilient locomotion strategies even in failure cases. The use of minimal hardware—limited to actuators and standard computing modules such as the Jetson Orin Nano—ensures feasibility for actual space deployment where power and payload budgets are tight. The software architecture, built on ROS2, allows modular integration, real-time visualization, and teleoperation, while supporting future extensions to machine learning-based decision frameworks. While the thesis makes significant strides in rover design and autonomy, some limitations remain. All experiments were conducted under Earth gravity using lunar regolith simulants. Although the simulants emulate particle size and cohesion, gravitational effects on sinkage and traction may vary in the actual lunar environment. Furthermore, the autoencoder anomaly detection system currently requires pre-training on representative data, which may limit adaptability to entirely new terrains or failure modes. Future work should explore deployment in better lunar analog environments or participate in field campaigns. Integrating onboard machine learning, realtime terrain classification, and cooperative behavior among multiple rovers could also enhance exploration efficiency. Lightweight AI strategies that balance performance with embedded system constraints are a promising avenue to pursue. The mechanical configuration can be further optimized for navigating complex terrain. In summary, this thesis presents a unified framework for fault-tolerant, terrainaware, and adaptive mobility in planetary rovers. Through a combination of mechanical simplicity, intelligent use of actuator feedback, and modeling, it demonstrates that high performance and resilience can be achieved even with limited resources. The outcomes contribute to the broader goal of enabling sustainable, autonomous, and low-cost robotic operations on the Moon, laying foundational principles for future lunar infrastructure development and Mars preparation missions.