Shingo Shimoda

Potential Field Navigation of High Speed Unmanned Ground Vehicles on Uneven Terrain

This paper proposes a potential field-based method for high speed navigation of unmanned ground vehicles (UGVs) on uneven terrain. A potential field is generated in the two-dimensional “trajectory space” of the UGV path curvature and longitudinal velocity. Dynamic constraints, terrain conditions, and navigation conditions can be expressed in this space. A maneuver is chosen within a set of performance bounds, based on the potential field gradient. In contrast to traditional potential field methods, the proposed method is subject to local maximum problems, rather than local minimum. It is shown that a simple randomization technique can be employed to address this problem. Simulation and experimental results show that the proposed method can successfully navigate a UGV between pre-defined waypoints at high speed, while avoiding unknown hazards. Further, vehicle velocity and curvature are controlled to avoid rollover and excessive side slip. The method is computationally efficient, and thus suitable for on-board real-time implementation.

Human-robot collaboration in precise positioning of a three-dimensional object

This paper deals with fundamental issues of human-robot cooperation in precise positioning of a flat object on a target. Based on the analysis of human-human interaction, two cooperation schemes are introduced. Several algorithms implementing these schemes are developed. A general theoretical framework for human/robot cooperation has been developed to represent these algorithms. The evaluation of the algorithms was carried out using our in-house made robot prototype and experiments by human subjects has demonstrated the effectiveness of our schemes. The main problem was the regulation of the robot-human interaction. Since the robot has no range sensors, it has to rely on the force and displacement information resulting from the interaction with human to understand human intention. The way the robot interprets these signal is crucial for smooth interaction. To be able to carry out a concrete task a simplification was made, in which robot and human do not directly hold the object but a frame to which the object and various sensors are attached.

Mobility of a microgravity rover using internal electro-magnetic levitation

A new type of mobility is discussed for space projects such as the MUSES-C aiming at small asteroid exploration. We propose the use of electro-magnetic levitation in order to integrate a mobility into the microgravity rover. The rover has a spherical shape and a smaller spherical shell inside. Four electromagnets are symmetrically located between the outer sphere surface and the inner sphere shell with one end of each directed to the center of the shell. With electromagnetic force of the magnets, a sphere iron ball inside the shell is controlled and levitated. When the rover lifts the ball inside with the electro-magnetic force, the rover is in return pressed down the ground by the reaction force, due to which the rover system not only gains upward momentum for floatation, but also obtains friction that enables its rolling on the ground. The prototype microgravity rover was developed and experimental results indicate effectiveness of the proposed mobility.

Biomimetic Approach to Tacit Learning Based on Compound Control

The remarkable capability of living organisms to adapt to unknown environments is due to learning mechanisms that are totally different from the current artificial machine-learning paradigm. Computational media composed of identical elements that have simple activity rules play a major role in biological control, such as the activities of neurons in brains and the molecular interactions in intracellular control. As a result of integrations of the individual activities of the computational media, new behavioral patterns emerge to adapt to changing environments. We previously implemented this feature of biological controls in a form of machine learning and succeeded to realize bipedal walking without the robot model or trajectory planning. Despite the success of bipedal walking, it was a puzzle as to why the individual activities of the computational media could achieve the global behavior. In this paper, we answer this question by taking a statistical approach that connects the individual activities of computational media to global network behaviors. We show that the individual activities can generate optimized behaviors from a particular global viewpoint, i.e., autonomous rhythm generation and learning of balanced postures, without using global performance indices.

High-speed navigation of unmanned ground vehicles on uneven terrain using potential fields

Many applications require unmanned ground vehicles (UGVs) to travel at high speeds on sloped, natural terrain. In this paper, a potential field-based method is proposed for UGV navigation in such scenarios. In the proposed approach, a potential field is generated in the two-dimensional “trajectory space” of the UGV path curvature and longitudinal velocity. In contrast to traditional potential field methods, dynamic constraints and the effect of changing terrain conditions can be easily expressed in the proposed framework. A maneuver is chosen within a set of performance bounds, based on the local potential field gradient. It is shown that the proposed method is subject to local maxima problems, rather than local minima. A simple randomization technique is proposed to address this problem. Simulation and experimental results show that the proposed method can successfully navigate a small UGV between predefined waypoints at speeds up to 7.0 m/s, while avoiding static hazards. Further, vehicle curvature and velocity are controlled during vehicle motion to avoid rollover and excessive side slip. The method is computationally efficient, and thus suitable for onboard real-time implementation.

Near-optimal navigation of high speed mobile robots on uneven terrain

This paper proposes a method for near-optimal navigation of high speed mobile robots on uneven terrain. The method relies on a layered control strategy. A high-level planning layer generates an optimal desired trajectory through uneven terrain. A low-level navigation layer guides a robot along the desired trajectory via a potential field-based control algorithm. The high-level planner is guaranteed to yield optimal trajectories but is computationally intensive. The low-level navigation layer is sub-optimal but computationally efficient. To guard against failures at the navigation layer, a model-based lookahead approach is employed that utilizes a reduced form of the optimal trajectory generation algorithm. Simulation results show that the proposed method can successfully navigate a mobile robot over uneven terrain while avoiding hazards. A comparison of the methodpsilas performance to a similar algorithm is also presented.

New mobility system based on elastic energy under microgravity

This paper proposes a new mobility system for planetary rover using springs and linear actuators. In the microgravity environment, hopping mobility is a possible choice for enhancing a new mobility of the robot. One method to hop is to press ones own to the ground. The proposed robot consists of two masses, and can press ones own to the ground using springs. So the rover can hop from the stationary state by transforming the elastic energy to the kinetic energy using the springs and linear actuators. Furthermore the robot can land without bounding by transforming the kinetic energy to the elastic energy. The simulation studies and the ground experiments shows that the proposed rover could hop and land.

Muscle synergy space: learning model to create an optimal muscle synergy.

Muscle redundancy allows the central nervous system (CNS) to choose a suitable combination of muscles from a number of options. This flexibility in muscle combinations allows for efficient behaviors to be generated in daily life. The computational mechanism of choosing muscle combinations, however, remains a long-standing challenge. One effective method of choosing muscle combinations is to create a set containing the muscle combinations of only efficient behaviors, and then to choose combinations from that set. The notion of muscle synergy, which was introduced to divide muscle activations into a lower-dimensional synergy space and time-dependent variables, is a suitable tool relevant to the discussion of this issue. The synergy space defines the suitable combinations of muscles, and time-dependent variables vary in lower-dimensional space to control behaviors. In this study, we investigated the mechanism the CNS may use to define the appropriate region and size of the synergy space when performing skilled behavior. Two indices were introduced in this study, one is the synergy stability index (SSI) that indicates the region of the synergy space, the other is the synergy coordination index (SCI) that indicates the size of the synergy space. The results on automatic posture response experiments show that SSI and SCI are positively correlated with the balance skill of the participants, and they are tunable by behavior training. These results suggest that the CNS has the ability to create optimal sets of efficient behaviors by optimizing the size of the synergy space at the appropriate region through interacting with the environment.

Adaptability of Tacit Learning in Bipedal Locomotion

The capability of adapting to unknown environmental situations is one of the most salient features of biological regulations. This capability is ascribed to the learning mechanisms of biological regulatory systems that are totally different from the current artificial machine-learning paradigm. We consider that all computations in biological regulatory systems result from the spatial and temporal integration of simple and homogeneous computational media such as the activities of neurons in brain and protein-protein interactions in intracellular regulations. Adaptation is the outcome of the local activities of the distributed computational media. To investigate the learning mechanism behind this computational scheme, we proposed a learning method that embodies the features of biological systems, termed tacit learning. In this paper, we elaborate this notion further and applied it to bipedal locomotion of a 36DOF humanoid robot in order to discuss the adaptation capability of tacit learning comparing with that of conventional control architectures and that of human beings. Experiments on walking revealed a remarkably high adaptation capability of tacit learning in terms of gait generation, power consumption and robustness.

Study on Mole-Typed Deep Driller Robot for Subsurface Exploration

This paper presents a mobile robotic system designed to perform deep soil sampling for lunar subsurface exploration in the near future. Drilling robots have to carry the excavated regolith backward because of its high density. Therefore a new scheme is proposed, to move forward under the soil by making use of reactive force caused by pushing the discharged regolith. Simple experiments demonstrate the effectiveness of the proposed method.