Assistive devices, such as exoskeletons and prostheses, have revolutionized the field of rehabilitation and mobility assistance. Efficiently detecting transitions between different activities, such as walking, stair ascending and descending, and sitting, is crucial for ensuring adaptive control and enhancing user experience. We present an approach for real-time transition detection, aimed at optimizing the processing-time performance. By establishing activity-specific threshold values through trained machine learning models, we effectively distinguish motion patterns and we identify transition moments between locomotion modes. This threshold-based method improves real-time embedded processing time performance by up to 11 times compared to machine learning approaches. The efficacy of the developed finite-state machine is validated using data collected from three different measurement systems. Moreover, experiments with healthy participants were conducted on an active pelvis orthosis to validate the robustness and reliability of our approach. The proposed algorithm achieved high accuracy in detecting transitions between activities. These promising results show the robustness and reliability of the method, reinforcing its potential for integration into practical applications.

Efficient and safe multi-agent swarm coordination in environments where humans operate, such as warehouses, assistive living rooms, or automated hospitals, is crucial for adopting automation. In this paper, we augment the obstacle avoidance algorithm based on dynamical system modulation for a swarm of heterogeneous holonomic mobile agents. A smooth prioritization is proposed to change the reactivity of the swarm towards the specific agents. Further, a soft decoupling of the initial agent's kinematics is used to design an independent rotation control to ensure the agent reaches the desired position and orientation simultaneously. This decoupling allowed the introduction of a novel heuristic, the virtual drag. It minimizes the disturbance influence an agent has when moving through its surrounding. Additionally, the safety module adapts the velocity commands from the dynamical system modulation to avoid colliding trajectories between agents. The evaluation was performed in simulated assisted living and hospital environments. The prioritization successfully increased the minimum distance relative to a moving agent. The safety module is observed to create collision-free dynamics where alternative methods fail. Additionally, the repulsive nature of the safety module augments the convergence rate, thus making the proposed method better applicable to dense real-world scenarios.

Collaborative object manipulation and transport with self-reconfigurable modular robots can take a major role in improving modularity and adaptability of smart-home and factory-like environments. Controlling modules to achieve efficient behaviours is challenging due to the high number of degrees of freedom in the system and the physical constraints. We present an end-to-end planner that discovers collaborative behaviours for modules to manipulate and transport objects to bring them to a human defined place. Our approach is based on a centralized planner using stochastic best-first search with a custom heuristic and pruning strategy. We use Quadratic Programming to define multi-robot controller to evaluate action feasibility for transitions between the search tree nodes with respect to important constraints of the system (collisions, joint and torque limits). The controller can be design to be aware of human reachable space for object handover and use it as a measure to asses closeness to the goal node. Results show that the proposed method can effectively coordinate the actions of multiple robots, leading to an emerging efficient manipulation and transport of objects with variable shapes and weight to within human reachable space. This work brings self-reconfigurable modular robots one step closer to assistive human-robot interaction applications or smart logistics.

In order to facilitate and assist the indoor mobility of people with special needs, the classically static objects in the environment, such as furniture, can be rendered mobile. The need for efficient and safe autonomous coordination of a mobile furniture swarm arises. We present a closed-form approach for mobile furniture obstacle avoidance and navigation within an indoor environment. The approach shows that each mobile furniture agent, defined by a polygonal surface, does not collide with any static or mobile obstacle (e.g., a person is moving around). All controllable mobile furniture converges towards a defined goal position and orientation. We showcase the application of this algorithm in simulation on mobile furniture for smart environments. Results demonstrate that the proposed method can coordinate a swarm of mobile furniture to get out of the way of a mobile agent representing a person with limited mobility passing through the room while avoiding obstacles and converging towards a predefined target pose.

Modular robots (MRs) consist of unique robots which interconnect and work as a collective to perform objectives. Coordinating these robots rely on robust communication, as modules moving independently can lead to damaging behaviour. We present a robust structure for modular robot communication, implemented and tested on a new MR. The structure has different communication protocols depending on the importance and bandwidth of the exchanged information, has fast error responses, and considerations which allow for two modules to actuate the same joint. We evaluate the wireless protocols, novel error response, and coordinated actuation empirically, validating the system on a new modular robot, the Mori3. We find two wireless protocols can be used to balance speed and reliability; transmitting errors through both wireless and serial is more consistent and faster; and sharing motor targets, control variables, and measurements allow for motors to operate a shared joint with equal efforts.

We study the use of humanoid robot technology for physical assistance in motion for a frail person. A careful design of a whole-body controller for a humanoid robot needs to be developed in order to ensure efficient, intuitive and secure interaction between humanoid-assistant and human-patient. Here, we present a design and implementation of a whole-body controller that enables a humanoid robot with a mobile base to autonomously reach a person, perform audiovisual communication of intent, and establish several physical contacts for initiating physical assistance. Our controller uses (i) visual human perception as a feedback for navigation and (ii) joint residual signal based contact detection for closed-loop physical contact creation. We assess the developed controller on a healthy subject and report on the experiments achieved and the results.

In order to enable efficient control of a human-humanoid in physical contact settings, a real-time solution for a contact observer is required. We propose a novel approach for proprioceptive sensor based contact sensing suitable for affordable personal robots with no force/torque or electric current sensing. We combine robot model knowledge and the output of acceleration resolved quadratic programming whole-body controller to make a prediction of expected position tracking error for computing our proposed contact observer signal. We demonstrate the efficiency of our approach in the experiments of contact detection and estimation of collision direction and intensity on a real humanoid robot Pepper platform controlled by a task-space multi-objective quadratic programming controller.