The synergy between the robot's abilities and human expertise provides several advantages in the industrial processes. A proper integration of Task and Motion Planning (TAMP), considering the interaction of the user with the environment is essential to maximize the benefits of robot-assisted tasks and to ensure safety in collaborative H-R tasks. However, the state of the art of the TAMP research ifeld should be improved to overcome the limitations of the traditional TAMP approaches to be used in industrial scenarios. We illustrate a list of challenges to be solved in order to improve to manage eficiently the dynamism and uncertainty given by introducing a human operator into a robotized process of draping fiber carbon plies, with reference to the granted European Project DrapeBot. Finally, the proposed TAMP architecture is presented to address the challenges described.
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Human-Robot Collaboration (HRC) has emerged as a significant technological challenge in the
industrial landscape in recent years. By combining the precision, eficiency, and repeatability
of robots with the intelligence, adaptability and expertise of humans, numerous advantages
emerge. Such collaboration reduces operator fatigue, improves ergonomic conditions, and
enhances production quality [
A common technique in TAMP is interleaving the symbolic and geometric search processes
by calling a motion planner at each step and assigning geometric parameters to the currently
symbolic state before proceeding. The interleaving becomes problematic when a planned state is
LGOBE
(E. Menegatti)
valid in symbolic space, but geometrically infeasible. To address this, FFRob [
Since an industrial environment shared by humans and robots is a highly dynamic scenario,
many manufacturing industries have not yet introduced automation in their processes. The
unpredictability of human presence must be integrated into TAMP and robot control, leveraging
the latest advances in perception and interactions. Thus, this paper ofers an enhanced approach
that could solve the challenges outlined below for manufacturing processes of an industrial HRC
scenario. The following list of challenges was initially identified from the author’s experience
in the industrial environment and previous works [
CHL1 To integrate the knowledge of humans’ and robots’ capabilities CHL2 To compute feasible action sequences and share them among humans and robots CHL3 To satisfy ergonomic constraints by synchronizing robot and human movements CHL4 To ensure human safety during robot motion CHL5 To continuously monitor the scene by adapting on-line the robot to changes
These five challenges can be grouped into three macro categories: collaboration (CHL1-3), safety (CHL4) and monitoring (CHL5). Firstly, CHL1 calls for the seamless integration of human and robot capabilities, emphasizing the need to harness their expertise efectively. CHL2 extends this by highlighting the intricate task of computing a viable action sequence and facilitating its sharing between human and robot counterparts, which is pivotal for synchronized and eficient collaboration. CHL3 complements these by emphasizing the ergonomic alignment of robot movements with human actions, particularly vital in cooperative endeavours involving physical interaction. Moving on to safety, CHL4 addresses the paramount issue of ensuring human safety throughout robot motion, necessitating robust safety mechanisms and real-time risk assessment. Last, the theme of monitoring comes to the fore with CHL5, stressing the robot’s continuous scene observation and adaptive response capabilities to address environmental changes proactively.
We have tackled the above challenges within the EU project DrapeBot (https://www.drapebot.
eu/), aiming to develop an HRC system that aids operators in carbon fiber draping. In particular,
it focuses on the dynamic scheduling of shared human-robot activities within a manufacturing
environment where humans and robots collaborate to complete complex tasks like draping [
In HRC industrial applications, the TAMP framework is pivotal. It must ensure flexibility in managing work plans, efectively address operator interventions, handle inputs from external sensors (e.g., perception systems and laser scanners), and safeguard operator safety. Furthermore, it should maintain production quality by automating inspection processes for quality control. The use of a TAMP indeed allows the industrial process to take a step forward, bringing an increase in productivity and more eficient use of resources and operator skills by removing the operator from more stressful and repetitive tasks (e.g. transporting patches, in-depth inspection during the process). Therefore, one specific industrial scenario where a human-aware TAMP solution can play a critical role is inside the carbon fiber draping process. Draping involves transporting the carbon ply onto the mould and shaping it to fit. Another vital process is visual inspection to ensure product quality. This process is predominantly manual, carried out by skilled human operators whose expertise is essential for the final product’s high quality. Employing TAMP lets the operator be more free to focus on the draping activity, which is the one that requires the highest skill to obtain the best final quality of the product. Thus, being draping an excellent case for attempting to address the illustrated challenges of a dynamic human-aware industrial scenario, we are developing a TAMP approach within the DrapeBot project according to the approach outlined by (Fig. 2). Our proposed framework comprises three main modules: Task Planner, Motion Planner and Central Node.
Task Planner. The Task Planner [
A three-tier hierarchical design enhances modularity and adaptability: • Primitive actions: Fundamental operations (e.g., Move, Draping, Inspection, etc.). • Composite actions: Sequences of primitives for complex actions like patch transportation, involving robot movement, carbon fiber detection, etc. • Final Plan: Provides a high-level view of the draping process by laying out the entire sequence of tasks for both human and robot agents.
Motion Planner. The Motion Planner computes collision-free trajectory for robots. To
ensure operator safety (CHL4), Safe Zones are introduced to restrict robot entry, assuring
operator freedom. Motion planning algorithm considers these zones to compute non-collaborative
motions (Fig. 1). The collaborative trajectory is the most challenging to compute. The
planner must consider human limitations, emphasizing ergonomic constraints based on operator
stature (CHL3). Figure 3 displays the window for the cost function of human operations while
cooperating with the robot. A skeletal tracking system [
Central Node. In the classical approaches, Task and Motion planner modules communicate directly to exchange information. However, due to the large amount of data to be handled from the industrial scene and process, our framework provides a Central Node to integrate and optimize task and motion planner modules. The plan is carried out by the Central Node, which also controls and supervises the proper primitives’ activity. Additionally, this module constantly monitors the condition of the workcell using the sensors in the environment (CHL5). The Central Node also handles human gestures that trigger action not foreseen in the plan (CHL1). In that case, it sends the information to the Task Planner module which is in charge of creating a new plan where the requested action is the first action to be performed.
To improve the management of a dynamic and uncertain scenario by introducing a human cooperating with a robot in an industrial process, we consider the limits of the current state of the art of TAMP approaches. We list a series of challenges that should be addressed, like integrating human skills with robot flexibility, the ergonomic constraints due to the operator’s physical body, the workers’ safety issues, and the processes’ monitoring procedures. These challenges are illustrated with respect to a real carbon fiber draping industrial process. We abstract three diferent levels: a Task planner computing the process evolution taking into account the robot and human capabilities; a Motion Planner, computing the collision-free trajectories, that uses the Safe Zones for human safety; a Central Node monitoring the actions of the operator in the environment.
This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 101006732 (DrapeBot).