a b s t r a c tThis paper presents a new line of project based learning in the School of Engineering of University of Minho: the Innovation and Entrepreneurship Integrated Project (IEIP). Four groups, each one composed of students from different engineering integrated master courses -Mechanical, Industrial Electronics and Computers, Polymer, Industrial Management -compete against each other in developing or improving commercial products manufactured by actual industries. There have been so far five editions of the IEIP, with five different companies and five diverse products, however, all these products included components that required knowledge from all the engineering courses involved. Only with the cooperation between the students of the various courses that compose each multidisciplinary team, the success is attainable. As each student has to deal with various engineering scopes, students' technical skills are greatly enlarged and they acquire a multidisciplinary knowledge that was not possible in another way. Their soft skills like project management, teamwork, communication ability and personal development, which are valuable requisites for their future employers, are also improved. The participating industries also take advantage of the project: the groups competing against each other act as a multiskilled work force, actually making proposals capable of improving their products, their efficiency, and reducing costs.

This paper presents a novel analytic method to uniquely solve inverse kinematics of 7 degrees-of-freedom manipulators while avoiding joint limits and singularities. Two auxiliary parameters are introduced to deal with the self-motion manifolds: the global configuration (GC), which specifies the branch of inverse kinematics solutions; and the arm angle (ψ) that parametrizes the elbow redundancy within the specified branch. The relations between the joint angles and the arm angle are derived, in order to map the joint limits and singularities to arm angle values. Then, intervals of feasible arm angles for the specified target pose and global configuration are determined, taking joint limits and singularities into account. A simple metric is proposed to compute the elbow position according to the feasible intervals. When the arm angle is determined, the joint angles can be uniquely calculated from the position-based inverse kinematics algorithm. The presented method does not exhibit the disadvantages inherent to the use of the Jacobian matrix and can be implemented in real-time control systems. This novel algorithm is the first position-based inverse kinematics algorithm to solve both global and local manifolds, using a redundancy resolution strategy to avoid singularities and joint limits. 60 are imposed to better imitate the human arm. These limitations, however, also simplify the inverse kinematics solution since they condition the possible configurations. Ultimately, solutions developed for applications under these constraints cannot be directly extended to manipulators with wider working ranges. Also, only Kim and Rosen [21] addressed the joint limits avoidance problem, by using an optimization routine that relies on estimated weight coefficients to distance from joint limits. 65

In this paper we show how non-linear attractor dynamics can be used as a framework to control teams of autonomous mobile robots that should navigate according to a predefined geometric formation. The environment does not need to be known a priori and may change over time. Implicit to the control architecture are some important features such as establishing and moving the formation, split and join of formations (when necessary to avoid obstacles). Formations are defined by a formation matrix. By manipulating this formation matrix it is also possible to switch formations at run time. Examples of simulation results and implementations with real robots (teams of Khepera robots and medium size mobile robots), demonstrate formation switch, static and dynamic obstacle avoidance and split and join formations without the need for any explicit coordination scheme. Robustness against environmental perturbations is intrinsically achieved because the behaviour of each robot is generated as a time series of asymptotically stable states, which contribute to the asymptotic stability of the overall control system.

Dynamical systems theory is used here as a theoretical language and tool to design a distributed control archictecture that generates navigation in formation, integrated with obstacle avoidance, for a team of three autonomous robots. In this approach the level of modeling is at the level of behaviors. A "dynamics" of behavior is defined over a state'space of behavioral variables. The environment is also modeled in these terms by representing task constraints as attractors (i.e. asymptotically stable states) or reppelers (i.e. unstable states) of behavioral dynamics. For each robot attractors and repellers are combined into a vector field that governs the behavior. The resulting dynamical systems that generate the behavior of the robots are non-linear. Computer simulations support the validity of our dynamic model architectures.

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We present a distributed leader-helper architecture for teams of two autonomous mobile robots that jointly transport large payloads while avoiding collisions with obstacles (either static or dynamic). The leader navigates to the goal destination and the helper is responsible for maintaining an appropriate distance (which is a function of the object's length) to the leader. Both robots share the responsibility of ensuring that the transported object does not collide with obstructions. No path needs to be given a priori to the robots nor to the payload. The team is able to perform its transportation task in unknown environments that can have corridors, corners and may change the layout online. The payload can be of different dimensions. The team is able to cope with abrupt/strong perturbations that challenge the team behavior during the execution of the task. These characteristics make this approach suitable to be deployed in warehouses or office-like environments. The motion of each robot is controlled by a time series asymptotically stable states, which is formalized using the attractor dynamics approach to behavior based robotics. The advantages are: (i) the overt behavior is smooth and stable; (ii) because the behavior is generated as a time sequence of attractor states, for the control variables, it contributes to the overall asymptotically stability of the system that makes it robust against perturbations. We present results of experiments in simulated environments and with real robots in real environments.

d e Azurem 4800-058 Guimaraes (PORTUGAL) sergio.monteiro@dei.uminho.pt, miguel@mct.uminho.pt and estela.bicho@dei.uminho.pt Absmct-We show bow non-linear attractor dynamics can be used to implement' robot formations in unknown environments. The desimd formation geometry is given through a matrix where the parameten in each tine (its leader, desired distance and relative orientation to the leader) define the desired pose of a robot in the formation. The parameter values are then used to shape the vector fields of the dynamical systems that generate values for the contml variables (i.e. beading direction and path velocity). Then these dynamical systems are tuned such that the control variables are always very close to one of the resultant attracton. The advantage is that the systems are more robust against perturbations because the behavior is generated as a time series of asymptotically stable states.Experimental results (with three Khepera robots) demonstrate the ability of the team to create and stabilize the formation, BS well as avoiding obstacles. Flexihility is achieved in that as the senses world changes, the systems may change their planning solutions continuously but also discontinuously (tunning the formation versus split to avoid obstade). I.

In this paper we focus on the problem of assigning robots to places in a desired formation, considering random initial locations of the robots. Since we use a leader-follower strategy, we also address the task of choosing the leader to each follower. The result is a formation matrix that describes the relation between the robots and the desired formation shape. Simple algorithms are defined, that are based on the minimization of the distances of robots to places in the formation. Both these algorithms are implemented in a decentralized way. We assume that communication is possible, but the requirements are of very-low bandwidth.